ORIGINAL_ARTICLE
Optimal design of cross docking supply chain networks with time-varying uncertain demands
This paper proposes an integrated network design model for a post-distribution cross-docking strategy, comprising multi product production facilities with shared production resources, capacitated cross docks with setup cost and customer zones with time windows constraints. The model is dynamic in terms of time-varying uncertain demands, whereas uncertainty is expressed with scenario approach and contains both ‘‘wait-and-see’’ and ‘‘here-and-now’’ decisions. Inventory is just permitted in plants and over several time periods. The objective of the model is to minimize the sum of the fixed location costs for establishing cross docking centers and inventory related costs across the supply chain while ensuring that the limited service rate of cross docking centers and production facilities, and also the lead time requirements of customers are not violated. The problem is formulated as a mixed-integer linear programming problem and solved to global optimality using CPLEX. Due to the difficulty of obtaining the optimum solution in medium and large-scale problems, two heuristics that generate globally feasible, near optimal solution, Imperialistic competitive algorithm (ICA) and simulated annealing (SA), are also proposed as heuristics. We find that CPLEX is not able to solve some of the sets to optimality and turned out to run out of memory, but it performs quite well for small test sets, as compared with the two heuristics. While SA is a faster heuristic method in terms of runtime, ICA generates better results on average, but in more time.
https://www.jise.ir/article_57038_e153c6963b818db665cafd171f454224.pdf
2018-05-24
1
20
Facilities planning and design
cross-docking
mixed integer model
heuristics
Javad
Behnamian
behnamian@basu.ac.ir
1
Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
AUTHOR
Seyed Mohammad Taghi
Fatemi Ghomi
fatemi@aut.ac.ir
2
Department of Industrial Engineering,Amirkabir University of Technology,Tehran,Iran
LEAD_AUTHOR
Fariborz
Jolai
fjolai@ut.ac.ir
3
Department of Industrial Engineering,University of Tehran
AUTHOR
M.
Telgerdi
m_telgerdi@aut.ac.ir
4
Department of Industrial Engineering,Amirkabir University of Technology, Tehran, Iran
AUTHOR
Ahmadizar, F., Zeynivand, M., & Arkat, J. (2015). Two-level vehicle routing with cross-docking in a three-echelon supply chain: A genetic algorithm approach. Applied Mathematical Modelling, 39(22), 7065-7081.
1
Atashpaz-Gargari, E., Hashemzadeh, F., & Lucas, C. (2008, June). Designing MIMO PIID controller using colonial competitive algorithm: applied to distillation column process. In Evolutionary Computation, 2008. CEC 2008.(IEEE World Congress on Computational Intelligence). IEEE Congress on(pp. 1929-1934). IEEE.
2
Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In Evolutionary computation, 2007. CEC 2007. IEEE Congress on (pp. 4661-4667). IEEE.
3
Bachlaus, M., Pandey, M. K., Mahajan, C., Shankar, R., & Tiwari, M. K. (2008). Designing an integrated multi-echelon agile supply chain network: a hybrid taguchi-particle swarm optimization approach. Journal of Intelligent Manufacturing, 19(6), 747.
4
Buijs, P., Vis, I. F., & Carlo, H. J. (2014). Synchronization in cross-docking networks: A research classification and framework. European Journal of Operational Research, 239(3), 593-608.
5
Chen, P., Guo, Y., Lim, A., & Rodrigues, B. (2006). Multiple crossdocks with inventory and time windows. Computers & operations research, 33(1), 43-63.
6
Cho, N. (2009). Integrated network design models for crossdocking and warehousing strategies with tactical considerations (Doctoral dissertation, Purdue University).
7
Cóccola, M., Méndez, C. A., & Dondo, R. G. (2015). A branch-and-price approach to evaluate the role of cross-docking operations in consolidated supply chains. Computers & Chemical Engineering, 80, 15-29
8
Cota, P. M., Gimenez, B. M., Araújo, D. P., Nogueira, T. H., de Souza, M. C., & Ravetti, M. G. (2016). Time-indexed formulation and polynomial time heuristic for a multi-dock truck scheduling problem in a cross-docking centre. Computers & Industrial Engineering, 95, 135-143.
9
Davis, T. (1993). Effective supply chain management. Sloan management review, 34(4), 35.
10
Donaldson, H., Johnson, E., Ratliff, H., & Zhang, M. (1999). Schedule-driven cross-docking networks: Technical report. Georgia Institute of Technology.
11
Enderer, F., Contardo, C., & Contreras, I. (2017). Integrating dock-door assignment and vehicle routing with cross-docking. Computers & Operations Research, 88, 30-43.
12
Garey, M. R. (1979). DS Johnson Computers and intractability. A Guide to the Theory of NP-Completeness.
13
Gümüş, M., & Bookbinder, J. H. (2004). Cross‐docking and its implications in location‐distribution systems. Journal of Business Logistics, 25(2), 199-228.
14
Jayaraman, V., & Ross, A. (2003). A simulated annealing methodology to distribution network design and management. European Journal of Operational Research, 144(3), 629-645.
15
Ladier, A. L., & Alpan, G. (2016). Robust cross-dock scheduling with time windows. Computers & Industrial Engineering, 99, 16-28.
16
Lim, A., Miao, Z., Rodrigues, B., & Xu, Z. (2005). Transshipment through crossdocks with inventory and time windows. Naval Research Logistics (NRL), 52(8), 724-733.
17
Longinidis, P., & Georgiadis, M. C. (2011). Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty. International journal of production economics, 129(2), 262-276.
18
Ma, H., Miao, Z., Lim, A., & Rodrigues, B. (2011). Crossdocking distribution networks with setup cost and time window constraint. Omega, 39(1), 64-72.
19
Melo, M. T., Nickel, S., & Da Gama, F. S. (2006). Dynamic multi-commodity capacitated facility location: a mathematical modeling framework for strategic supply chain planning. Computers & Operations Research, 33(1), 181-208.
20
Miao, Z., Fu, K., Fei, Q., & Wang, F. (2008, June). Meta-heuristic algorithm for the transshipment problem with fixed transportation schedules. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 601-610). Springer, Berlin, Heidelberg.
21
Mousavi, S. M., Tavakkoli-Moghaddam, R., & Siadat, A. (2013). Optimal design of the cross-docking in distribution networks: Heuristic solution approach. International Journal of Engineering-Transactions A: Basics, 27(4), 533.
22
Musa, R., Arnaout, J. P., & Jung, H. (2010). Ant colony optimization algorithm to solve for the transportation problem of cross-docking network. Computers & Industrial Engineering, 59(1), 85-92.
23
Nikolopoulou, A. I., Repoussis, P. P., Tarantilis, C. D., & Zachariadis, E. E. (2017). Moving products between location pairs: Cross-docking versus direct-shipping. European Journal of Operational Research, 256(3), 803-819.
24
Papadimitriou, C. H., & Steiglitz, K. (1998). Combinatorial optimization: algorithms and complexity. Courier Corporation
25
Reeves, C. R. (1995). Modern heuristic techniques for combinatorial problems. Advanced topics in computer science. Modern Heuristic Techniques for Combinatorial Problems: Advanced Topics in Computer Science.
26
Ross, A., & Jayaraman, V. (2008). An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design. Computers & Industrial Engineering, 55(1), 64-79
27
Seyedhoseini, S. M., Rashid, R., & Teimoury, E. (2015). Developing a cross-docking network design model under uncertain environment. Journal of Industrial Engineering International, 11(2), 225-236.
28
Shapiro, J. F. (1999). On the connections among activity-based costing, mathematical programming models for analyzing strategic decisions, and the resource-based view of the firm. European Journal of Operational Research, 118(2), 295-314.
29
Shi, W., Liu, Z., Shang, J., & Cui, Y. (2013). Multi-criteria robust design of a JIT-based cross-docking distribution center for an auto parts supply chain. European Journal of Operational Research, 229(3), 695-706.
30
Shokrollahpour, E., Zandieh, M., & Dorri, B. (2011). A novel imperialist competitive algorithm for bi-criteria scheduling of the assembly flowshop problem. International Journal of Production Research, 49(11), 3087-3103.
31
Sung, C. S., & Yang, W. (2008). An exact algorithm for a cross-docking supply chain network design problem. Journal of the Operational Research Society, 59(1), 119-136
32
Sung, C. S., & Song, S. H. (2003). Integrated service network design for a cross-docking supply chain network. Journal of the Operational Research Society, 54(12), 1283-1295.
33
Tang, S. (2007). The impacts of cross-docking operation on supply chain management (Doctoral dissertation, The Hong Kong Polytechnic University).
34
Tsiakis, P., Shah, N., & Pantelides, C. C. (2001). Design of multi-echelon supply chain networks under demand uncertainty. Industrial & Engineering Chemistry Research, 40(16), 3585-3604.
35
Van Belle, J., Valckenaers, P., & Cattrysse, D. (2012). Cross-docking: State of the art. Omega, 40(6), 827-846.
36
Yan, H., & Tang, S. L. (2009). Pre-distribution and post-distribution cross-docking operations. Transportation Research Part E: Logistics and Transportation Review, 45(6), 843-859.
37
Yu, V. F., Jewpanya, P., & Kachitvichyanukul, V. (2016). Particle swarm optimization for the multi-period cross-docking distribution problem with time windows. International Journal of Production Research, 54(2), 509-525.
38
ORIGINAL_ARTICLE
A New Formulation for Cost-Sensitive Two Group Support Vector Machine with Multiple Error Rate
Support vector machine (SVM) is a popular classification technique which classifies data using a max-margin separator hyperplane. The normal vector and bias of the mentioned hyperplane is determined by solving a quadratic model implies that SVM training confronts by an optimization problem. Among of the extensions of SVM, cost-sensitive scheme refers to a model with multiple costs which considers different error rates for misclassification. The cost-sensitive scheme is useful when misclassifications cannot be considered equal. For example, it is true for medical diagnosis. In such cases, misclassifying a patient as healthy implies more loss in comparison to the opposite loss. Therefore, cost-sensitive scheme poses as a modified model and hereby aims at minimizing loss function instead of generalization error. This paper, concentrates on a new formulation cost-sensitive classification considering both misclassification cost and accuracy measures. Also, in the training phase a new heuristic algorithm will be used to solve the proposed model. The superiority of the novel method is affirmed after comparing to the traditional ones.
https://www.jise.ir/article_59552_f44503642f2159cf052a6ed3d35f93d4.pdf
2018-08-01
21
30
Cost-sensitive Learning
Classification
Support Vector Machine
Supervised Learning
Amir Abbas
Najafi
aanajafi@kntu.ac.ir
1
Faculty of Industrial Engineering, K.N.Toosi University of Technology
LEAD_AUTHOR
Ali
Nedaie
alinedaie@kntu.ac.ir
2
Faculty of Industrial Engineering, K.N.Toosi University of Technology
AUTHOR
Cao, P., Zhao, D., & Zaiane, O. (2013, April). An optimized cost-sensitive SVM for imbalanced data learning. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 280-292). Springer Berlin Heidelberg.
1
Chen, X. L., Jiang, Y., Chen, M. J., Yu, Y., Nie, H. P., & Li, M. (2012). A Dynamic Cost Sensitive Support Vector Machine. In Advanced Materials Research (Vol. 424, pp. 1342-1346). Trans Tech Publications.
2
Chen, Y., & Wang, J. Z. (2003). Support vector learning for fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems, 11(6), 716-728.
3
Crammer, K., & Singer, Y. (2001). On the algorithmic implementation of multiclass kernel-based vector machines. Journal of machine learning research, 2(Dec), 265-292.
4
Fung, G. M., & Mangasarian, O. L. (2005). Multicategory proximal support vector machine classifiers. Machine learning, 59(1-2), 77-97.
5
Hwang, J. P., Park, S., & Kim, E. (2011). A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function. Expert Systems with Applications, 38(7), 8580-8585.
6
Mangasarian, O. L., & Musicant, D. R. (2001). Lagrangian support vector machines. Journal of Machine Learning Research, 1(Mar), 161-177.
7
Mangasarian, O. L., & Wild, E. W. (2001). Proximal support vector machine classifiers. In Proceedings KDD-2001: Knowledge Discovery and Data Mining.
8
Nedaie, A., & Najafi, A. A. (2016). Polar support vector machine: Single and multiple outputs. Neurocomputing, 171, 118-126.
9
Platt, J. C., Cristianini, N., & Shawe-Taylor, J. (1999, November). Large Margin DAGs for Multiclass Classification. In nips (Vol. 12, pp. 547-553).
10
Pontil, M., & Verri, A. (1998). Support vector machines for 3D object recognition. IEEE transactions on pattern analysis and machine intelligence, 20(6), 637-646.
11
Qi, Z., Tian, Y., & Shi, Y. (2012). Laplacian twin support vector machine for semi-supervised classification. Neural Networks, 35, 46-53.
12
Qi, Z., Tian, Y., & Shi, Y. (2013). Robust twin support vector machine for pattern classification. Pattern Recognition, 46(1), 305-316.
13
Shawe-Taylor, J., & Sun, S. (2011). A review of optimization methodologies in support vector machines. Neurocomputing, 74(17), 3609-3618.
14
Suykens, J. A., De Brabanter, J., Lukas, L., & Vandewalle, J. (2002). Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48(1), 85-105.
15
Tian, Y., Qi, Z., Ju, X., Shi, Y., & Liu, X. (2014). Nonparallel support vector machines for pattern classification. IEEE transactions on cybernetics, 44(7), 1067-1079.
16
Tran, Q. A., Li, X., & Duan, H. (2005). Efficient performance estimate for one-class support vector machine. Pattern Recognition Letters, 26(8), 1174-1182.
17
Turney, P. D. (1995). Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of artificial intelligence research, 2, 369-409.
18
Vapnik, V. (1998). Statistical Learning Theory, New York, Wiley.
19
Wan, J. W., Yang, M., & Chen, Y. J. (2012). Cost sensitive semi-supervised Laplacian support vector machine. Acta Electronica Sinica, 40(7), 1410-1415.
20
Waring, C. A., & Liu, X. (2005). Face detection using spectral histograms and SVMs. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 35(3), 467-476.
21
Yang, C. Y., Wang, J. J., Chou, J. J., & Lian, F. L. (2015). Confirming robustness of fuzzy support vector machine via ξ–α bound. Neurocomputing, 162, 256-266.
22
Zheng, E. H., Li, P., & Song, Z. H. (2006). Cost sensitive support vector machines. Control and decision, 21(4), 473.
23
ORIGINAL_ARTICLE
The effects of misclassification errors on multiple deferred state attribute sampling plan
Multiple deferred state (MDS) sampling plan by attribute in which current lot and future lots information is utilised on sentencing submitted lot, is constructed under the assumption of perfect inspection. But sometimes the inspection may not be free of inspection errors. In this paper, we develop MDS-plan by attribute to the state where misclassification errors exist during the inspection. In the following, we consider effects of the inspection errors on operating characteristic curve, expected disposition time and average sample number (ASN) for decision in MDS- plan. In order to discuss influence of the inspection errors on these mentioned measures, we have more focus on a specific feature of MDS(0,1,2)-plan. Also, some applicable examples are given to make more understanding. The results show that accuracy and performance of MDS(0,1,2)-plan can be affected by the inspection errors. Also we show that the inspection errors not only cause the considerable difference between true and observed curves of the expected disposition time in MDS(0,1,2)-plan but also have a negative influence on the ASN curve of the mentioned plan.
https://www.jise.ir/article_59609_afe6782afe2d7abb93bd06d3004cbeb9.pdf
2018-08-02
31
46
Multiple deferred state sampling plan
inspection errors
operating characteristic curve
average sample number
Robab
Afshari
robab.afshari@mail.um.ac.ir
1
Ferdowsi University of Mashhad
AUTHOR
Bahram
Sadeghpour Gildeh
sadeghpour@um.ac.ir
2
Ferdowsi University of Mashhad
LEAD_AUTHOR
Afshari, R., SadeghpourGildeh, B. &Sarmad, M. (2018). Multiple deferred state sampling plan with fuzzy parameter.International Journal of Fuzzy Systems, 20(2), 549-557.
1
2
Afshari, R. & Sadeghpour Gildeh, B. (2017). Fuzzy multiple deferred state variable sampling plan,Journal of Intelligent and Fuzzy Systems, Accepted,DOI: 10.3233/JIFS-17907.
3
Balamurali, S., Jeyadurga, P. & Usha, M. (2016). Designing of bayesian multiple deferred state sampling plan based on Gamma Poisson distribution. American Journal of Mathematical and Management Sciences, 35(1), 77-90.
4
Balamurali, S. & Jun, C. H. (2007). Multiple dependent state sampling plans for lot acceptance based on measurement data. European Journal of Operational Research, 180(3), 1221-1230.
5
Beaing, I. & Case, K. E. (1981). A wide variety of AOQ and ATI performance measures with and without inspection error. Journal of Quality Technology, 13(1), 1-9.
6
Case, K. E., Benett, G. K. & Schmidt, J. W. (1975). The effect on inspection error on average outgoing quality.Journal of Quality Technology, 7(1), 28-33.
7
Chen, C. H. & Chou, C. Y. (2003). Economic specification limits under the inspection error.Journal of the Chinese Institute of Industrial Engineers, 20(1), 9-13.
8
Chen, T. T., Huang, C. P. & Lien, S. Y. (2008). Dodge-Roming rectifying single sampling plans based on inspection error. In: Proceedings of International Conference on Business and Information Management, Linkou, Taipei County, Taiwan.
9
Collins, R. D., Case, K. E. & Bennett, G. K. (1973). The effect of inspection errors in single sampling inspection plans.International Journal of Production Research, 11(3), 289-298.
10
Dodge, H. F. (1955). Chain sampling inspection plan.Industerial quality control, 11(4), 10-13.
11
Dodge, H. F. & Stephens, K. S. (1966). Some new chain sampling inspection plans.Industerial quality control, 23(2), 61-67.
12
Dorris, A. L. & Foote, B. L. (1978). Inspection errors and statistical quality control.AIIE Transactions, 10(2), 184-192.
13
Duffuaa S. O. & El-Gaaly A. (2015). Impact of inspection errors on the formulation of a multi-objective optimization process targeting model under inspection sampling plan.Computers and Industrial Engineering, 80, 254-260.
14
Fallahnezhad M. S. & Yousefi Babadi A. (2015). A new acceptance sampling plan using bayesian approach in the presence of inspection errors.Transactions of the Institute of Measurement and Control, 37(9), 1060-1073.
15
Jun C. H., Aslam M., Azam M., Balamurali S. & Rao G. S. (2014). Mixed multiple dependent state sampling plans based on process capability index.Journal of Testing and Evaluation, 43(1), 171-178.
16
Latha M. &Subbiah K. (2015). Selection of bayesian multiple deferred state (BMDS-1) sampling plan based on quality regions.International Journal of Recent Scientific Research, 6(5), 3864-3867.
17
Markowski, E. P.& Markowski, C. A. (2002). Improved attribute acceptance sampling plans in the presence of misclassification error.European Journal of Operational Research, 139(3), 501-510.
18
Mogg, J. M. & Wortham, A. W. (1970). Dependent stage sampling inspection.International Journal of Production Research, 8(4), 385-395.
19
Montgomery, D. C. (1991). Introduction to statistical quality control. New York: Wiley.
20
Osanaiye, P. A. & Alebiosu, S. A. (1988). Effects of industrial inspection errors on some plans that utilise the surrounding lot information.Journal of Applied Statistics, 15(3), 295-304.
21
Senthilkumar D., Ramya S. R. & Raffie B. E. (2015). Construction and selection of repetitive deferred variables sampling (RDVS) plan indexed by quality levels.Journal of Academia and Industrial Research, 3(10), 497.
22
Soundararajan, V. & Vijayaraghavan, R. (1990). Construction and selection of multiple dependent (deferred) state sampling plan.Journal of Applied Statistics, 17(3), 397-409.
23
Subramain, K. & Haridoss, V. (2012). Development of multiple deferred state sampling plan based on minimum risks using the weighted poisson distribution for given acceptance quality level and limiting quality level.International Journal of Quality Engineering and Technology, 3(2), 168-180.
24
Suich, R. (1990). The effects of inspection errors on acceptance sampling for nonconformities.Journal of Quality Technology, 22(4), 314-318.
25
Wortham, A. W.& Baker, R. C. (1976). Multiple deferred state sampling inspection.International Journal of Production Research, 14(6), 719-731.
26
Wu C. W., Lee A. & Chen Y. (2016). A novel lot sentencing method by variables inspection considering multiple dependent state.Quality and Reliability Engineering International, 32(3), 985-994.
27
Wu, C. W., Liu, S. W. & Lee, A. (2015). Design and construction of a variable multiple dependent state sampling plan based on process yield.European Journal Industrial engineering, 9(6), 819-838.
28
Yan A., Liu S. & Dong X. (2016). Designing a multiple dependent state sampling plan based on the coefficient of variation.SpringerPlus, 5(1), 1447.
29
ORIGINAL_ARTICLE
An Optimal Preventive Maintenance Model to Enhance Availability and Reliability of Flexible Manufacturing Systems
General preventive maintenance model for the components of a system, which improves the reliability to ‘as good as new,’ was used to optimize the maintenance cost. The cost function of a maintenance policy was minimized under given availability constraint. On the other hand, in order to ensure appropriate reliability and availability, the development of the optimal maintenance policy is the one of the main issues in system to perform preventive maintenance (PM) in equipment. In this paper, maintenance characteristics of a typical flexible manufacturing system (FMS) have been determined. These characteristics can be used to understand and prevent the complex reality of failures and repairs. Also, an optimal model for the preventive maintenance management of a FMS has been presented based on preview literature in order to enhance availability and reliability of this system and to reduce the cost of maintenance tasks. Finally, proposed framework has been applied for a robot paint sprayer and its results shown in a form of the preventive maintenance plan, distribution fitting and Reliabilities’ parameters for each component s of robot paint sprayer, and the maintenance scheduling timetable.
https://www.jise.ir/article_54749_b68c3aa12d86cf85c55b47cfaff8583d.pdf
2018-08-23
47
61
Maintenance management
preventive maintenance
Flexible manufacturing systems
Availability
Reliability
Maintenance scheduling
Bakhtiar
Ostadi
bostadi@modares.ac.ir
1
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
AKS Jardine (1973). Maintenance replacement and reliability. Pitman Publishing, Toronto, Ontario.
1
Benjamin Lhorente, Diederik Lugtigheid, Peter F. Knights & Alejandro Santana (2004). "A model for optimal armature maintenance in electric haul truck wheel motors: a case study". Reliability Engineering & System Safety, 84(2), 209-218.
2
Bris, R., Chatelet, E., Yalaoui, F., 2003, New method to minimize the preventive maintenance cost of series –parallel systems. Reliability Engineering and System Safety, 82, 247–255.
3
Cho, D.I., Parlar, M., 1991, A survey of maintenance models for multi-unit systems. European Journal of Operational Research, 51, 1-23.
4
Duarte, J.A.C., Craveiro, J.C.T.A., Trigo, T.P., 2006, Optimization of the preventive maintenance plan of a series components system. International Journal of Pressure Vessels and Piping, 83, 244–248.
5
Fotsch, R.J., 1985, Machine tool justification policies: Their effect on productivity and profitability. Journal of Manufacturing Systems, 3 (2), 169-195.
6
Gits, C.W., 1986, On the maintenance concept for a technical system; II. Literature review. Maintenance Management International, 6 (3), 181-196.
7
Hammer, H., 1987, Availability, performance and service of FMS. The FMS Magazine, 5 (4), 180-186.
8
Koomsap, P., Shaikh, N.I., V. V. Prabhu, V.V., 2005, Integrated process control and condition-based maintenance scheduler for distributed manufacturing control systems. International Journal of Production Research, 43 (8), 1625 – 1641.
9
Lie, C.H., Hwang, C.L., Tillman, F.A., 1977, Availability of maintained systems: A state-of-the-art survey. AIIE Transactions, 9 (7), 247-259.
10
Luxhøj, J.T, Jens, J.O., Thorsteinsson, U., 1997, Trends and perspectives in industrial maintenance management, Journal of Manufacturing Systems, 16 (6), 437-453.
11
McCall, J.J., 1965, Maintenance policies for stochastically failing equipment: A survey. Management Science, 11, 493- 525.
12
Meredith, J.R., 1988, Installation of flexible manufacturing systems teaches management lessons in integration, labor, costs, and benefits. Industrial Engineering, 20 (4), 17-27.
13
Michael Vineyard, Kwasi Amoako-Gyampah & Jack R Meredith (1999). "Failure rate distributions for flexible manufacturing systems: An empirical study". European Journal of Operational Research, 116(1), 139-155.
14
Nada, O.A., EIMaraghy, H.A., EIMaraghy, W.H., 2006, Quality Prediction in Manufacturing System Design, Journal of Manufacturing Systems, 25 (3), 153-171.
15
Pintelon, L.M.A., Van Puyvelde, F.L.B., Gelders, L.F., 1995, An age-based replacement policy with non-zero repair times for a continuous production process. International Journal of Production Research, 33 (8), 2111-2123.
16
Sherif, Y.S., Smith, M.L., 1981, Optimal maintenance models or systems subject to failure: A review. Naval Research Logistics Quarterly, 28, 47-74.
17
Tsai, Y., Wang, K., Tsai, L., 2004, A study of availability-centered preventive maintenance for multi-component systems. Reliability Engineering and System Safety, 84, 261-270.
18
Yang, Z., Djurdjanovic, D. and Ni, J., 2007, Maintenance Scheduling for a Manufacturing System of Machines with Adjustable Throughput, IIE Transactions on Quality and Reliability Engineering, 39 (12), 1111-1125.
19
Zhou, J., Djurdjanovic, D., Simmons-Ivy, J. and Ni, J. 2007, Integration of Maintenance and Reconfiguration Operations for Cost-Effective Maintenance in Reconfigurable Manufacturing Systems, IIE Transactions on Quality and Reliability Engineering, 39 (12), 1085-1102.
20
ORIGINAL_ARTICLE
A multi-objective Two-Echelon Capacitated Vehicle Routing Problem for perishable products
This article addresses a general tri-objective two-echelon capacitated vehicle routing problem (2E-CVRP) to minimize the total travel cost, customers waiting times and carbon dioxide emissions simultaneously in distributing perishable products. In distributing perishable products, customers’ satisfaction is very important and is inversely proportional to the customers waiting times. The proposed model is a mixed integer non-linear programming (MINLP). By applying some linearization methods, the MINLP model exchanged to a mixed integer linear programming (MILP). This paper uses a non-dominated sorting genetic (NSGA-II) algorithm to solve the presented mathematical model. The related results would be compared with Lp-metric results in small-sized test problems and with multi objective particle swarm optimization (MOPSO) algorithm in medium and large sized test problems. In order to evaluate the quality of the solution sets, the results of two metaheuristic algorithms are compared based on four comparison metrics in medium sized problems. The obtained results indicate the efficiency of the NSGA-II algorithm.
https://www.jise.ir/article_54750_758d3f7dd777f21502e080f774cf671a.pdf
2018-10-07
62
84
2E-CVRP
carbon dioxide emissions
perishable products
customers waiting times
linearization
Multi objective Optimization
Rashed
Sahraeian
sahraeian@shahed.ac.ir
1
Industrial Engineering Department, College of Engineering, Shahed University, Tehran, Iran
LEAD_AUTHOR
Mehraneh
Esmaeili
m_esmaili@shahed.ac.ir
2
Industrial Engineering Department, College of Engineering, Shahed University, Tehran, Iran
AUTHOR
Angel-Bello, F., Martínez-Salazar, I., & Alvarez, A. (2013). Minimizing waiting times in a route design problem with multiple use of a single vehicle. Electronic Notes in Discrete Mathematics, 41, 269-276.
1
Afshar-Nadjafi, B., & Razmi-Farooji, A. (2014). A Comparison of NSGA II and MOSA for Solving Multi-depots Time-dependent Vehicle Routing Problem with Heterogeneous Fleet. Journal of Optimization in Industrial Engineering, 7(16), 65-73.
2
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29
ORIGINAL_ARTICLE
Self-Starting Control Chart and Post Signal Diagnostics for Monitoring Project Earned Value Management Indices
Earned value management (EVM) is a well-known approach in a project control system which uses some indices to track schedule and cost performance of a project. In this paper, a new statistical framework based on self-starting monitoring and change point estimation is proposed to monitor correlated EVM indices which are usually auto-correlated over time and non-normally distributed. Also, a new change point estimator is developed to find the real time of change in the indices mean. Furthermore, a new diagnosing method is presented to recognize the deviated mean index. The performance of the proposed methods is evaluated through simulation studies and an illustrative example.
https://www.jise.ir/article_54916_429a50b2fedc3f17b6e3ac07bceca1e7.pdf
2018-10-08
85
100
Correlated EVM indices
self-starting monitoring
auto-correlated non-normal indices
Change point
diagnosing method
projects
Fatemeh
Sogandi
f.sogandi1990@gmail.com
1
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
AUTHOR
S.Meysam
Mousavi
mousavi.sme@gmail.com
2
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
LEAD_AUTHOR
Amirhossein
Amiri
amirhossein.amiri@gmail.com
3
Department of Industrial Engineering, Faculty of Engineering, Shahed University, Tehran, Iran
AUTHOR
Acebes F, Pajares J, Galán JM, and López-Paredes A (2013) Beyond earned value management: A graphical framework for integrated cost, schedule and risk monitoring. Procedia-Social and Behavioral Sciences 74(1):181-189.
1
Acebes F, Pajares J, Galán JM, and López-Paredes A (2014) A new approach for project control under uncertainty. Going back to the basics. International Journal of Project Management 32(3):423-34.
2
Acebes F, Pereda M, Poza D, Pajares J and Galán, JM (2015). Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques. International Journal of Project Management 33(7):1597-1609.
3
Acebes, F, Pajares, J., Galán, JM, and López-Paredes, A (2016) A project monitoring and control system using EVM and montecarlo simulation. Project Management and Engineering Research 4(1): 31-40.
4
Ahmad L, Aslam M, and Jun CH (2014) Designing of X-bar control charts based on process capability index using repetitive sampling. Transactions of the Institute of Measurement and Control 36(3):367-374.
5
Aliverdi R, MoslemiNaeni L, and Salehipour A (2013) Monitoring project duration and cost in a construction project by applying statistical quality control charts. International Journal of Project Management 31(3):411-423.
6
Anbari FT (2003) Earned value project management method and extensions. International Journal of Project Management 34(4):12–23.
7
Aslam M, Azam M, and Jun CH (2015) A new control chart for exponential distributed life using EWMA. Transactions of the Institute of Measurement and Control 35(2):205-219.
8
Băncescu M (2016) Controlling Project Schedule Progress, Using Control Charts. Cybernetics and Systems 47(7): 602-615.
9
Bauch GT, and Chung CA (2001) A statistical project control tool for engineering managers. Project Management Journal 32(3):37–44.
10
Burke R (2003) Project management planning and control techniques 4thed.
11
Chen HL (2014) Improving forecasting accuracy of project earned value metrics: linear modeling approach. Journal of Management in Engineering 30(2):135-145.
12
Cioffi DF (2006) Designing project management: a scientific notation and an improved formalism for earned value calculations. International Journal of Project Management 24(2):136–144.
13
Colin J and Vanhoucke M (2014) Setting tolerance limits for statistical project control using earned value management. Omega 49(1):107-122.
14
Colin J and Vanhoucke M (2015) Developing a framework for statistical process control approaches in project management. International Journal of Project Management33(6): 1289-1300.
15
Fleming QW and Coppelman JM (2005) Earned value project management 3th ed. Project Management Institute.
16
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17
Leu SS and Lin YC (2008) Project performance evaluation based on statistical process control techniques. Journal of Construction Engineering and Management-ASCE 134(10):813–819.
18
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19
Lipke K (2003) Earned schedule: a breakthrough extension to earned value theory? A retrospective analysis of real project data. The Measurable News 1(2):13–23.
20
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21
Lipke W (2002) A study of the normality of earned value management indicators. The Measurable News 4(1):1-16.
22
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23
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24
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25
Lombard P. (2007) Project scheduling and cost control: Planning, monitoring and controlling the baseline. Project Management Journal 39(2):115-115.
26
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27
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28
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29
MoslemiNaeni L, Shadrokh SH, and Salehipour A (2011) A fuzzy approach for the earned value management. International Journal of Project Management 29(6):764–772.
30
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31
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36
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37
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38
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39
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40
ORIGINAL_ARTICLE
Equitable multi objective model for public facility location using RLTP technique
In the present research, a multi-objective model is proposed, which considers equity among the citizens in addition to the cost criterion. Then, the model will be solved using Reservation Level Tchebycheff Procedure (RLTP), which is one of the interactive multi-objective decision-making techniques. Subsequently, the obtained results will be compared with those of the single-objective models to determine the effect of considering and not considering the equity criterion on public facilities location. Results of the present study show that the basic models of public facilities location do not consider the equity criterion; thus, in order to protect citizens’ rights, it is necessary for decision-makers of the urban management and planning to consider the objective of equity, along with other objectives of the project, as a multi-objective model in public facilities location problems. The proposed multi-objective model has also desirable and acceptable performance, which can be used in the public facilities location problems.
https://www.jise.ir/article_54917_4b944191feb0494d2e0f38061209dc37.pdf
2018-10-09
101
113
Citizenship equity
urban management and planning
public facilities location
reservation level Tchebycheff procedure (RLTP)
Ali
Bozorgi-Amiri
alibozorgi@ut.ac.ir
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Ariyan
Hosseinzadeh
hosseinzadeh.a@ut.ac.ir
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Barbati, M., Bruno, G., & Marín, A. (2015). Balancing the arrival times of users in a two-stage location problem. Annals of Operations Research, 1-16.
1
Barbati, M., & Piccolo, C. (2015). Equality measures properties for location problems. Optimization Letters, 1-18.
2
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3
Batta, R., Lejeune, M., & Prasad, S. (2014). Public facility location using dispersion, population, and equity criteria. European Journal of Operational Research, 234(3), 819-829.
4
Bell, J. E., Griffis, S. E., Cunningham, W. A., & Eberlan, J. A. (2011). Location optimization of strategic alert sites for homeland defense. Omega,39(2), 151-158.
5
Berman, O., Drezner, Z., Tamir, A., & Wesolowsky, G. O. (2009). Optimal location with equitable loads. Annals of operations research, 167(1), 307-325.
6
Berman, O., & Kaplan, E. H. (1990). Equity maximizing facility location schemes. Transportation Science, 24(2), 137-144.
7
Boffey, T. B., Mesa, J. A., Ortega, F. A., & Rodrigues, J. I. (2008). Locating a low-level waste disposal site. Computers & Operations Rearches, 35(3), 701-716.
8
Caballero, R., González, M., Guerrero, F. M., Molina, J., & Paralera, C. (2007). Solving a multiobjective location routing problem with a metaheuristic based on tabu search. Application to a real case in Andalusia. European Journal of Operational Research, 177(3), 1751-1763.
9
Caramia, M., & Mari, R. (2015). A decomposition approach to solve a bilevel capacitated facility location problem with equity constraints. Optimization Letters, 1-23.
10
Chanta, S., Mayorga, M. E., & McLay, L. A. (2011). Improving emergency service in rural areas: a bi-objective covering location model for EMS systems. Annals of Operations Research, 221(1), 133-159.
11
Chanta, S., Mayorga, M. E., & McLay, L. A. (2014). The minimum p-envy location problem with requirement on minimum survival rate. Computers & Industrial Engineering, 74, 228-239.
12
Coulter, P.B. (1980), "Measuring the inequity of urban publicservices: A methodological discussion with applications", Policy Studies Journal 8, 683-698.
13
Erkut, E. (1992), "Inequality measures for location problems", University of Alberta Research Report No. 91-2, September 1992.
14
Galvão, R. D., Espejo, L. G. A., Boffey, B., & Yates, D. (2006). Load balancing and capacity constraints in a hierarchical location model.European Journal of Operational Research, 172(2), 631-646.
15
Gini, C. (1912), Variabilita e Mutabilita, Bologna. Gopalan, R., Batta, R., and Karwan, M.H. (1990), "The equity constrained shortest path problem", Computers & Operations Research, 17/3, 297-307.
16
Gopalan, R., Kolluri, K.S., Batta, R., and Karwan, M.H. (1990), "Modeling equity in the transportation of hazardous materials", Operations Research, 38, 961-973.
17
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18
Jia, H., Ordóñez, F., & Dessouky, M. (2007). A modeling framework for facility location of medical services for large-scale emergencies. IIE transactions, 39(1), 41-55.
19
Johnson, M. P. (2003). Single-period location models for subsidized housing: Tenant-based subsidies. Annals of Operations Research, 123(1-4), 105-124.
20
Kalcsics, J., Nickel, S., Puerto, J., & Rodríguez‐Chía, A. M. (2015). Several 2‐facility location problems on networks with equity objectives. Networks,65(1), 1-9.
21
Karsu, Ö., & Morton, A. (2015). Inequity averse optimization in operational research. European Journal of Operational Research, 245(2), 343-359.
22
Khodaparasti, S., Maleki, H. R., Bruni, M. E., Jahedi, S., Beraldi, P., & Conforti, D. (2015). Balancing efficiency and equity in location-allocation models with an application to strategic EMS design. Optimization Letters, 1-18.
23
Khodaparasti, S., Maleki, H. R., Jahedi, S., Bruni, M. E., & Beraldi, P. (2016). Enhancing community based health programs in Iran: a multi-objective location-allocation model. Health care management science, 1-15.
24
Lejeune, M. A., & Prasad, S. Y. (2013). Effectiveness–equity models for facility location problems on tree networks. Networks, 62(4), 243-254.
25
López‐de‐los‐Mozos, M. C., Puerto, J., & Rodríguez‐Chía, A. M. (2013). Robust mean absolute deviation problems on networks with linear vertex weights. Networks, 61(1), 76-85.
26
Maliszewski, P. J., Kuby, M. J., & Horner, M. W. (2012). A comparison of multi-objective spatial dispersion models for managing critical assets in urban areas. Computers, Environment and Urban Systems, 36(4), 331-341.
27
Marsh, M. T., & Schilling, D. A. (1994). Equity measurement in facility location analysis: A review and framework. European Journal of Operational Research,74(1), 1-17.
28
McAllister, D. M. (1976). Equity and efficiency in public facility location.Geographical Analysis, 8(1), 47-63.
29
Melachrinoudis, E., & Xanthopulos, Z. (2003). Semi-obnoxious single facility location in Euclidean space. Computers & Operations Research, 30(14), 2191-2209.
30
Mestre, A. M., Oliveira, M. D., & Barbosa-Póvoa, A. (2012). Organizing hospitals into networks: a hierarchical and multiservice model to define location, supply and referrals in planned hospital systems. OR spectrum,34(2), 319-348.
31
Mladenović, N., Labbé, M., & Hansen, P. (2003). Solving the p‐Center problem with Tabu Search and Variable Neighborhood Search. Networks,42(1), 48-64.
32
Morrill, R.L., and Symons, J. (1977), "Efficiency and equity aspects of optimum location", Geographical Analysis 9, 216-225.
33
Mulligan, G.F. (1991), "Equality measures and facility location", Regional Science 7/4, 548-56l.
34
Mumphrey, A. J., Seley, J. E., & Wolpert, J. (1971). A decision model for locating controversial facilities. Journal of the American Institute of Planners,37(6), 397-402.
35
Ogryczak, W. (2009). Inequality measures and equitable locations. Annals of Operations Research, 167(1), 61-86.
36
Ohsawa, Y., & Tamura, K. (2003). Efficient location for a semi-obnoxious facility. Annals of Operations Research, 123(1-4), 173-188.
37
Ohsawa, Y., Ozaki, N., & Plastria, F. (2008). Equity-efficiency bicriteria location with squared Euclidean distances. Operations research, 56(1), 79-87.
38
Rawls, J. (1971). A Theory of Justice (Cambridge. Mass.: Harvard University.
39
Romero, N., Nozick, L. K., & Xu, N. (2016). Hazmat facility location and routing analysis with explicit consideration of equity using the Gini coefficient. Transportation Research Part E: Logistics and Transportation Review, 89, 165-181.
40
Savas, E. S. (1978). On equity in providing public services. Management Science, 24(8), 800-808.
41
Steuer, R. E., & Choo, E. U. (1983). An interactive weighted Tchebycheff procedure for multiple objective programming. Mathematical programming,26(3), 326-344.
42
Suzuki, A., & Drezner, Z. (2009). The minimum equitable radius location problem with continuous demand. European Journal of Operational Research, 195(1), 17-30.
43
ORIGINAL_ARTICLE
Simultaneous reduction of emissions (CO2 and CO) and optimization of production routing problem in a closed-loop supply chain
Environmental pollution and emissions, along with the increasing production and distribution of goods, have placed the future of humanity at stake. Today, measures such as the extensive reduction in emissions, especially of CO2 and CO, have been emphasized by most researchers as a solution to the problem of environmental protection. This paper sought to explore production routing problem in closed-loop supply chains in order to find a solution to reduce CO2 and CO emissions using the robust optimization technique in the process of product distribution. The uncertainty in some parameters, such as real-world demand, along with heterogeneous goals, compelled us to develop a fuzzy robust multi-objective model. Given the high complexity of the problem, metaheuristic methods were proposed for solving the model. To this end, the bee optimization method was developed. Some typical problems were solved to evaluate the solutions. In addition, in order to prove the algorithm’s efficiency, the results were compared with those of the genetic algorithm in terms of quality, dispersion, uniformity, and runtime. The dispersion index values showed that the bee colony algorithm produces more workable solutions for the exploration and extraction of the feasible region. The uniformity index values and the runtime results also indicated that the genetic algorithm provides shorter runtimes and searches the solution space in a more uniform manner, as compared with the bee colony algorithm.
https://www.jise.ir/article_57039_02d92875d80f2d592d5f3f5c358be595.pdf
2018-04-09
114
133
emissions
production routing
Closed-loop supply chain
robust optimization
Yasser
Emamian
yasser.emamian@gmail.com
1
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
LEAD_AUTHOR
Isa
Nakhai
nakhai.isa@gmail.com
2
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
AUTHOR
Alireza
Eydi
alireza.eydi@uok.ac.ir
3
Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran
AUTHOR
Adulyasak, Y., Cordeau, J.-F. & Jans, R. (2014). Optimization-based adaptive large neighborhood search for the production routing problem. Transportation Science, 48(1), 20-45.
1
Adulyasak, Y., Cordeau, J.-F. & Jans, R. (2015). The production routing problem: A review of formulations and solution algorithms. Computers & Operations Research, 55, 141-152.
2
Bektas, T. & Laporte, G. (2011). The Pollution-Routing Problem. Transportation Research Part B: Methodological, 45(8), 1232-1250.
3
Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
4
Jimenez, M., Arenas, M., Bilbao, A. & Rodr, M. V. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609.
5
Kannan, G., Sasikumar, P. & Devika, K. (2010). A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling. Applied Mathematical Modelling, 34(3), 655-670.
6
Naber, S. K., Deree, D. A., Spliet, R. & Van-Den-Heuvel, W. (2015). Allocating CO2 emission to customers on a distribution route. Omega, 54, 191-199.
7
Omidi-Rekavandi, M., Tavakkoli-Moghaddam, R., Ghodratnama, A. & Mehdizadeh, E. (2014). Solving a Novel Closed Loop Supply Chain Network Design Problem by Simulated Annealing. Applied mathematics in Engineering, Management and Technology., 2(3), 404-415.
8
Pham, D., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S. & Zaidi, M. The bees algorithm-A novel tool for complex optimisation. Intelligent Production Machines and Systems-2nd I* PROMS Virtual International Conference (3-14 July 2006), (2011). sn.
9
Pishvaee, M. S., Rabbani, M. & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637-649.
10
Pouralikhani, H., Najmi, H., Yadegari, E. & Mohammadi, E. (2013). A multi-period model for managing used products in green supply chain management under uncertainty. J. Basic Appl. Sci. Res, 3(2), 984-995.
11
Pradenas, L., Oportus, B. & Parada, V. (2013). Mitigation of greenhouse gas emissions in vehicle routing problems with backhauling. Expert Systems with Applications, 40(8), 2985-2991.
12
Sun, L. & Wang, B. (2015). Robust optimisation approach for vehicle routing problems with uncertainty. Mathematical Problems in Engineering, 2015, 1-8.
13
Taghavifard, M., Sheikh, K. & Shahsavari, A. (2009). Modified Ant Colony Algorithm For The Vehicle Routing Problem With Time Windows. International Journal of Industrial Engineering and Production Management, 20(2), 23-30.
14
Tajik, N., Tavakkoli-moghaddam, R., Vahdani, B. & Meysam Mousavi, S. (2014). A robust optimization approach for pollution routing problem with pickup and delivery under uncertainty. Journal of Manufacturing Systems, 33(2), 277-286.
15
Talaei, M., Moghaddam, B. F., Pishvaee, M. S., Bozorgi-Amiri, A. & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry. Journal of Cleaner Production, 113, 662-673.
16
Tavakkoli-moghaddam, R., Azarkish, M. & Sadeghnejad-Barkousaraie, A. (2011). A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem. Expert Systems with Applications, 38(9), 10812-10821.
17
Tavakkoli-moghaddam, R., Safaie, N., Kah, M. M. O. & Rabbani, M. (2007). A New Capacitated Vehicle Routing Problem with Split Service for Minimizing Fleet Cost by Simulated Annealing. Journal of the Franklin Institute, 344(5), 406-425.
18
Tibben-lembke, R. S. & Rogers, D. S. (2002). Differences between forward and reverse logistics in a retail environment. Supply Chain Management: An International Journal, 7(5), 271-282.
19
Wang, H.-F. & Hsu, H.-W. (2010). A closed-loop logistic model with a spanning-tree based genetic algorithm. Computers & operations research, 37(2), 376-389.
20
ORIGINAL_ARTICLE
Minimizing the maximum tardiness and makespan criteria in a job shop scheduling problem with sequence dependent setup times
The job shop scheduling problem (JSP) is one of the most difficult problems in traditional scheduling because any job consists of a set operations and also any operation processes by a machine. Whereas the operation is placed in the machine, it is essential to be considering setup times that the times strongly depend on the various sequencing of jobs on the machines. This research is developed a two-objective model to solve JSP with sequence-dependent setup times (SDST). Considering SDST and optimizing of the both objectives simultaneously (makespan and maximum tardiness) bring us closer to natural-world problems. The ε-constraint method is applied to solve the mentioned two-objective model. A set of numerical data is generated and tested to validate the model’s efficiency and flexibility. The developed model can efficiently use for solving JSPs in the real world, especially for manufacturing companies with having setup and delivery time’s constraints.
https://www.jise.ir/article_57040_59ef11a3c39c282089bba7bd9aa316bd.pdf
2018-10-10
134
150
Job shop scheduling
sequence-dependent setup times
makespan criterion
maximum tardiness criterion
mixed integer nonlinear programming
Mehdi
Heydari
mheydari@iust.ac.ir
1
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
Adel
Aazami
a_aazami@ind.iust.ac.ir
2
School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Al-Hinai, N. and T. Y. ElMekkawy (2011a). An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem. Flexible Services and Manufacturing Journal 23(1): 64-85.
1
Al-Hinai, N. and T. Y. ElMekkawy (2011b). Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm. International Journal of Production Economics 132(2): 279-291.
2
Asano, M. and H. Ohta (2002). A heuristic for job shop scheduling to minimize total weighted tardiness. Computers & Industrial Engineering 42(2–4): 137-147.
3
Bagheri, A. and M. Zandieh (2011). Bi-criteria flexible job-shop scheduling with sequence-dependent setup times—variable neighborhood search approach. Journal of Manufacturing Systems 30(1): 8-15.
4
Brucker, P., et al. (1994). A branch and bound algorithm for the job-shop scheduling problem. Discrete Applied Mathematics 49(1–3): 107-127.
5
Carlier, J. and E. Pinson (1989). An Algorithm for Solving the Job-Shop Problem. Management Science 35(2): 164-176.
6
Della Croce, F., et al. (1995). A genetic algorithm for the job shop problem. Computers & Operations Research 22(1): 15-24.
7
Ebadi, A. and G. Moslehi (2013). An optimal method for the preemptive job shop scheduling problem. Computers & Operations Research 40(5): 1314-1327.
8
Fattahi, P. and F. Daneshamooz (2017). Hybrid algorithms for Job shop Scheduling Problem with Lot streaming and A Parallel Assembly Stage. Journal of Industrial and Systems Engineering 10(3): 92-112.
9
Fattahi, P., et al. (2007). Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. Journal of Intelligent Manufacturing 18(3): 331-342.
10
Garey, M. R., et al. (1976). The Complexity of Flowshop and Jobshop Scheduling. Mathematics of Operations Research 1(2): 117-129.
11
Gonçalves, J. F., et al. (2005). A hybrid genetic algorithm for the job shop scheduling problem. European Journal of Operational Research 167(1): 77-95.
12
González, M. A., et al. (2013). Lateness minimization with Tabu search for job shop scheduling problem with sequence dependent setup times. Journal of Intelligent Manufacturing 24(4): 741-754.
13
Kim, S. and P. Bobrowski (1994). Impact of sequence-dependent setup time on job shop scheduling performance. The International Journal of Production Research 32(7): 1503-1520.
14
Kuhpfahl, J. and C. Bierwirth (2016). A study on local search neighborhoods for the job shop scheduling problem with total weighted tardiness objective. Computers & Operations Research 66: 44-57.
15
Kurdi, M. (2016). An effective new island model genetic algorithm for job shop scheduling problem. Computers & Operations Research 67: 132-142.
16
Laarhoven, P. J. M. v., et al. (1992). Job Shop Scheduling by Simulated Annealing. Operations Research 40(1): 113-125.
17
Lee, Y. H. and M. Pinedo (1997). Scheduling jobs on parallel machines with sequence-dependent setup times. European Journal of Operational Research 100(3): 464-474.
18
Li, J.-Q., et al. (2014). A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities. Applied Mathematical Modelling 38(3): 1111-1132.
19
Mattfeld, D. C. and C. Bierwirth (2004). An efficient genetic algorithm for job shop scheduling with tardiness objectives. European Journal of Operational Research 155(3): 616-630.
20
Mousakhani, M. (2013). Sequence-dependent setup time flexible job shop scheduling problem to minimise total tardiness. International journal of production research 51(12): 3476-3487.
21
Naderi, B. and A. Azab (2014). Modeling and heuristics for scheduling of distributed job shops. Expert Systems with Applications 41(17): 7754-7763.
22
Naderi, B., et al. (2009). Scheduling job shop problems with sequence-dependent setup times. International journal of production research 47(21): 5959-5976.
23
Naderi, B., et al. (2009). Scheduling sequence-dependent setup time job shops with preventive maintenance. The International Journal of Advanced Manufacturing Technology 43(1): 170-181.
24
Nowicki, E. and C. Smutnicki (1996). A Fast Taboo Search Algorithm for the Job Shop Problem. Management Science 42(6): 797-813.
25
Özgüven, C., et al. (2012). Mixed integer goal programming models for the flexible job-shop scheduling problems with separable and non-separable sequence dependent setup times. Applied Mathematical Modelling 36(2): 846-858.
26
Park, B. J., et al. (2003). A hybrid genetic algorithm for the job shop scheduling problems. Computers & Industrial Engineering 45(4): 597-613.
27
Petrovic, S., et al. (2008). Fuzzy job shop scheduling with lot-sizing. Annals of Operations Research 159(1): 275-292.
28
Pinedo (2012). Scheduling: theory, algorithms, and systems, Springer Science & Business Media.
29
Pinedo and M. Singer (1999). A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop. Naval Research Logistics 46(1): 1-17.
30
Ponnambalam, G. S., et al. (2000). A Tabu Search Algorithm for Job Shop Scheduling. The International Journal of Advanced Manufacturing Technology 16(10): 765-771.
31
Qi, G. J., et al. (2000). The Application of Parallel Multipopulation Genetic Algorithms to Dynamic Job-Shop Scheduling. The International Journal of Advanced Manufacturing Technology 16(8): 609-615.
32
Saidi-Mehrabad, M. and P. Fattahi (2007). Flexible job shop scheduling with tabu search algorithms. The International Journal of Advanced Manufacturing Technology 32(5): 563-570.
33
Sharma, P. and A. Jain (2015). Performance analysis of dispatching rules in a stochastic dynamic job shop manufacturing system with sequence-dependent setup times: Simulation approach. CIRP Journal of Manufacturing Science and Technology 10: 110-119.
34
Shen, L., et al. (2017). Solving the Flexible Job Shop Scheduling Problem with Sequence-Dependent Setup Times. European Journal of Operational Research.
35
Sun, X. and J. S. Noble (1999). An approach to job shop scheduling with sequence-dependent setups. Journal of Manufacturing Systems 18(6): 416-430.
36
Taillard, E. D. (1994). Parallel Taboo Search Techniques for the Job Shop Scheduling Problem. ORSA Journal on Computing 6(2): 108-117.
37
Tan, et al. (2015). Configuration and the advantages of the shifting bottleneck procedure for optimizing the job shop total weighted tardiness scheduling problem. Journal of scheduling: 1-24.
38
Watanabe, M., et al. (2005). A genetic algorithm with modified crossover operator and search area adaptation for the job-shop scheduling problem. Computers & Industrial Engineering 48(4): 743-752.
39
Yang, S., et al. (2010). An improved constraint satisfaction adaptive neural network for job-shop scheduling. Journal of scheduling 13(1): 17-38.
40
Zhang, et al. (2007). A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem. Computers & Operations Research 34(11): 3229-3242.
41
Zhang, et al. (2008). A very fast TS/SA algorithm for the job shop scheduling problem. Computers & Operations Research 35(1): 282-294.
42
Zhang, et al. (2012). A genetic algorithm with tabu search procedure for flexible job shop scheduling with transportation constraints and bounded processing times. Computers & Operations Research 39(7): 1713-1723.
43
ORIGINAL_ARTICLE
An algorithm for integrated worker assignment, mixed-model two-sided assembly line balancing and bottleneck analysis
This paper addresses a multi-objective mixed-model two-sided assembly line balancing and worker assignment with bottleneck analysis when the task times are dependent on the worker’s skill. This problem is known as NP-hard class, thus, a hybrid cyclic-hierarchical algorithm is presented for solving it. The algorithm is based on Particle Swarm Optimization (PSO) and Theory of Constraints (TOC) and consists of two stages. In stage one, simultaneous balancing and worker assignment are studied. In stage two, bottleneck analysis and product-mix determination are carried out. In addition, a bi-level mathematical model is presented to describe the problem. The following objective functions are verified in this paper: (1) minimizing the number of mated-stations (2), minimizing the number of stations (3) minimizing the human costs (4) minimizing the weighted smoothness index and (5) maximizing the total profit. In addition to the proposed algorithm, another algorithm, which is based on the simulated annealing and the theory of constraints, is developed to compare the performance of the proposed algorithm in terms of the running time and the solution quality over the different benchmarked test problems. Moreover, several lower bounds are developed for the number of the stations and the number of the mated-stations. The results show and support the efficiency of the proposed approaches.
https://www.jise.ir/article_57658_8c2660e46811901cb3c07dbcd53417b8.pdf
2018-10-10
151
174
Two-sided assembly line balancing problem (TSALBP)
worker assignment
mixed-model
particle swarm optimization algorithm (PSO)
simulated annealing algorithm (SA)
theory of constraints
Parvaneh
Samouei
samouei_parvaneh@yahoo.com
1
Department of industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
LEAD_AUTHOR
Parviz
Fattahi
p.fattahi@alzahra.ac.ir
2
Department of Industrial Engineering, Alzahra University, Tehran, Iran
AUTHOR
Araújo, F.B., Costa, A, M., & Miralles, C. (2012). Two extensions for the ALWABP: Parallel stations and collaborative approach. International Journal of Production Economics, 140, 483–495.
1
Bartholdi, J.J. (1993). Balancing two-sided assembly lines: a case study. International Journal of Production Research, 31(10), 2447–2461.
2
Battaïa, O., & Dolgui, A. (2013). A taxonomy of line balancing problems and their solution approaches. International Journal of Production Economics, 142(2), 259–277.
3
Blum, C., & Miralles, C. (2011). On solving the assembly line worker assignment and balancing problem via beam search. Computers & Operations Research, 38, 328–339.
4
Borba, L., & Ritt, M. (2014). A heuristic and a branch-and-bound algorithm for the Assembly Line Worker Assignment and Balancing Problem. Computers & Operations Research 4587–4596.
5
Boysen, N., Fliedner, M., & Scholl, A. (2007). A classification of assembly line balancing problems. European Journal of Operational Research, 183, 674–693.
6
Boysen, N., Fliedner, M., & Scholl, A. (2008). Assembly line balancing: Which model to use when?. International Journal of Production Economics, 111, 509–528.
7
Cannas, V. G., Pero, M., Pozzi, R., Tommaso Rossi, T. (2018). Complexity reduction and kaizen events to balance manual assembly lines: an application in the field. International Journal of Production Research, 1-18.
8
Chutima, P., & Chimklai, P. (2012). Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimisation with negative knowledge. Computers and Industrial Engineering, 62, 39–55.
9
Costa, A. M., & Miralles, C. (2009). Job rotation in assembly lines employing disabled workers. International Journal of Production Economics, 120, 625–632.
10
Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley and Sons, Inc, New York, NY, USA.
11
Dolgui, A., Kovalev, S., Kovalyov, M. Y., Malyutin, S., Soukhal, A. (2018). Optimal workforce assignment to operations of a paced assembly line. European Journal of Operational Research 24(1), 200-211.
12
Giglio, D., Paolucci, M., Roshani, A.R., Tonelli, F. (2017). Multi-manned Assembly Line Balancing Problem with Skilled Workers: A New Mathematical Formulation. IFAC-Papers On Line 50 (1), 1211-1216.
13
Hamta, N., Fatemi Ghomi, S.M.T., Jolai, F., & Akbarpour Shirazi, M. (2013). A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect. International Journal of Production Economics, 141(1), 99-111.
14
Hu, S.J., Ko, J., Weyand, L., El Maraghy, H.A., Lien, T.K., Koren, Y., Bley, H., Chryssolouris, G., Nasr, N., & Shpitalni, M. (2011). Assembly system design and operations for product variety. CIRP Annals-Manufacturing Technology, 60, 715–733.
15
Kellegöz, T. (2017). Assembly line balancing problems with multi-manned stations: a new mathematical formulation and Gantt based heuristic method. Annals of Operations Research 253(1), 377-404.
16
Kennedy, J., & Eberhart, R.C. (1995). Particle swarm optimization. In proceedings of IEEE international Conference on Neural Networks (Perth, Australia). 1942-1948.
17
Miralles, C., García-Sabater, J. P., Andrés, C., & Cardos, M. (2007). Advantages of assembly lines in Sheltered Work Centres for Disabled. A case study. International Journal of Production Economics, 110, 187–197.
18
Miralles, C., Garía-Sabater, J. P., Andrés, C., & Cardós, M. (2008). Branch and bound procedures for solving the Assembly Line Worker Assignment and Balancing Problem: Application to Sheltered Work centres for Disabled. Discrete Applied Mathematics, 156, 352-367.
19
Moreira, M. C. O., Ritt, M., Costa, A. M., & Chaves, A. A. (2012). "Simple heuristics for the assembly line worker assignment and balancing problem. Journal of Heuristics, 18, 505–524.
20
Mutlu, Ö., Polat, O., & Supciller, A. A. (2013). An iterative genetic algorithm for the assembly line worker assignment and balancing problem of type-II. Computers & Operations Research, 40 (1), 418–426.
21
Özcan, U., & Toklu, B. (2009). Balancing of mixed-model two-sided assembly lines. Computers and Industrial Engineering, 57, 217–227.
22
Özcan, U., Gokcen, H., & Toklu, B. (2010). Balancing parallel two-sided assembly lines. International Journal of Production Research, 48 (16), 4767–4784.
23
Pastor, R. (2011). LB-ALBP: the lexicographic bottleneck assembly line balancing problem. International Journal of Production Research, 49(8), 2425-2442.
24
Pastor, R., Chueca, I., & García-Villoria, A. (2012). A heuristic procedure for solving the Lexicographic Bottleneck Assembly Line Balancing Problem (LB-ALBP). International Journal of Production Research, 50(7), 1862-1876.
25
Purnomo, H. D., Wee, H. M., & Rau, H. (2013). Two-sided assembly lines balancing with assignment restriction. Mathematical and Computer Modeling, 57, 189–199.
26
Roshani, A. R., Giglio, D. (2017). Simulated annealing algorithms for the multi-manned assembly line balancing problem: minimising cycle time. International Journal of Production Research 55(10), 2731-2751.
27
Salveson, M.E. (1955). The assembly line balancing problem. Journal of Industrial Engineering, 6(3), 18–25.
28
Scholl, A. (1999). balancing and sequencing of assembly lines. Physica-Verlag.
29
Scholl, A., & Becker, C. (2006). State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. European Journal of Operational Research, 168, 666–693.
30
Simaria, A. S., & Vilarinho, P. M. (2009). 2-ANTBAL: An ant colony optimization algorithm for balancing two-sided assembly lines. Computers & Industrial Engineering, 56, 489–506.
31
Sirovetnukul, R., & Chutima, P. (2010). The Impact of Walking Time On U-Shaped Assembly Line Worker Allocation Problems. Engineering Journal, 14 (2), 53-78.
32
Song, B.L., Wong, W.K., Fan, J.T., & Chan, S.F. (2006). A recursive operator allocation approach for assembly line balancing optimization problem with the consideration of operator efficiency. Computers & Industrial Engineering, 51, 585–608.
33
Taguchi, G. (1986). Introduction to Quality Engineering. Asian Productivity organization.
34
Vilà, M., & Pereira, J. (2014). A branch-and-bound algorithm for assembly line worker assignment and balancing problems. Computers & Operations Research, 44, 105–114.
35
Xiaofeng, H., Erfei, W., Jinsong, B., & Ye, J. (2010). A branch-and-bound algorithm to minimize the line length of a two-sided assembly line. European Journal of Operational Research, 206, 703–707.
36
Zaman, T., Paul, S. K., & Azeem, A. (2012). Sustainable operator assignment in an assembly line using genetic algorithm. International Journal of Production Research, 50 (18), 5077–5084.
37
Zhang, W., Gen, M., Lin, L. (2008). A Multi-objective Genetic Algorithm for Assembly Line Balancing Problem with Worker Allocation, IEEE International Conference on Systems, Man and Cybernetics.
38
ORIGINAL_ARTICLE
An integrated heuristic method based on piecewise regression and cluster analysis for fluctuation data (A case study on health-care: Psoriasis patients)
Trend forecasting and proper understanding of the future changes is necessary for planning in health-care area.One of the problems of analytic methods is determination of the number and location of the breakpoints, especially for fluctuation data. In this area, few researches are published when number and location of the nodes are not specified.In this paper, a clustering-based method is developed to obtain the number and the location of breakpoints. We propose an appropriate piecewise regressionmodel to analyze the fluctuation data and predict trends of them.Theefficiency of proposed integrated approach is evaluated by using a simulated and real example, and results are compared with results of Mars algorithm. Comparison shows that proposed approach has less sum of square error (SSE) criterion than Mars algorithm with equall number of nods.
https://www.jise.ir/article_59612_4b192da3e9a86efbd7fa37fce6b4d284.pdf
2018-10-10
175
189
Piecewise regression
node
Clustering
Mars algorithm
health-care systems
Farnoosh
Bagheri
bagheri@usc.ac.ir
1
Department of Industrial Engineering, University of Science and Culture, Tehran, Iran
LEAD_AUTHOR
mahsa
laari
mahsa.laari@yahoo.com
2
Department of Industrial Engineering, University of Science and Culture, Tehran, Iran
AUTHOR
Reza
Kamranrad
rezakamranrad@gmail.com
3
Department of Industrial Engineering, University of Science and Culture, Tehran, Iran
AUTHOR
Majid
Jalili
jalili7@yahoo.com
4
Department of Industrial Engineering, Material and Energy Research Center, Karaj, Iran
AUTHOR
Arsang, S. H., Kazemnejad, A., & Amani, F. (2011). Applying segmented regression model to analysis the trend of tuberculosis incidence rate in Iran between 1964-2008. Iranian Journal of Epidemiology, 7(3), 6-12.
1
Bashiri, M, Kamranrad, R. (2015). Applied multivariate statistical method (in Persian), Shahed university, Tehran, Iran.
2
Cavanaugh, K. C., Kellner, J. R., Forde, A. J., Gruner, D. S., Parker, J. D., Rodriguez, W., & Feller, I. C. (2014). Poleward expansion of mangroves is a threshold response to decreased frequency of extreme cold events. Proceedings of the National Academy of Sciences, 111(2), 723-727.
3
Chamroukhi, F. (2016). Piecewise regression mixture for simultaneous functional data clustering and optimal segmentation. Journal of Classification, 33(3), 374-411.
4
D’Avino, D., & Erto, P. (2008, February). A piecewise regression model for identifying the strategic inflection point for organizational change. In 10th QMOD Conference. Quality Management and Organiqatinal Development. Our Dreams of Excellence; 18-20 June; 2007 in Helsingborg; Sweden (No. 026). Linköping University Electronic Press.
5
De Andrés, J., Lorca, P., de Cos Juez, F. J., & Sánchez-Lasheras, F. (2011). Bankruptcy forecasting: A hybrid approach using Fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS). Expert Systems with Applications, 38(3), 1866-1875.
6
Ferrarini, A. (2011). Detecting ecological breakpoints: a new tool for piecewise regression. Computational Ecology and Software, 1(2), 121-124.
7
Ekman, T., & Kubin, G. (1999). Nonlinear prediction of mobile radio channels: measurements and MARS model designs. In Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on (Vol. 5, pp. 2667-2670). IEEE.
8
Greene, M. E., Rolfson, O., Garellick, G., Gordon, M., & Nemes, S. (2015). Improved statistical analysis of pre-and post-treatment patient-reported outcome measures (PROMs): the applicability of piecewise linear regression splines. Quality of Life Research, 24(3), 567-573.
9
Hong, A., & Chen, A. (2012). Piecewise regression model construction with sample efficient regression tree (SERT) and applications to semiconductor yield analysis. Journal of Process Control, 22(7), 1307-1317.
10
Jafari-Koshki, T., Hosseini, S. M., Arsang-Jang, S., Amini, M., & Faghihimani, E. (2015). Trends of diabetic nephropathy prevalence in Isfahan, Iran, during 1992-2010. Journal of research in medical sciences: the official journal of Isfahan University of Medical Sciences, 20(10), 944.
11
Jalili, M. (2015). Developing robust prediction model for passenger demand and cargo shocks in iran’s air industry using piecewise regression (in Persian).
12
John Lu, Z. Q. (2010). The elements of statistical learning: data mining, inference, and prediction. Journal of the Royal Statistical Society: Series A (Statistics in Society), 173(3), 693-694.
13
Leathwick, J. R., Rowe, D., Richardson, J., Elith, J., & Hastie, T. (2005). Using multivariate adaptive regression splines to predict the distributions of New Zealand's freshwater diadromous fish. Freshwater Biology, 50(12), 2034-2052.
14
Leathwick, J. R., Elith, J., & Hastie, T. (2006). Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecological modelling, 199(2), 188-196.
15
Lee, T. S., Chiu, C. C., Chou, Y. C., & Lu, C. J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis, 50(4), 1113-1130.
16
Lee, T. S., & Chen, I. F. (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, 28(4), 743-752.
17
Malash, G. F., & El-Khaiary, M. I. (2010). Piecewise linear regression: A statistical method for the analysis of experimental adsorption data by the intraparticle-diffusion models. Chemical Engineering Journal, 163(3), 256-263.
18
Marsh, L. C., & Cormier, D. R. (2001). Spline regression models (Vol. 137). Sage.
19
Matthews, T. J., Steinbauer, M. J., Tzirkalli, E., Triantis, K. A., & Whittaker, R. J. (2014). Thresholds and the species–area relationship: a synthetic analysis of habitat island datasets. Journal of Biogeography, 41(5), 1018-1028.
20
Muggeo, V. M. (2003). Estimating regression models with unknown break‐points. Statistics in medicine, 22(19), 3055-3071.
21
Muggeo, V. M. (2008). Segmented: an R package to fit regression models with broken-line relationships. R news, 8(1), 20-25.
22
Strikholm, B. (2006). Determining the number of breaks in a piecewise linear regression model (No. 648). SSE/EFI Working Paper Series in Economics and Finance.
23
Trevor, H., Robert, T., & JH, F. (2009). The elements of statistical learning: data mining, inference, and prediction.
24
Toms, J. D., & Lesperance, M. L. (2003). Piecewise regression: a tool for identifying ecological thresholds. Ecology, 84(8), 2034-2041.
25
Werner, R., Valev, D., Danov, D., & Guineva, V. (2015). Study of structural break points in global and hemispheric temperature series by piecewise regression. Advances in Space Research, 56(11), 2323-2334.
26
Xue, Y., Liu, S., Zhang, L., & Hu, Y. (2013). Integrating fuzzy logic with piecewise linear regression for detecting vegetation greenness change in the Yukon River Basin, Alaska. International journal of remote sensing, 34(12), 4242-4263.
27
Yang, L., Liu, S., Tsoka, S., & Papageorgiou, L. G. (2016). Mathematical programming for piecewise linear regression analysis. Expert Systems with Applications, 44, 156-167.
28
York, T. P., Eaves, L. J., & van den Oord, E. J. (2006). Multivariate adaptive regression splines: a powerful method for detecting disease–risk relationship differences among subgroups. Statistics in medicine, 25(8), 1355-1367.
29
Yu, J. R., Tzeng, G. H., & Li, H. L. (2001). General fuzzy piecewise regression analysis with automatic change-point detection. Fuzzy sets and systems, 119(2), 247-257.
30
ORIGINAL_ARTICLE
A credit period contract towards coordination of pharmaceutical supply chain:The case of inventory-level-dependent demand
This paper considers a two stages pharmaceutical supply chain (PSC) consisting of a pharmaceutical manufacturer (pharma-manufacturer) that supplies one type of pharmaceutical product to a pharma-retailer. The customer demand rate for the pharmaceutical product is dependent on the pharma-retailer’s current-inventory level. The pharma-retailer determines the order quantity ( ) value as decision variable and the pharma-manufacturer uses EPQ system that usually the economic order quantity value of retailer is less than the optimal production quantity value of manufacturer. First, the problem is investigated in decentralized decision-making and accordingly, a coordination incentive based on credit payment period policy to coordinate the mentioned PSC in two structures is proposed: independent optimization and centralized model with credit policy. Moreover, numerical examples and sensitivity analysis are considered to illustrate the results of the presented coordination structures toward decentralized model.
https://www.jise.ir/article_59680_4ee7d0e57baac0f44888875e3b691ca5.pdf
2018-10-11
190
206
Pharmaceutical supply chain
inventory-dependent demand
production
credit payment period
Coordination
Mahdi
Ebrahimzadeh-Afruzi
m_ebrahimzade94@ind.iust.ac.ir
1
School of Industrial Engineering, Iran University of Science and Technology
AUTHOR
Alireza
Aliahmadi
pe@iust.ac.ir
2
School of Industrial Engineering, Iran University of Science and Technology
LEAD_AUTHOR
Arkan, A. and S. R. Hejazi (2012). "Coordinating orders in a two echelon supply chain with controllable lead time and ordering cost using the credit period." Computers & Industrial Engineering 62(1): 56-69.
1
2
Baker, R. and T. L. Urban (1988). "A deterministic inventory system with an inventory-level-dependent demand rate." Journal of the operational research society: 823-831.
3
4
Bishara, R. H. (2006). "Cold chain management–an essential component of the global pharmaceutical supply chain." American Pharmaceutical Review 9(1): 105-109.
5
6
Braglia, M., et al. (2016). "A novel approach to safety stock management in a coordinated supply chain with controllable lead time using present value." Applied Stochastic Models in Business and Industry 32(1): 99-112.
7
8
Chan, C. K., et al. (2010). "A delayed payment method in co-ordinating a single-vendor multi-buyer supply chain." International Journal of Production Economics127(1): 95-102.
9
10
Chen, K. (2012). "Procurement strategies and coordination mechanism of the supply chain with one manufacturer and multiple suppliers." International Journal of Production Economics138(1): 125-135.
11
12
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99
ORIGINAL_ARTICLE
Multi-item inventory model with probabilistic demand function under permissible delay in payment and fuzzy-stochastic budget constraint: A signomial geometric programming method
This study proposes a new multi-item inventory model with hybrid cost parameters under a fuzzy-stochastic constraint and permissible delay in payment. The price and marketing expenditure dependent stochastic demand and the demand dependent the unit production cost are considered. Shortages are allowed and partially backordered. The main objective of this paper is to determine selling price, marketing expenditure, credit period, and variables of inventory control simultaneously for maximizing the total profit. To solve the problem, first some transformations are applied to convert the original problem into a multi-objective nonlinear programming problem, of which each objective has signomial terms. Then, the multi-objective nonlinear programming problem is solved by first converting it into a single objective problem and then by using global optimization of signomial geometric programming problems. At the end, several numerical examples and sensitivity analysis are done to test model and solution procedure and also obtain managerial insights.
https://www.jise.ir/article_59681_42adbf72cce5732557c02e686f886c37.pdf
2018-10-16
207
227
Signomial geometric programming
delay in payment
fuzzy-stochastic recourse
price and marketing dependent stochastic demand
EOQ
Masoud
Rabani
mrabani@ut.ac.ir
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Leila
Aliabadi
leyla.aliabadi@ut.ac.ir
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Aggarwal, S. and Jaggi, C. (1995) 'Ordering policies of deteriorating items under permissible delay in payments', Journal of the operational research society, 658-662.
1
Cheng, T. (1991) 'EPQ with process capability and quality assurance considerations', Journal of the Operational Research Society, 42(8), 713-720.
2
Chung, K.-J. and Huang, Y.-F. (2003) 'The optimal cycle time for EPQ inventory model under permissible delay in payments', International Journal of Production Economics, 84(3), 307-318.
3
Chung, K.-J., Liao, J.-J., Ting, P.-S., Lin, S.-D. and Srivastava, H.M. (2015) 'The algorithm for the optimal cycle time and pricing decisions for an integrated inventory system with order-size dependent trade credit in supply chain management', Applied Mathematics and Computation, 268, 322-333.
4
Das, K., Roy, T.K. and Maiti, M. (2004) 'Multi-item stochastic and fuzzy-stochastic inventory models under two restrictions', Computers & Operations Research, 31(11), 1793-1806.
5
De, S.K. and Sana, S.S. (2015) 'Backlogging EOQ model for promotional effort and selling price sensitive demand-an intuitionistic fuzzy approach', Annals of Operations Research, 233(1), 57-76.
6
Goyal, S.K. (1985) 'Economic order quantity under conditions of permissible delay in payments', Journal of the operational research society, 335-338.
7
He, Y., Zhao, X., Zhao, L. and He, J. (2009) 'Coordinating a supply chain with effort and price dependent stochastic demand', Applied Mathematical Modelling, 33(6), 2777-2790.
8
Ho, C.-H., Ouyang, L.-Y. and Su, C.-H. (2008) 'Optimal pricing, shipment and payment policy for an integrated supplier–buyer inventory model with two-part trade credit', European Journal of Operational Research, 187(2), 496-510.
9
Huang, Y.-F. (2007) 'Economic order quantity under conditionally permissible delay in payments', European Journal of Operational Research, 176(2), 911-924.
10
Islam, S. and Roy, T.K. (2006) 'A fuzzy EPQ model with flexibility and reliability consideration and demand dependent unit production cost under a space constraint: A fuzzy geometric programming approach', Applied Mathematics and computation, 176(2), 531-544.
11
Jamal, A., Sarker, B. and Wang, S. (1997) 'An ordering policy for deteriorating items with allowable shortage and permissible delay in payment', Journal of the operational research society, 48(8), 826-833.
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14
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15
Maihami, R. and Abadi, I.N.K. (2012) 'Joint control of inventory and its pricing for non-instantaneously deteriorating items under permissible delay in payments and partial backlogging', Mathematical and Computer Modelling, 55(5), 1722-1733.
16
Maihami, R. and Karimi, B. (2014) 'Optimizing the pricing and replenishment policy for non-instantaneous deteriorating items with stochastic demand and promotional efforts', Computers & Operations Research, 51, 302-312.
17
Maihami, R., Karimi, B. and Ghomi, S.M.T.F. (2017) 'Effect of two-echelon trade credit on pricing-inventory policy of non-instantaneous deteriorating products with probabilistic demand and deterioration functions', Annals of Operations Research, 257(1-2), 237-273.
18
Mandal, N.K., Roy, T.K. and Maiti, M. (2006) 'Inventory model of deteriorated items with a constraint: A geometric programming approach', European Journal of Operational Research, 173(1), 199-210.
19
Mutapcic, A., Koh, K., Kim, S. and Boyd, S. (2006) 'GGPLAB version 1.00: a Matlab toolbox for geometric programming'.
20
Panda, D., Kar, S. and Maiti, M. (2008) 'Multi-item EOQ model with hybrid cost parameters under fuzzy/fuzzy-stochastic resource constraints: a geometric programming approach', Computers & Mathematics with Applications, 56(11), 2970-2985.
21
Pang, Q., Chen, Y. and Hu, Y. (2014) 'Coordinating three-level Supply Chain by revenue-sharing contract with sales effort dependent demand', Discrete Dynamics in Nature and Society, 2014.
22
Pramanik, P., Maiti, M.K. and Maiti, M. (2017) 'A supply chain with variable demand under three level trade credit policy', Computers & Industrial Engineering, 106, 205-221.
23
Sadjadi, S.J., Hesarsorkh, A.H., Mohammadi, M. and Naeini, A.B. (2015) 'Joint pricing and production management: a geometric programming approach with consideration of cubic production cost function', Journal of Industrial Engineering International, 11(2), 209-223.
24
Samadi, F., Mirzazadeh, A. and Pedram, M.M. (2013) 'Fuzzy pricing, marketing and service planning in a fuzzy inventory model: a geometric programming approach', Applied Mathematical Modelling, 37(10), 6683-6694.
25
Sarkar, B., Saren, S. and Cárdenas-Barrón, L.E. (2015) 'An inventory model with trade-credit policy and variable deterioration for fixed lifetime products', Annals of Operations Research, 229(1), 677-702.
26
Soni, H. and Patel, K. (2012) 'Optimal pricing and inventory policies for non-instantaneous deteriorating items with permissible delay in payment: Fuzzy expected value model', International journal of industrial engineering computations, 3(3), 281-300.
27
Soni, H.N. (2013) 'Optimal replenishment policies for non-instantaneous deteriorating items with price and stock sensitive demand under permissible delay in payment', International journal of production Economics, 146(1), 259-268.
28
Tabatabaei, S.R.M., Sadjadi, S.J. and Makui, A. (2017) 'Optimal production and marketing planning with geometric programming approach'.
29
Taleizadeh, A.A., Pentico, D.W., Jabalameli, M.S. and Aryanezhad, M. (2013) 'An EOQ model with partial delayed payment and partial backordering', Omega, 41(2), 354-368.
30
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31
Xu, G. (2014) 'Global optimization of signomial geometric programming problems', European Journal of Operational Research, 233(3), 500-510.
32
ORIGINAL_ARTICLE
Developing a fuzzy expert system to predict technology commercialization success
A majority of efforts in terms of technology commercialization have failed; however, the issue of commercialization and its high importance are agreed upon by policymakers, entrepreneurs and researchers. This shows the high complexity of the commercialization process. One of the main solutions to overcome the commercialization problems is to predict the success of technology commercialization before its implementation. Hence, this study aims to design a fuzzy expert system to predict the technology commercialization success in the early stages of its development and before its implementation. According to the literature review and the fuzzy Delphi method, the technology commercialization success factors (TCSFs) were identified and refined. The final result of the fuzzy Delphi process consists of 32 components categorized in four dimensions: technical specifications, financial and economic specifications, market specifications and rules and regulations. These success dimensions form the inputs of the prediction model in this study. The performance of the model was evaluated by actual samples selected from different fields of technology. The accuracy of the model was estimated to be 73% according to a validation process, indicating the high accuracy of the proposed model in predicting the commercialization success. This model could be used practically by risk-taking investors, technology advocates and innovators to adopt new technology commercialization opportunities.
https://www.jise.ir/article_74015_094b4a1e30d23cd5ea4fefb5c0707ba3.pdf
2018-10-17
228
250
Technology commercialization
technology commercialization success factors
commercialization success predict
fuzzy expert system
Jafar
YazdiMoghaddam
j.yazdimoghaddam@gmail.com
1
Faculty of Industrial Engineering, Yazd University, Yazd, Iran
AUTHOR
Mohammad Salleh
Owlia
owliams@yazd.ac.ir
2
Faculty of Industrial Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
Reza
Bandarian
bandarianr@ripi.ir
3
Business Development Department, Research Institute of Petroleum Industry (RIPI), Tehran, Iran
AUTHOR
Alhabashneh, O., Iqbal, R., Doctor, F. & James, A. (2017), "Fuzzy rule based profiling approach for enterprise information seeking and retrieval". Information Sciences 394–395, pp.18–37.
1
Amiri, M., Ardeshir, A. & Fazel Zarandi, M.H. (2017), "Fuzzy probabilistic expert system for occupational hazard assessment in construction". Safety Science, 93, 16–28.
2
Åstebro T. (2004), "Key success factors for technological entrepreneurs R&D Project". IEEE Transactions on Engineering Management, 51(3), pp. 314-321.
3
Balachandra, R., Goldschmidt, M. & Friar, J. H. (2004), "The evolution of technology generations and associated markets: A double helix model". IEEE Transactions on Engineering Management, 51, 3–12.
4
Bandarian, R. (2007), "Evaluation of commercial potential of a new technology at the early stage of development with fuzzy logic", Journal of Technology Management & Innovation, 2(4), pp.73-85.
5
Beriha, G.S., Patnaik, B., Mahapatra, S.S., Padhee, S. (2012), "Assessment of safety performance in Indian industries using fuzzy approach". Expert Systems with Applications, 39, 3311–3323.
6
Brown, M. A. (1997), "Performance metrics for a technology commercialization program", International Journal of Technology Management, 13(3), pp. 229-244.
7
Cheng, CH., Ching, H. & Lin, Y. (2002), "Evaluating the Best Main Battle Tank Using Fuzzy Decision Theory with Linguistic Criteria Evaluation". European Journal of Operational Research, Vol. 142, pp. 174-186.
8
Chifos, C. & Jain R. K. (1997), "A comprehensive methodology for evaluating the commercial potential of technologies: the strategic technology evaluation method", International Journal of Industrial Engineering, 4(4), pp. 220-235.
9
Cho, J. & Lee, J. (2013), "Development of a new technology product evaluation model for assessing commercialization opportunities using Delphi method and fuzzy AHP approach", Expert Systems with Applications, 40, pp. 5314–5330.
10
Department Homeland Security. (2013), DHS SBIR Commercialization Assistance Workshop, 8th floor, Washington DC, pp. 6-12.
11
Galbraith, C.S., DeNoble, A.F., Ehrlich, S.B & Kline, D.M. (2007), "Can experts really assess future technology success? A neural network and Bayesian analysis of early stage technology proposals". The Journal of High Technology Management Research, 17, pp. 125-137.
12
Giarratano J. and, Riley, G. (2004),Expert Systems: Principles and Programming, Fourth Edition, PWS Publishing Company.
13
Hadavandi, E., Shavandi, H. and Ghanbari, A. (2010), "Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting", Knowledge-Based Systems, 23, No. 8, pp. 800-808.
14
Haji pour, V. Kazemi, A. Mousavi, S.M. (2013), "A Fuzzy Expert System to Increase Accuracy and Precision in Measurement System Analysis", Measurement, pp.1-18.
15
Hillston, J. (2005). A compositional approach to performance modelling, Cambridge University Press.
16
Hsu, D.W.L. Shen, Y.C. Yuan, B.J.C & Chou. C.J. (2015), "Toward successful commercialization of university technology: Performance drivers of university technology transfer in Taiwan", Technological Forecasting & Social Change, 92, pp. 25-39.
17
Jung, M. Lee, Y. & Lee H. (2015), "Classifying and prioritizing the success and failure factors of technology commercialization of public R&D in South Korea: using classification tree analysis", Journal of Technology Transfer, 40, pp. 877–898.
18
Kalali, N. S. (2015). A fuzzy inference system for supporting the retention strategies of human capital. Procedia – Social and Behavioral Sciences, 207, 344 –353.
19
Kathleen, A. R. (2003), Bringing New Technology to Market, Prentice Hall, New Jersey.
20
Kimura, O. (2010), "Public R&D and commercialization of energy-efficient technology: a case study of Japanese projects". Energy Policy, 38, pp. 7358–7369.
21
Kirchberger, M. A. & Pohl, L. (2016), "Technology commercialization: a literature review of success factors and antecedents across different contexts", Journal of Technology Transfer, 41(3).
22
Kumar, V. & Jain, R. K. (2003), "Commercialization of new technologies in India: An empirical study of perception of technology institutions". Technovation, 23(2), pp. 113-120.
23
Link, A.N. & Scott, J.T. (2010), "Government as entrepreneur: Evaluating the commercialization success of SBIR projects", Research Policy, 39, pp. 589-601.
24
Linstone, H.A. & Turoff, M. (2002), The Delphi Method Techniques and applications, Online Available: www.inei.org.br/ inovateca/estudos-e-pesquisas-em inovacao/ delphibook.pdf.
25
Martyniuk, A. O., Jain, R. K. & Stone, H. J. (2003), "Critical success factors and barriers to technology transfer: case studies and implications", International Journal of Technology Transfer and Commercialization, 2(3) pp. 306-327.
26
Mirghafoori, S.H. Sadeghi Arani, Z. and Jafarnejad, A. (1390), "Forecasting success of commercialization of innovative ideas using artificial neural networks; the case of inventors and innovations in Yazd province", Iranian Journal of Science and Technology Policy, 4(1), pp. 63-76.
27
Mohan, S.R. & Rao A.R. (2003), "Early identification of innovation and market acceptable technologies-A model for improving technology transfer capabilities of research institutes". Journal of scientific &industrial research, 62, pp. 865-875.
28
Mohannak, K. & Samtani, L. (2014), "A Criteria-based Approach for Evaluating Innovation Commercialization", DRUID Society Conference 2014, CBS, Copenhagen, June 16-18, pp. 1-15.
29
NASA Office of the Chief Technologist, (2012), NASA Procedural Requirements: NASA Technology Commercialization Process, Chp.3, pp. 1-5.
30
Pal, D., Mandana, K.M., Pal, S., Sarkar, D. & Chakraborty, Ch. (2012), "Fuzzy expert system approach for coronary artery disease screening using clinical parameters". Knowledge-Based Systems, 36, 162–174.
31
Rahal, A.D. & Rabelo, L.C. (2006), "Assessment Framework for the Evaluation and Prioritization of University Inventions for Licensing and Commercialization". Engineering Management Journal, 18(4), pp. 28-36.
32
Ravi K. Jain, R.K., Martyniuk, A.O., Harris, M.M., Niemann, R.E. & Woldmann, K. (2003), "Evaluating the Commercial Potential of Emerging Technologies". Int. J. Technology Transfer and Commercialization, 2(1), pp. 32-50.
33
Rostek, K. (2014), "Modeling Commercial Potential of Innovative Projects". International Review of Management and Business Research, 3(1), pp. 78-95.
34
Rubell, M. L. G., & Jessy, J. C. (2016). "A multiple fuzzy inference systems framework for daily stock trading with application to NASDAQ stock exchange". Expert Systems with Applications, 44, 13–21.
35
Siler, W. & Buckley, J. J. (2005), Fuzzy Expert Systems and Fuzzy Reasoning, John Wiley & Sons, Inc.
36
Slater S.F. & Mohr J.J. (2006), "Successful Development and Commercialization of Technological Innovation: Insights Based on Strategy Type". Product Innovation Management, 23, pp. 26-33.
37
Sohn, S.Y. & Moon, T.H. (2003), "Structural equation model for predicting technology commercialization success index (TCSI)". Technological Forecasting & Social Change, 70, pp. 885–899.
38
Yen, J., & Langari, R. (1999). Fuzzy logic: Intelligence, control and information. Prentice Hall, ISBN: 0135258170.
39
Zadeh, L.A., Kacprzyk, J., (1999). Computing with words in Information/Intelligent systems 1: foundations. Physica 33, 10. http://dx.doi.org/10.1007/978-3-7908-1873-4.
40
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41
ORIGINAL_ARTICLE
A differential evolution algorithm to solve new green VRP model by optimizing fuel consumption considering traffic limitations for collection of expired products
The purpose of this research is to present a new mathematical modeling for a vehicle routing problem considering concurrently the criteria such as distance, weight, traffic considerations, time window limitation, and heterogeneous vehicles in the reverse logistics network for collection of expired products. In addition, we aim to present an efficient solution approach according to differential evolution (DE) procedure to solve such a complicated problem. By using mathematical modeling tools for formulating the environmental sensitivities in vehicle routing problems, the reverse logistics must be managed according to criteria such as cargo weight carried by the vehicle, the vehicle speed and the covered distance by the vehicle. This leads to optimization and reduction of transportation fuel consumption and hence reduction of air pollution and environment concerns. This concept has led to creation and study of the green vehicle routing problems in this paper.Numerical analysis indicates that performance of the proposed DE algorithm can be validated in terms of CPU run time and optimality gap for solving the proposed model. Furthermore, sensitivity analysis show that extending maximum travelling distance by each vehicle, and increasing capacity of vehicles lead to reduction of total cost in the problem.
https://www.jise.ir/article_59682_18b5a191775c97b79994878e63506777.pdf
2018-10-17
251
269
Green Vehicle Routing Problem
reverse logistics
expired products
transportation system
differential evolutionary algorithm
Mojgan
Karami
karamimozhgan@yahoo.com
1
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
AUTHOR
Vahidreza
Ghezavati
vrghezavati@gmail.com
2
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
LEAD_AUTHOR
Alshamrani, A., Mathur, K., andBallou, R. H. (2007). Reverse logistics: simultaneous design of delivery routes and returns strategies. Computers and Operations Research, 34, 595–619.
1
Ahmadizar, F.,Zeynivand, M., andArkat, J. (2015), Two-level vehicle routing with cross-docking in a three-echelon supply chain: A genetic algorithm approach, Applied Mathematical Modelling, 39 (22), Pages 7065-7081.
2
Aras, N., Aksen, D., andTekin, M. T.(2011). Selective multi-depot vehicle routing problem with pricing. Transportation Research Part C: Emerging Technologies, 19,866–884.
3
Bauer, J., Bektas_, T., andCrainic, T. G. (2010).Minimizing greenhouse gas emissions in intermodal freight transport: an application to rail service design.Journal of the Operational Research Society, 61, 530–542.
4
Buhrkal,K., Larsen,A.,andRopke,S.(2012). The waste collection vehicle routing problem with time windows in a city logistics context.Procedia - Social and Behavioral Sciences39 , 241 – 254.
5
Bektas_, T., andLaporte, G. (2011).The pollution-routing problem.TransportationResearch Part B, 45, 1232–1250.
6
Dell’Amico, M., Righini, G., andSalani, M. (2006).A branch-and-price approach to the vehicle routing problem with simultaneous distribution and collection.Transportation Science, 40, 235–247.
7
Demir, E., Bektas_, T., andLaporte, G. (2012). An adaptive large neighborhood searchheuristic for the pollution-routing problem. European Journal of Operational Research, 223, 346–359.
8
ErdoŸan, S.,and Miller-Hooks, E. (2012).A green vehicle routing problem.Transportation Research Part E: Logistics and Transportation Review, 48(1)100–114.
9
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