ORIGINAL_ARTICLE
An approximation algorithm and FPTAS for Tardy/Lost minimization with common due dates on a single machine
This paper addresses the Tardy/Lost penalty minimization with common due dates on a single machine. According to this performance measure, if the tardiness of a job exceeds a predefined value, the job will be lost and penalized by a fixed value. Initially, we present a 2-approximation algorithm and examine its worst case ratio bound. Then, a pseudo-polynomial dynamic programming algorithm is developed. We show how to transform the dynamic programming algorithm to an FPTAS using the technique of "structuring the execution of an algorithm" and examine the time complexity of our FPTAS.
http://www.jise.ir/article_13928_157982a0c51e66e6ccb4d360ae167c76.pdf
2016-04-01T11:23:20
2020-09-26T11:23:20
1
19
Single machine scheduling
Tardy/Lost penalty
Common due date
Approximation algorithm
FPTAS
Kamran
Kianfar
k.kianfar@in.iut.ac.ir
true
1
Faculty of Engineering, University of Isfahan, Isfahan, Iran.
Faculty of Engineering, University of Isfahan, Isfahan, Iran.
Faculty of Engineering, University of Isfahan, Isfahan, Iran.
AUTHOR
Ghasem
Moslehi
moslehi@cc.iut.ac.ir
true
2
Department of Industrial and Systems Engineering, Isfahan University of Technology
Department of Industrial and Systems Engineering, Isfahan University of Technology
Department of Industrial and Systems Engineering, Isfahan University of Technology
LEAD_AUTHOR
Ali
Nookabadi
ali-nook@cc.iut.ac.ir
true
3
Department of Industrial and Systems Engineering, Isfahan University of Technology
Department of Industrial and Systems Engineering, Isfahan University of Technology
Department of Industrial and Systems Engineering, Isfahan University of Technology
AUTHOR
ALMINANA, M., ESCUDERO, L. F., LANDETE, M., MONGE, J. F., RABASA, A. & SSANCHEZ-SORIANO, J. 2010. A DSS for water irrigation scheduling. Omega, 38, 492-500.
1
BAPTISTE, P. &SADYKOV, R. 2009. On scheduling a single machine to minimize a piecewise linear objective function: A compact MIP formulation. Naval Research Logistics, 56, 487-502.
2
BLAZEWICZ, J. 1984. Scheduling preemptible tasks on parallel processors with information loss. Technique et Science Informatiques, 3, 415-420.
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BLAZEWICZ, J., PESCH, E., STERNA, M. & WERNER, F. 2008. Methaheuristic approaches for the two-machine flow-shop problem with weighted late work criterion and common due date. Computers and Operations Research, 35, 574-599.
5
CHEN, B., POTTS, C. N. & WOEGINGER, G. J. 1998. A review of machine scheduling. In: DU, D. Z. & PARDALOS, P. M. (eds.) Handbook of combinatorial optimization. Boston: Kluwer Academic Publishers.
6
ENGELS, D. W., KARGER, D. R., KOLLIOPOULOS, S. G., SENGUPTA, S., UMA, R. N. & WEIN, J. 2003. Techniques for Scheduling with Rejection. Journal of Algorithms, 49, 175-191.
7
FEDERGRUEN, A. & MOSHEIOV, G. 1994. Greedy heuristics for single-machine scheduling problems with general earliness and tardiness costs. Operations Research Letters, 16, 199-208.
8
IBARRA, O. & KIM, C. E. 1875. Fast approximation algorithms for the knapsack and sum of subset problems. problems, Journal of the ACM, 22, 463 468.
9
JI, M. & CHENG, T. C. E. 2010. Batch scheduling of simple linear deteriorating jobs on a single machine to minimize makespan. European Journal of Operational Research, 202, 90-98.
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KACEM, I. & MAHJOUB, A. R. 2009. Fully polynomial time approximation scheme for the weighted ﬂow-time minimization on a single machine with a ﬁxed non-availability interval. Computers and Industrial Engineering, 56, 1708-1712.
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KAHLBACHER, H. G. 1993. Scheduling with monotonous earliness and tardiness penalties. European Journal of Operational Research, 64, 258-277.
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15
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16
MOSLEHI, G. & KIANFAR, K. 2014. Approximation Algorithms and an FPTAS for the Single Machine Problem with Biased Tardiness Penalty. Journal of Applied Mathematics.
17
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19
POTTS, C. N. & VAV WASSENHOVE, L. N. 1992a. Approximation algorithms for schrduling a single machine to minimize total late work. Operations Research Letters, 11, 261-266.
20
POTTS, C. N. & VAV WASSENHOVE, L. N. 1992b. Single machine scheduling to minimize total late work. Operations Research, 40, 586-595.
21
REN, J., ZHANG, Y. & SUN, J. 2009. The NP-hardness of minimizing the total late workk on an unbounded batch machine. Asia-Pacific Journal of Operational Research, 26, 351-363.
22
SHABTAY, D. 2008. Due date assignment and scheduling a single machine with a general earliness/tardiness cost function. Computers and Operations Research, 35, 1539-1545.
23
SHABTAY, D. & BENSOUSSAN, Y. 2010. Maximizing the weighted number of just-in-time jobs in several two-machine scheduling systems. Journal of Scheduling, 15, 39-47.
24
SHABTAY, D., BENSOUSSAN, Y. & KASPI, M. 2012. A bicriteria approach to maximize the weighted number of just-in-time jobs and to minimize the total resource consumption cost in a two-machine ﬂow-shop scheduling system. International Journal of Production Economics, 136, 67-74.
25
SHABTAY, D., GASPAR, N. & KASPI, M. 2013. A survey on offline scheduling with rejection. Journal of Scheduling, 16, 3-28.
26
SLOTNICK, S. A. 2011. Order acceptance and scheduling: A taxonomy and review. European Journal of Operational Research, 212, 1-11.
27
STEINER, G. & ZHANG, R. 2009. Approximation algorithms for minimizing the total weighted number of late jobs with late deliveries in two-level supply chains. Journal of Scheduling, 12, 565-574.
28
STERNA, M. 2000. Problems and algorithms in non-classical shop scheduling, Poznan, Scientific Publishers ofthe Polish Academy of Sciences.
29
STERNA, M. 2006. Late work scheduling in shop systems. Posnan University of Technology.
30
STERNA, M. 2007a. Dominancec relations for two-machine flow-shop problem with late work criterion. Bulletin of the Polish Academy of Sciences, 55, 59-69.
31
STERNA, M. 2007b. Late work minimization in a small flexible manufacturing system. Computers and Industrial Engineering, 52, 210-228.
32
STERNA, M. 2011. A survey of scheduling problems with late work criteria. Omega, 39, 120-129.
33
VENTURA, J. A. & RAHHAKRISHNAN, S. 2003. Single machine scheduling with symmetric earliness and tardiness penalties. European Journal of Operational Research, 144, 598-612.
34
WOEGINGER, G. J. 2000. When does a dynamic programming formulation guarantee the existence of an FPTAS? INFORMS Journal on Computing, 12, 57-74.
35
YUAN, J. 1992. The NP-hardness of the single machine common due date weighted tardiness problem. Systems Science and Mathematical Sciences, 5, 328-333.
36
ZHOU, X. & CAI, X. 1997. General stochastic single-machine scheduling with regular cost functions. Mathematical and Computer Modelling, 26, 95-108.
37
ORIGINAL_ARTICLE
Bayesian Estimation of Change Point in Phase One Risk Adjusted Control Charts
Use of risk adjusted control charts for monitoring patients’ surgical outcomes is now popular.These charts are developed based on considering the patient’s pre-operation risks. Change point detection is a crucial problem in statistical process control (SPC).It helpsthe managers toanalyzeroot causes of out-of-control conditions more effectively. Since the control chart signals do not necessarily indicate the real change point of the process, in this researcha Bayesian estimation methodis applied to find the time and the size of a change in patients’ post-surgery death or survival outcome. The process is monitored in phase Iusing Risk Adjusted Log-likelihood Ratio Test (RALRT) chart,in whichthe logistic regression model is applied to take into accountpre-operation individual risks. Markov Chain Monte Carlo method is applied to obtain the posterior distribution of the change pointmodel including time and size of the change in the Bayesian framework and also to obtain the corresponding credible intervals. Performance evaluations of the Bayesian estimator in comparison with the maximum likelihood estimator (MLE) are conducted by means of different simulation studies. When the magnitude of the change is small, simulation results indicate superiority of the Bayesian estimator over MLE, especially when a more accurate estimation of the change point is of interest.
http://www.jise.ir/article_13929_9196c05dfe49224f3229e63aafb9531e.pdf
2016-04-01T11:23:20
2020-09-26T11:23:20
20
37
Risk Adjusted Control Charts
Change point
Bayesian Estimation
Markov Chain Monte Carlo (MCMC)
Reza
Ghasemi
rghasemi@mail.kntu.ac.ir
true
1
Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran.
Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran.
Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran.
LEAD_AUTHOR
Yasser
Samimi
y_samimi@kntu.ac.ir
true
2
Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran
AUTHOR
Hamid
Shahriari
hshahriari@kntu.ac.ir
true
3
Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N Toosi University of Technology, Tehran, Iran
AUTHOR
Alemi, F. and Sullivan,T. (2001).Tutorial on Risk Adjusted X-Bar Chart: Application to Measurement of Diabetes Control. Quality Management in Healthcare, 9, 57-63.
1
Amiri, A., &Allahyari, S. (2012). Change Point Estimation Methods for Control Chart PostsignalDiagnostics: ALiterature Review. Quality and Reliability Engineering International, 28(7), 673-685.
2
Assareh, H . Smith, I. and Mengersen, K. (2011a). Bayesian Estimation of the Time of a Linear Trend in Risk Adjusted Control Charts. IAENG International Journal of Computer Science, 38, 409-417.
3
Assareh, H . Smith, I. and Mengersen, K. (2011b). Bayesian Change Point Detection in Monitoring Cardiac Surgery Outcomes .Quality Management In Healthcare, 20, 207-222.
4
Assareh, H . Smith, I.andMengersen, K. (2011c). Change Point Detection in Risk Adjusted Control Charts. Statistical Methods in Medical Research, 0, 1-22.
5
Assareh, H. and Mengersen, K. (2011b). Bayesian Estimation of the Time of a Decrease in Risk Adjusted Survival Time Control Charts. International Journal of Applied Mathematics, 41, 360-366.
6
Assareh, H. and Mengersen, K. (2012). Change Point Estimation in Monitoring Survival Time. PLoS ONE. 7,1-7.
7
Assareh, H. and Mengersen, K.(2011a). Detection of the Time of a Step Change in Monitoring Survival Time.Proceedings of the World Congress on Engineering, London, U.K. July 6-8, 1: 1-9.
8
Collins, G. S .Jibawi, A. and McCulloch, P. (2010). Control Chart Methods for Monitoring Surgical Performance: A Case Study from Gastro-Oesophageal Surgery. Journal of Cancer Surgery (EJSO). 37, 473-480.
9
Colosimo, B.M. Castillo, E.D. Editors.(2007). Bayesian Process Monitoring, Control and Optimization.US.CRC Press.47-66.
10
Cook, A.D. Duke, G. Hart, G.K. Pilcher, D. and Mullany, D. (2008). Review of the Application of Risk-Adjusted Charts to Analyse Mortality Outcomes in Critical Care.Critical Care Resuscitation. 10,239-251.
11
Fienberg, S.E. van der Linden, W.J. Editors. Lynch, S.M. (2007). Statistical for Social and Behavioral Science. Section11: Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. USA.Springer Science+ Business Media.105-150.
12
Gombay, E. Hussein, A.A. and Steiner, S.H.(2011). Monitoring Binary Outcomes Using Risk-Adjusted Charts: a Comparative Study.Statistics In Medicine. 30, 2815-2826.
13
Hogg, R.V. Craig, A.T. (2004). Introduction to Mathematical Statistics.Pearson Education. China. 413-420.
14
Jones, M.A. and Steiner, S.H. (2011).Assessing the Effect of Estimation Error on Risk-Adjusted CUSUM Chart Performance.International Journal for Quality in Healthcare.24, 1-6.
15
Matheny, M.E. Machado, L.O. and Resnic, F.S. (2007).Risk-Adjusted Sequential Probability Ratio Test Control Chart Methods for Monitoring Operator and Institutional Mortality Rates in Interventional Cardiology.American Heart Journal. 155, 114-120.
16
Matheny, M.E. Normand, S.L.T. Gross, T.P. Dabic, D.M. Berrios, N.L. Vidi,V.D. Donnely, S. and Resnic, F.S. (2011).Evaluation of an Automated Safety Surveillance System Using Risk-Adjusted Sequential Probability Ratio Testing. BMC Medical Informatics and Decision Making. 11,1-8.
17
Myers, R.H. Montgomery, D.C. Vining, G.G.(2002). Generalized Linear Models.John Wiley & Sons, Inc. New York. 322-328.
18
Paynabar, K. and Jin, J. (2012).Phase I Risk-Adjusted Control Charts for Monitoring Surgical Performance by Considering Categorical Covariates. Journal of Quality Technology. 44, 39-53.
19
Perry, M.B. PignatielloJr, J.J. Simpson, J.R. (2006). Estimating the Change Point of a Poisson Rate Parameter with a Linear Trend Disturbance.Quality and Reliability Engineering International.22,371-384. DOI: 10.1002/qre.715.
20
Sego, L.H. Marion, R. Reynolds, Jr. Woodall, W. (2009).Risk-Adjusted Monitoring of Survival Times.Statistics In Medicine. 28, 1386-1401.
21
Sibaanda, T. and Sibanda ,N. (2007). The CUSUM Chart Method as a Tool for Continuous Monitoring of Clinical Outcomes Using Routinely Collected Data. BMC Medical Research Methodology.7, 1-7.
22
Spiegelhalter, D., Grigg, O., Kinsman, R. and Treasure, T. (2003).Risk-Adjusted Sequential Probability Test. applications to Bristol, Shipman and adult cardiac surgery.International Journal for Quality in Health Care.15, 7-13.
23
Steiner, S.T. and Jones, M. (2009). Risk-Adjusted Survival Time Monitoring with an Updating Exponentially Weighted Moving Average (EWMA) Control Chart. Statistics In Medicine. 29, 444-454.
24
Steiner, S.T. Cook, R.J. Farewell, V.T. and Treasure, T. (2000).Monitoring Surgical Performance Using Risk-Adjusted Cumulative Sun Charts.Biostatistics. 1, 441-452.
25
Tsui, K.L. Goldsman, D. Jiang, W. and Wong, S.Y. (2010).Recent Research in Public Health Surveillance and Health Management.Prognostics & System Health Management Conference.Macau.MU 3059. 0,1-22.
26
Unkel, S. Farrington, P. Garthwaite. P.H. Robertson, C.and Andrews, N. (2011). Statistical Methods for the Prospective Detection of Infectious Disease Outbreaks: A review. Journal of the Royal Statistical Society: Series A (Statistics in Society). 175, 49-82.
27
Woodall, W. (2006).The Use of Control Charts in Health-Care and Public-Health Surveillance.Journal of Quality Technology.38, 89-104.
28
Woodall, W. H., & Montgomery, D. C. (2014).Some current directions in the theory and application of statistical process monitoring. Journal of Quality Technology, 46(1), 78-94.
29
ORIGINAL_ARTICLE
An Insight into the Model Structures Applied in DEA-Based Bank Branch Efficiency Measurements
In this paper, we focus on the Data Envelopment Analysis (DEA)-based model structures have been used in assessing bank branch efficiency. Probing the methodologies of 75 published studies at the branch level since 1985 to early 2015, we found that these models can be divided into four categories: standard basic DEA models, single level and multi-level models, enriched (hybrid) models and special models. Also, summary statistics for DEA applications in bank branches from the perspectives of different measurement approaches adopted by researchers and the frequency of appearing the models of each category in the literature of discussion are derived and presented. The illustrated statistical comparisons show that the popularity of multi-level models than the single level models are on the rise. Furthermore, as a result, we can conclude that from the perspective of performance measurement approaches applied to bank branches, the production approach is more widely used than the others.
http://www.jise.ir/article_13930_12087c9e526dcd148df07baddfa0620f.pdf
2016-04-01T11:23:20
2020-09-26T11:23:20
38
53
Bank branch
Data Envelopment Analysis
Efficiency
model structures
Fatemeh
Rakhshan
rakhshan@mathdep.iust.ac.ir
true
1
Department of Mathematics, Iran University of Science & Technology, Tehran, Iran.
Department of Mathematics, Iran University of Science & Technology, Tehran, Iran.
Department of Mathematics, Iran University of Science & Technology, Tehran, Iran.
LEAD_AUTHOR
Mohammad
Alirezaee
mralirez@iust.ac.ir
true
2
Department of Mathematics, Iran University of Science & Technology, Tehran, Iran
Department of Mathematics, Iran University of Science & Technology, Tehran, Iran
Department of Mathematics, Iran University of Science & Technology, Tehran, Iran
AUTHOR
Maryam
Modirii
m.modirii@gmail.com
true
3
Department of Mathematics, Iran University of Science & Technology, Tehran, Iran
Department of Mathematics, Iran University of Science & Technology, Tehran, Iran
Department of Mathematics, Iran University of Science & Technology, Tehran, Iran
AUTHOR
Majid
Iranmanesh
iranmanesh.majid@gmail.com
true
4
Department of Mathematics, Semnan University, Tehran, Iran
Department of Mathematics, Semnan University, Tehran, Iran
Department of Mathematics, Semnan University, Tehran, Iran
AUTHOR
Ahn, H. & Le, M. H., (2014). An insight into the specification of the input-output set for DEA-based bank branch efficiency measurement. Management Review Quarterly, 64, 3-37.
1
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3
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4
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5
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21
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22
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27
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Giokas, D. I., (1991). Bank branch operating efficiency: A comparative application of DEA and the loglinear model. Omega, 19, 549-557.
29
Giokas, D. I., (2008). Assessing the efficiency in operations of a large Greek bank branch network adopting different economic behaviors. Economic Modeling, 25, 559-574.
30
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33
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34
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Lovell, C. A. K., & Pastor, J. T., (1997). Target setting: an application to a bank branch network. European Journal of Operational Research, 98, 290-299.
36
Manandhar, R., & Tang, J. C. S., (2002). The evaluation of bank branch performance using data envelopment analysis: a framework. Journal of High Technology Management Research, 13. 1-17.
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Paradi, J. C., Vela, S. A., & Zhu, H., (2010). Adjusting for cultural differences, a new DEA model applied to a merged bank. Journal of Productivity Analysis, 33, 109-123.
39
Paradi, J. C., & Zhu, H. (2013). A survey on bank branch efficiency and performance research with data envelopment analysis. Omega, 41, 61-79.
40
Portela, A. S., & Thanassoulis, E., (2005). Profitability of a sample of Portuguese bank branches and its decomposition into technical and allocative components. European Journal of Operational Research, 162, 850-866.
41
Portela, A. S., & Thanassoulis, E., (2010). Malmquist-type indices in the presence of negative data: an application to bank branches. Journal of Banking and Finance, 34, 1472-1483.
42
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Sherman, H. D., & Gold, F., (1985). Bank branch operating efficiency: evaluation with data envelopment analysis. Journal of Banking and Finance, 9, 297-315.
44
Sherman, H. D., & Rupert, T. J., (2006). Do bank mergers have hidden or foregone value? Realized and unrealized operating synergies in one bank merger. European Journal of Operational Research, 168, 253-268.
45
Sherman, H. D., & Zhu, J., (2006). Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: evidence from a U.S. bank application. Annals of Operations Research, 145, 301-319.
46
Shyu, J., & Chiang, T., (2012). Measuring the true managerial efficiency of bank branches in Taiwan: A three-stage DEA analysis. Expert Systems with Applications, 39, 114494-11502.
47
Ševčovič, D., Hlická, M., & Brunovski, P., (2001). DEA analysis for a large structured bank branch network. Central European Journal of Operations Research, 9, 329-342.
48
Sowlati, T., & Paradi, J., (2004). Establishing the ''practical frontier'' in data envelopment analysis. Omega, 32 (4), 261-271.
49
Tone, K., (2001). A slack-based measure of efficiency in data envelopment analysis. European Journal of Operational Research Society, 130, 498-509.
50
Wu, D., Yang, Z., & Liang, L., (2006a). Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert Systems with Applications, 31, 108-115.
51
Wu, D., Yang, Z., & Liang, L., (2006b). Efficiency analysis of cross-region bank branches using fuzzy data envelopment analysis. Expert Applied Mathematics and Computations, 181, 271-281.
52
Yang, C., & Liu, H. M., (2012). Managerial efficiency in Taiwan bank branches: A network DEA. Economic Modeling, 29, 450-461.
53
Yang, Z., & Paradi, J. C., (2006). Cross firm bank branch benchmarking using "handi-capped" data envelopment analysis to adjust for corporate strategic effects. System Sciences, 2, 34b.
54
Yang, C., Wong, B. Y. H., Xu, D. L., Liu, X. B., & Steuer, R. E., (2010). Integrated bank performance assessment and management planning using hybrid minimax reference point -DEA approach. European Journal of Operational Research, 207, 1506-1518.
55
ORIGINAL_ARTICLE
Impact of queuing theory and alternative process routings on machine busy time in a dynamic cellular manufacturing system
A new mathematical model based on the alternative process routings in presence of a queuing system in a dynamic cellular manufacturing system has been proposed in this paper.This model integrates two problems of cell formation and inter-cell layout and also an efficiency factor which is defined for minimizing the cell load variation through the maximizing the busy time for all machine types. In order to evaluate the performance of proposed model, some numerical examples are generated randomly and solved using GAMS optimization software suitable for MIP and MINLP models. The Baron solver which is capable of solving both linear and nonlinear model is implemented. Experimental results verify the applicability of proposed model in every industrial plant which implements a CMS. Moreover, based on the sensitivity analysis, the queue system has significant impact on overall system efficiency. In other words by increasing the part arrival rate the machine busy time is increased strictly.
http://www.jise.ir/article_13931_901da01c3bfcede0ef91eed3c0ae689f.pdf
2016-04-01T11:23:20
2020-09-26T11:23:20
54
66
Queuing Theory
cellular manufacturing system
machine breakdown
Reliability
Saeed
Sadeghi
saeedsadeghi900@gmail.com
true
1
Department of Industrial Engineering, IlamBranch, Islamic Azad university,Ilam, Iran
Department of Industrial Engineering, IlamBranch, Islamic Azad university,Ilam, Iran
Department of Industrial Engineering, IlamBranch, Islamic Azad university,Ilam, Iran
LEAD_AUTHOR
Masoud
Seidi
seidi.masoud@gmail.com
true
2
Faculty of Engineering, Ilam university, Ilam, Iran
Faculty of Engineering, Ilam university, Ilam, Iran
Faculty of Engineering, Ilam university, Ilam, Iran
AUTHOR
Ehsan
Shahbazi
e_shahbazi286@yahoo.com
true
3
Department of Industrial Engineering, Ilam Branch, Islamic Azad university,Ilam, Iran
Department of Industrial Engineering, Ilam Branch, Islamic Azad university,Ilam, Iran
Department of Industrial Engineering, Ilam Branch, Islamic Azad university,Ilam, Iran
AUTHOR
Arani, S. D., & Mehrabad, M. S. (2014). A two stage model for Cell Formation Problem (CFP) considering the inter-cellular movements by AGVs. Journal of Industrial and Systems Engineering, 7(1), 43-55.
1
Arkat, J., Farahani, M. H., & Hosseini, L. (2012). Integrating cell formation with cellular layout and operations scheduling. The International Journal of Advanced Manufacturing Technology, 61(5-8), 637-647.
2
Bagheri, M., & Bashiri, M. (2014a). A hybrid genetic and imperialist competitive algorithm approach to dynamic cellular manufacturing system. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 228(3), 458-470.
3
Bagheri, M., & Bashiri, M. (2014b). A new mathematical model towards the integration of cell formation with operator assignment and inter-cell layout problems in a dynamic environment. Applied Mathematical Modelling, 38(4), 1237-1254.
4
Bashiri, M., & Bagheri, M. (2013). A Two Stage Heuristic Solution Approach for Resource Assignment during a Cell Formation Problem. International Journal of Engineering-Transactions C: Aspects, 26(9), 943.
5
Chung, S.-H., Wu, T.-H., & Chang, C.-C. (2011). An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations. Computers & Industrial Engineering, 60(1), 7-15.
6
Dalfard, V. M. (2013). New mathematical model for problem of dynamic cell formation based on number and average length of intra and intercellular movements. Applied Mathematical Modelling, 37(4), 1884-1896.
7
Defersha, F. M., & Chen, M. (2006). A comprehensive mathematical model for the design of cellular manufacturing systems. International Journal of Production Economics, 103(2), 767-783.
8
Elbenani, B., Ferland, J. A., & Bellemare, J. (2012). Genetic algorithm and large neighbourhood search to solve the cell formation problem. Expert Systems with Applications, 39(3), 2408-2414.
9
Ghezavati, V., & Saidi-Mehrabad, M. (2011). An efficient hybrid self-learning method for stochastic cellular manufacturing problem: A queuing-based analysis. Expert Systems with Applications, 38(3), 1326-1335.
10
Ilić, O. R. (2014). An e-Learning tool considering similarity measures for manufacturing cell formation. Journal of Intelligent Manufacturing, 25(3), 617-628.
11
Saraç, T., & Ozcelik, F. (2012). A genetic algorithm with proper parameters for manufacturing cell formation problems. Journal of Intelligent Manufacturing, 23(4), 1047-1061.
12
Niakan, F., Baboli, A., Moyaux, T., & Botta-Genoulaz, V. (2015). A new multi-objective mathematical model for dynamic cell formation under demand and cost uncertainty considering social criteria. Applied Mathematical Modelling. doi: http://dx.doi.org/10.1016/j.apm.2015.09.047
13
Tavakkoli-Moghaddam, R., Javadian, N., Javadi, B., & Safaei, N. (2007). Design of a facility layout problem in cellular manufacturing systems with stochastic demands. Applied Mathematics and Computation, 184(2), 721-728.
14
Tavakkoli-Moghaddam, R., Minaeian, S., & Rabbani, S. (2008). A new multi-objective model for dynamic cell formation problem with fuzzy parameters. International Journal of Engineering—Transactions A: Basic, 21(2), 159-172.
15
Tavakkoli-Moghaddam, R., Ranjbar-Bourani, M., Amin, G. R., & Siadat, A. (2012). A cell formation problem considering machine utilization and alternative process routes by scatter search. Journal of Intelligent Manufacturing, 23(4), 1127-1139.
16
Tavakoli-Moghadam, R., Javadi, B., Jolai, F., & Mirgorbani, S. (2006). An efficient algorithm to inter and intra-cell layout problems in cellular manufacturing systems with stochastic demands. INTERNATIONAL JOURNAL OF ENGINEERING-MATERIALS AND ENERGY RESEARCH CENTER-, 19(1), 67.
17
Ulutas, B. (2015). Assessing the number of cells for a cell formation problem. IFAC-PapersOnLine, 48(3), 1122-1127. doi: http://dx.doi.org/10.1016/j.ifacol.2015.06.234
18
Wu, X., Chu, C.-H., Wang, Y., & Yue, D. (2007). Genetic algorithms for integrating cell formation with machine layout and scheduling. Computers & Industrial Engineering, 53(2), 277-289.
19
ORIGINAL_ARTICLE
A New Optimization Model for Designing Acceptance Sampling Plan Based on Run Length of Conforming Items
The purpose of this article is to present an optimization model for designing an acceptance sampling plan based on cumulative sum of run length of conforming items. The objective is to minimize the total loss including both the producer and consumer losses. The concept of minimum angle method is applied to consider producer and consumer risks in the optimization model. Also the average number of inspection is considered in the constraint of the model.A practical case study is solved and a sensitivity analysis is performed for elaborating the effect of some important parameters on the objective function. The results of sensitivity analysis showed that the proposed model performance is logical, reliable in all the cases and also has better performance in comparison with classical method in most of the cases. A computational experiment is done to compare the different sampling schemes. The results of computational experiment showed that the proposed model has better performance due to smaller ANI value in all cases.
http://www.jise.ir/article_13932_78cd8f58563fc84df27359fe3fdecde3.pdf
2016-04-01T11:23:20
2020-09-26T11:23:20
67
87
Quality Control
Conforming run length
Acceptance Sampling Plan
Minimum angle method
Taguchi loss function
Mohammad
Fallahnezhad
fallahnezhad@yazd.ac.ir
true
1
Department of Industrial Engineering, yazd university
Department of Industrial Engineering, yazd university
Department of Industrial Engineering, yazd university
LEAD_AUTHOR
Ahmad
Yazdi
ahmad_ahmadi_yazdi@yahoo.com
true
2
Isfahan University of Technology
Isfahan University of Technology
Isfahan University of Technology
AUTHOR
AhmadiYazdi A.,Fallahnezhad M.S. (2014),An optimization model for designing Acceptance Sampling Plan Based on Cumulative Count of Conforming Run Length Using Minimum Angle Method;Hacettepe Journal of Mathematics and Statistics 44(5); 1271-1281.
1
AmirhosseinAmiri, RamezanKhosravi. (2012),Estimating the change point of the cumulative count of a conforming control chart under a drift;ScientiaIranica 19 (3); 856–861.
2
Arizono I., Kanagawa A., Ohta H., WatakabeK., Tateishi K. (1997),Variable sampling plans for normal distribution indexed by Taguchi's loss function;Naval Research Logistics 44(6); 591-603.
3
Aslam M, Fallahnezhad MS, Azam M. (2013),Decision Procedure for the Weibull Distribution based on Run Lengths of Conforming Items;Journal of Testing and Evaluation 41(5); 826-832.
4
Bourke P.D. (2002),A continuous sampling plan using CUSUMs;Journal of Applied Statistics29(8); 1121-1133.
5
Bourke P.D. (2003),A continuous sampling plan using sums of conforming run-lengths;Quality and Reliability Engineering International 19(1);53–66.
6
Bowling, S.R.,Khasawneh, M.T.,Kaewkuekool, S.,Cho B.R. (2004),A Markovian approach to determining optimum process target levels for a multi-stage serial production system, European Journal of Operational Research 159(3); 636-650.
7
Bush N., Leonard E. J., Marchan JR M. Q. M. (1953), A method of discrimination for single and double sampling OC curves utilizing the tangent at the point of inflection;Chemical Corps Engineering Agency, USA.
8
Calvin TW. (1983),Quality control techniques for ‘zero-defects;IEEE Transactions on Components, Hybrids, and Manufacturing Technology 6;323–328.
9
Chen J.T. (2013),Design of cumulative count of conforming charts for high yield processes based on average number of items inspected;International Journal of Quality & Reliability Management 30(9);942 – 957.
10
Elsayed E. A., Chen A. (1994),An economic design of control chart using quadratic loss function; International Journal of Production Research 32(4); 873-887.
11
Fallahnezhad MS, Niaki STA, Abooie MH. (2011),A New Acceptance Sampling Plan Based on Cumulative Sums of Conforming Run-Lengths;Journal of Industrial and Systems Engineering 4(4); 256-264.
12
Fallahnezhad MS, HosseiniNasab H. (2011),Designing a Single Stage Acceptance Sampling Plan based on the control Threshold policy;International Journal of Industrial Engineering & Production Research 22(3); 143-150.
13
Fallahnezhad MS. (2012), A new Markov Chain Based Acceptance Sampling Policy via the Minimum Angle Method; Iranian Journal of Operations Research 3(1); 104-111.
14
Fallahnezhad MS, HosseiniNasab H. (2012),A New Bayesian Acceptance Sampling Plan with Considering Inspection Errors;ScientiaIranica 19(6); 1865-1869.
15
Fallahnezhad M.S.,Fakhrzad M.B. (2012),Determining an Economically Optimal (n,c) Design Via Using Loss Functions,International Journal of Engineering 25(3); 197-201.
16
Fallahnezhad MS, Niaki STA, VahdatZad MA. (2012),A New Acceptance Sampling Design Using Bayesian Modeling and Backwards Induction, International Journal of Engineering 25(1); 45-54.
17
Fallahnezhad M.S., AslamM. (2013),A New Economical Design of Acceptance Sampling Models Using Bayesian Inference;Accreditation and Quality Assurance 18(3); 187-195.
18
Fallahnezhad M.S.,Niaki STA. (2013),A New Acceptance Sampling Policy Based on Number of Successive Conforming Items;Communications in Statistics-Theory and Methods 42(8); 1542-1552.
19
Fallahnezhad MS, Sajjadieh M, Abdollahi P. (2014),An iterative decision rule to minimize cost of acceptance sampling plan in machine replacement problem;International Journal of Engineering 27(7); 1099-1106.
20
Fallahnezhad M.S. Ahmadi E. (2014),Optimal Process Adjustment with Considering Variable Costs for uni-variate and multi-variate Production Process;International Journal of Engineering 27(4); 561–572.
21
Fallahnezhad MS, AhmadiYazdi A. (2015), Comparison between count of cumulative conforming sampling plans and Dodge-Romig single sampling plan; Communications in Statistics-Theory and Methods,In press.
22
Fallahnezhad MS, A. AhmadiYazdi. (2015),Economic design of Acceptance Sampling Plans based on Conforming Run Lengths using Loss Functions;Journal of Testing and Evaluation 44(1); 1-8,
23
Ferrell W.G., Chhoker Jr.A. (2002), Design of economically optimal acceptance sampling plans with inspection error; Computers & Operations Research 29(10); 1283-1300.
24
Goh T. N. (1987),A control chart for very high yield processes;Quality Assurance 13; 18–22.
25
Kobayashia J.,Arizonoa I., Takemotoa Y. (2003),Economical operation of control chart indexed by Taguchi's loss function;International Journal of Production Research 41(6); 1115-1132.
26
Kuralmani V., Xie M., Goh T. N., Gan F. F. (2001),A conditional decision procedure for high yield processes;IIE Transactions 34; 1021–1030.
27
Ling-Yau Chan, ShaominWub. (2009),Optimal design for inspection and maintenance policy based on the CCC chart;Computers & Industrial Engineering 57(3); 667–676.
28
Min Zhang, GuohuaNie, Zhen He. (2014),Performance of cumulative count of conforming chart of variable sampling intervalswith estimated control limits;International Journal of Production Economics 150;114–124.
29
Mirabi M, Fallahnezhad MS. (2012),Analyzing Acceptance Sampling Plan by Markov Chains;South African Journal of Industrial Engineering 23(1);151-161.
30
Moskowitz H., Tang K. (1992), Bayesian variables acceptance-sampling plans: quadratic loss function and step loss function; Technometrics 34(3); 340-347.
31
Noorossana R., Saghaei A., Paynabar K., Samimi Y. (2007), On the conditional decision procedure for high yield processes;Computers and Industrial Engineering 53(3); 469–477.
32
Sotiris Bersimis, Markos V. Koutras, Petros E. Maravelakis. (2013),A compound control chart for monitoring and controlling high quality processes;European Journal of Operational Research 233(3); 595–603.
33
Soundararajan V, Christina AL. (1997), Selection of single sampling variables plans based on the minimum angle;Journal of Applied Statistics 24(2); 207-218.
34
Taguchi G. Chowdhury S. Wu Y. (2005),Taguchi's Quality Engineering Handbook. Quality Loss Function; John Wiley & Sons 171 -198.
35
Wu Z.,Shamsuzzamana M., Panb E.S. (2004),Optimization design of control charts based on Taguchi's loss function and random process shifts;International Journal of Production Research 42(2); 379-390.
36
Xie M., Goh T. N. (1992), Some procedures for decision making in controlling high yield processes;Quality and Reliability Engineering International 8;355–360.
37
Yan-Kwang Chen. (2013), Cumulative conformance count charts with variable sampling intervals for correlated samples;Computers & Industrial Engineering 64(1); 302–308.
38
Zhang M., Peng Y.M., SchuhA., Megahed F.M., Woodall W.H. (2013),Geometric charts with estimated control limits;Quality and Reliability Engineering International 29 (2); 209–223.
39
Zhang Wua, ZhaoJun Wang, Wei Jiang. (2010),A generalized Conforming Run Length control chart for monitoring the mean of a variable;Computers & Industrial Engineering 59(2); 185–192.
40
ORIGINAL_ARTICLE
Three New Heuristic Algorithms For The Fleet Size And Mix Green Vehicle Routing Problem
In recent years, great efforts have been made to reduce greenhouse gas emissions by vehicles. Petroleum products produces greenhouse gas emissions, therefore reducing the use of these products can make a major contribution to reducing pollution. The Fleet Size and Mix Vehicle Routing Problem is one of the most widely used routing branches. In this problem, there are vehicle with different capacities and there is the possibility of choosing vehicles of different types. In this paper, Fleet Size and Mix Vehicle Routing Problem is death considering the reduction of fuel consumption. Since this problem is NP-hard, three novel heuristic methods entitled GROS-I,GROS-II, GGT are presented for the problem. In order to evaluate the proposed heuristics a number of small, medium and large problems are solved. The results show that proposed algorithms have good performance.
http://www.jise.ir/article_13933_38f53e7b611b50bd2bcf7916c7477e2e.pdf
2016-04-01T11:23:20
2020-09-26T11:23:20
88
101
Mix vehicle routing
Reduction of fuel consumption
heuristics
Green Vehicle Routing Problem
Mahdi
Alinaghian
alinaghian@cc.iut.ac.ir
true
1
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
LEAD_AUTHOR
Mohsen
Zamani
m.zamani9978@gmail.com
true
2
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
AUTHOR
Clarke, G.u., and Wright, J.W. (1964). Scheduling of vehicles from a central depot to a number of delivery points. Operations research 12, 568-581.
1
Dantzig, G.B., and Ramser, J.H. (1959). The truck dispatching problem. Management science 6, 80-91.
2
Erdoğan, S., and Miller-Hooks, E. (2012). A green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review 48, 100-114.
3
Fagerholt, K., Laporte, G., and Norstad, I. (2010). Reducing fuel emissions by optimizing speed on shipping routes. Journal of the Operational Research Society 61, 523-529.
4
Faulin, J., Juan, A., Lera, F., and Grasman, S. (2011). Solving the capacitated vehicle routing problem with environmental criteria based on real estimations in road transportation: a case study. Procedia-Social and Behavioral Sciences 20, 323-334.
5
Felipe, Á., Ortuño, M.T., Righini, G., and Tirado, G. (2014). A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transportation Research Part E: Logistics and Transportation Review 71, 111-128.
6
Figliozzi, M.A. (2011). The impacts of congestion on time-definitive urban freight distribution networks CO2 emission levels: Results from a case study in Portland, Oregon. Transportation Research Part C: Emerging Technologies 19, 766-778.
7
Golden, B., Assad, A., Levy, L., and Gheysens, F. (1984). The fleet size and mix vehicle routing problem. Computers & Operations Research 11, 49-66.
8
Kara, İ., Kara, B., and Yetis, M.K. (2007). Energy Minimizing Vehicle Routing Problem. In Combinatorial Optimization and Applications, A. Dress, Y. Xu, and B. Zhu, eds. (Springer Berlin Heidelberg), pp. 62-71.
9
Kirby, H.R., Hutton, B., McQuaid, R.W., Raeside, R., and Zhang, X. (2000). Modelling the effects of transport policy levers on fuel efficiency and national fuel consumption. Transportation Research Part D: Transport and Environment 5, 265-282.
10
Koç, Ç., Bektaş, T., Jabali, O., and Laporte, G. (2014). The fleet size and mix pollution-routing problem. Transportation Research Part B: Methodological 70, 239-254.
11
Kopfer, H., and Kopfer, H. (2013). Emissions Minimization Vehicle Routing Problem in Dependence of Different Vehicle Classes. In Dynamics in Logistics, H.-J. Kreowski, B. Scholz-Reiter, and K.-D. Thoben, eds. (Springer Berlin Heidelberg), pp. 49-58.
12
Kucukoglu, I., Ene, S., Aksoy, A., and Ozturk, N. (2013). a green capacitated vehicle routing problem with fuel consumption optimization model International Journal of Computational Engineering Research 3, 16-23.
13
Kwon, Y.-J., Choi, Y.-J., and Lee, D.-H. (2013). Heterogeneous fixed fleet vehicle routing considering carbon emission. Transportation Research Part D: Transport and Environment 23, 81-89.
14
Lin, C., Choy, K.L., Ho, G.T., Chung, S., and Lam, H. (2014). Survey of green vehicle routing problem: Past and future trends. Expert Systems with Applications 41, 1118-1138.
15
Omidvar, A., and Tavakkoli-Moghaddam, R (2012). Sustainable vehicle routing: Strategies forcongestion management and refueling scheduling. In Energy Conference and Exhibition
16
(ENERGYCON), Florence, Italy, 1089–1094.
17
Palmer, A. (2007). The development of an integrated routing and carbon dioxide emissions model for goods vehicles.
18
Saberi, M.a.V., İ. (2012). Continuous Approximation Model for the Vehicle Routing Problem for Emissions Minimization at the Strategic Level. Journal of Transportation Engineering 138, 1368-1376.
19
Shao, S., and Huang, G.Q. (2014). A SHIP Inventory Routing Problem with Heterogeneous Vehicles under Order-Up-To Level Policies. In IIE Annual Conference. Proceedings (Institute of Industrial Engineers-Publisher), p. 1106.
20
Ubeda, S., Arcelus, F., and Faulin, J. (2011). Green logistics at Eroski: A case study. International Journal of Production Economics 131, 44-51.
21
Yong Peng, X.W. (Apr. 11, 2009 to Apr. 12, 2009). “Research on a Vehicle Routing Schedule to Reduce Fuel
22
Consumption”. 2014 Sixth International Conference on Measuring Technology and Mechatronics Automation 3.
23
ORIGINAL_ARTICLE
Competitive Vehicle Routing Problem with Time Windows and Stochastic Demands
The competitive vehicle routing problem is one of the important issues in transportation area. In this paper a new method for competitive VRP with time windows and stochastic demand is introduced. In the presented method a three time bounds are given and the probability of arrival time between each time bound is assumed to be uniform. The demands of each customer are different in each time window. Therefore, revenue given in each time window is different. In this paper a project with two companies in a city with eight customers is considered and the best routing with maximum revenue is obtained.
http://www.jise.ir/article_13906_fc9ae0271180af0b360a0c11e57a6968.pdf
2016-04-01T11:23:20
2020-09-26T11:23:20
102
112
Competitive VRP
Time window
stochastic demand
mathematical model
Amir
Shojaie
amir@ashojaie.com
true
1
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
LEAD_AUTHOR
Mohammad
Shariatmadaria
m.shariat.62@gmail.com
true
2
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
AUTHOR
Mojtaba
Moradia
moradi6465@yahoo.com
true
3
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
School of Industrial Engineering, Islamic Azad University, South Tehran Branch, Tehran, Iran
AUTHOR
Bent R, Van Hentenryck P (2004) Scenario-based planning for partially dynamic vehicle routing with stochastic customers. Operations Research 52(6):977–987
1
Bianchi L, Birattari M, Chiarandini M, Manfrin M, Mastrolilli M, Paquete L, Rossi-Doria O, Schiavinotto T (2004) Metaheuristics for the vehicle routing problem with stochastic demands. In: Parallel Problem Solving from Nature - PPSN VIII, Lecture Notes in Computer Science, Springer Berlin, Heidelberg, pp 450–460
2
Braysy O, Gendreau M. (2005) Vehicle routing problem with time windows, part I: route construction and local search algorithms. Transportation Science;39:104–18.
3
Cordeau JF, Desaulniers G, Desrosiers J, Solomon MM, Soumis F, (2002) The VRP with time windows. In: Toth P, Vigo D, editors. The vehicle routing problem, SIAM Monographs on Discrete Mathematics and Applications, Vol. 9, Philadelphia, PA; p. 157–194.
4
Geiger MJ. Multi-criteria und Fuzzy Systeme in Theorie und Praxis. In: A computational study of genetic crossover operators for multi-objective vehicle routing problem with soft time windows. Deutscher Universities-Verlag; 2003. p. 191–207.
5
Golden B, Laporte G, Taillard E. (1999) An adaptive memory heuristic for a class of vehicle routing problems with minmax objective. Computers and Operations Research;24:445–52.
6
Haugland D, Ho S, Laporte G (2007) Designing delivery districts for the vehicle routing problem with stochastic demands. European Journal of Operational Research 180(3):997–1010
7
J.E. Mendoza, B. Castaniera, C. Guereta, A.L. Medagliab, N. Velascob, (2010) A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands, Computers and Operations Research 37, 1886–1898.
8
Laporte G, Louveaux F, Van Hamme L (2002) An integer L-shaped algorithm for the capacitated vehicle routing problem with stochastic demands. Operations Research 50(3):415–423
9
Marinakis, Y. and M. Marinaki, (2013). Particle Swarm Optimization with Expanding Neighborhood Topology for the Permutation Flowshop Scheduling Problem, Soft Computing, (accepted), DOI:10.1007/s00500-013-0992-z
10
Mendoza JE, Castanier B, Gu´eret C, Medaglia AL, Velasco N (2010) A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands. Computers & Operations Research 37(11):1886–1898
11
Mendoza JE, Castanier B, Gu´eret C, Medaglia AL, Velasco N (2011) Constructive heuristics for the multicompartment vehicle routing problem with stochastic demands. Transportation Science 45(3):335–345
12
Qureshi AG, Taniguchi E, Yamada T. (2009E) An exact solution approach for vehicle routing and scheduling problems with soft time windows. Transportation Research;45(9), 60–77.
13
Secomandi N, Margot F (2009) Reoptimization approaches for the vehicle-routing problem with stochastic demands. Operations Research 57(1):214–230
14
Solomon MM, Desrosiers J. (1988) Time window constrained routing and scheduling problems. Transportation Science; 22(1):1–13.
15
Solomon MM. (1987) Algorithms for the vehicle routing and scheduling problem with time windows constraints. Operations Research; 35:254– 65.
16
Taillard ED. (1999) A heuristic column generation method for the heterogeneous fleet VRP. Rairo OR;33(1):1–14.
17
Tavakkoli-Moghaddam R, Saremi AR, Ziaee MS. (2006) A memetic algorithm for a vehicle routing problem with backhauls. Applied Mathematics and Computation;181:1049–60.
18
Tavakkoli-Moghaddam R, Safaei N, Gholipour Y. (2006) A hybrid simulated annealing for capacitated vehicle routing problems with the independent route length. Applied Mathematics and Computation;176:445–54.
19
Yang, W. H., Mathur, K., Ballou, R. H. (2000). Stochastic vehicle routing problem with restocking, Transportation Science, 34, 99-112.
20