ORIGINAL_ARTICLE
Reliability based budgeting with the case study of TV broadcast
Planning budget will help to identify wasteful expenditures, adapt financial situation changes quickly, and achieve financial goals. The reliability based budgeting has a great importance for broadcasting industry. In this study, several kinds of failure modes in TV broadcasting system have been detected based on recorded data. The risk priority number is used, for prioritizing the risks that are related to the reliability. We presented a multi-criteria decision making by analytic hierarchy process that has been used for prioritizing the proposed improvement options subject to budget requirement. The results indicate that human factor has more importance in the reliability of the system of TV broadcast that can be improved by education.
http://www.jise.ir/article_44915_0c1134f203ff39be60279048c3d341b8.pdf
2017-08-11T11:23:20
2020-01-28T11:23:20
1
15
Failure modes and Effects Analysis
Analytic Hierarchy Process
Risk priority number
Reliability
TV broadcasting system
Omran
Mohammadi
e_mohammadi@iust.ac.ir
true
1
Iran university of science and technology
Iran university of science and technology
Iran university of science and technology
LEAD_AUTHOR
Alireza
Poursaber
poursaber_a@yahoo.com
true
2
Iran University of science and technology
Iran University of science and technology
Iran University of science and technology
AUTHOR
Mohammad
Yavari
m.yavari@qom.ac.ir
true
3
University of Qom, Qom, Iran
University of Qom, Qom, Iran
University of Qom, Qom, Iran
AUTHOR
Aminbakhsh, S., Gunduz, M., & Sonmez, R. (2013). Safety risk assessment using analytic hierarchy process (AHP) during planning and budgeting of construction projects. Journal of Safety Research, 99-105.
1
Chin, K., & Tummalo, R. (1999). An evaluation of success factors using the AHP to implement ISO 14001-based EMS. 16(4), 341-62.
2
Esra, B. (2011). A capital budgeting problem for preventing workplace mobbing by using analytic hierarchy process and fuzzy 0–1 bidimensional knapsack model. Expert Systems with Applications 38, 12415-12422.
3
Hajshirmohammadi, A. (1383). Maintenance Planing. Isfahan: Ghazal.
4
Hajshirmohammadi, A., & Wedley, W. c. (2004). Maintenance management- an AHP application for centralization/decentralization. 10(1), 16-25.
5
Liu, H. C., Liu, L., & Liu, N. (2013). Risk evaluation approaches in failure mode and effects analysis. Expert Systems with Applications, 40, 828-838.
6
Moubray, J. (1389). Reliability Centered Maintenance. (A. Zavashkiani, & R. Azadegaan, Trans.) tehran: ariana ghalam.
7
Murat, M., & Shahriar, Z. (1991). Capital Budgeting in hospital management using the analytic hierarchy process. Socio-Econ Plann Sci. Vol 25. No 1, 27-34.
8
Partovi, Y. (1994). Determining what to benchmark: an analytic hieraarchy process approach. 14(6), 25-39.
9
Robert, R., & Thomas, R. (1994). An Integrating the Analytic Hierarchy Process (AHP) into the Multi objective budgeting models of public sector organizations. Socio-Ectm, Plann. Sci. Vol. 28. No. 3. Printed in Great Britain., 197-206.
10
Saaty, T. (1980). The Analytic Hierarchy Process. McGraw-Hill, New York.
11
Saaty, T. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process. ISBN 0-9620317-9-8, RWS.
12
Saaty, T. L., & Alexander, J. M. (1989). Conflict Resolution: Analytic Hierarchy Process. New York: praeger.
13
Saliminamin, M. (1370). Maintenance and reliability strategies. Tehran: Amirkabir technology university.
14
scimago. (2017, 2 15). Retrieved from VIZ TOOLS: http://www.scimagojr.com
15
Scopuse. (2017, 2 15). Retrieved from Analyse search results: www.scopus.com
16
Sun, C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert systems with applications, 7745-7754.
17
Vaidya, O., & Kumar, S. (2006). EuropeanJournal of Operational Research, 1-29.
18
Yang, c., & Chen, B. (2004). Key quality performance evaluation using fuzzy AHP. Journal of the Chinese Institute of Industrial Engineers, 543-550.
19
Zaim, S., Turkyilmaz, A., Acar, M. F., & Demirel, O. F. (2012). Maintenance strategy selection using AHP and ANP algorithms: a case study. Jurnal of Quality in Maintenance Engineering, 18(1), 16-29.
20
ORIGINAL_ARTICLE
Two-stage fuzzy-stochastic programming for parallel machine scheduling problem with machine deterioration and operator learning effect
This paper deals with the determination of machine numbers and production schedules in manufacturing environments. In this line, a two-stage fuzzy stochastic programming model is discussed with fuzzy processing times where both deterioration and learning effects are evaluated simultaneously. The first stage focuses on the type and number of machines in order to minimize the total costs associated with the machine purchase. Based on the made decisions, the second stage aims to schedule orders, while the objective is to minimize total tardiness costs. A dependent-chance programming (DCP) approach is used for the defuzzification of the proposed model. As the resulted formulation is a NP-hard problem, a branch and bound (B&B) algorithm with effective lower bound is developed. Moreover, a genetic algorithm (GA) is proposed to solve problems of large-sizes. The computational results reveal the high efficiency of the proposed methods, in particular the GA, to solve problems of large sizes.
http://www.jise.ir/article_44916_6a92e19e61251bd2d6f58c8461010957.pdf
2017-08-31T11:23:20
2020-01-28T11:23:20
16
32
scheduling
Design of production systems
Fuzzy methods
Integer programming
Learning
Meta-heuristics
Armin
Jabarzadeh
arminj@iust.ac.ir
true
1
Iran University of Science and Technology
Iran University of Science and Technology
Iran University of Science and Technology
LEAD_AUTHOR
Mohammad
Rostami
rostami_m@ind.iust.ac.ir
true
2
Iran University of Science and Technology
Iran University of Science and Technology
Iran University of Science and Technology
AUTHOR
Mahdi
Shahin
mahdi_shahin@ind.iust.ac.ir
true
3
Iran University of Science and Technology
Iran University of Science and Technology
Iran University of Science and Technology
AUTHOR
Kamran
Shahanaghi
shahanaghi@iust.ac.ir
true
4
Iran university of science and technology
Iran university of science and technology
Iran university of science and technology
AUTHOR
Al-Khamis, T. & R. M’Hallah (2011) A two-stage stochastic programming model for the parallel machine scheduling problem with machine capacity. Computers & Operations Research, 38, 1747–1759.
1
Baptiste, P., J. Carlier, A. Kononov, M. Queyranne, S. Sevastyanov & M. Sviridenko (2012) Integer preemptive scheduling on parallel machines. Operations Research Letters, 40, 440–444.
2
Bozorgirad, M. A. & R. Logendran (2012) Sequence-dependent group scheduling problem on unrelated-parallel machines. Expert Systems with Applications, 39, 9021–9030.
3
Fanjul-Peyro, L., F. Perea & R. Ruiz (2017) MIP models and matheuristics for the unrelated parallel machine scheduling problem with additional resources. European Journal of Operational Research.
4
Gerstl, E. & G. Mosheiov (2012) Scheduling on parallel identical machines with job-rejection and position-dependent processing times. Information Processing Letters, 112, 743–747.
5
Gharehgozli, A. H., R. Tavakkoli-Moghaddam & N. Zaerpour (2009) A fuzzy-mixed-integer goal programming model for a parallel-machine scheduling problem with sequence-dependent setup times and release dates. Robotics and Computer-Integrated Manufacturing, 25, 853–859.
6
Ji, M. & T. C. E. Cheng (2008) Parallel-machine scheduling with simple linear deterioration to minimize total completion time. European Journal of Operational Research 188, 342–347.
7
Joo, C. M. & B. S. Kim (2015) Hybrid genetic algorithms with dispatching rules for unrelated parallel machine scheduling with setup time and production availability. Computers & Industrial Engineering, 85, 102-109.
8
Kondakci, S., Ö. Kirca & M. Azizoǧlu (1994) An efficient algorithm for the single machine tardiness problem. International journal of production economics, 36, 213-219.
9
Lee, K. H. 2006. First course on fuzzy theory and applications. Springer.
10
Lee, W.-C., M.-C. Chuang & W.-C. Yeh (2012) Uniform parallel-machine scheduling to minimize makespan with position-based learning curves. Computers & Industrial Engineering, 63, 813–818.
11
Liao, L. W. & G. J. Sheen (2008) Parallel Machine Scheduling with Machine Availability and Eligibility Constraints. European Journal of Operation Research, 184, 458-467.
12
Liu, B. & Y.-K. Liu (2002) Expected value of fuzzy variable and fuzzy expected value models. IEEE Transactions on Fuzzy Systems, 10, 445–450.
13
Liu, M. (2013) Parallel-machine scheduling with past-sequence-dependent delivery times and learning effect. Applied Mathematical Modelling, 37, 9630–9633.
14
Liu, M., F. Zheng, S. Wang & Y. Xu (2013) Approximation algorithms for parallel machine scheduling with linear deterioration. Theoretical Computer Science, 497, 108–111.
15
Mazdeh, M. M., F. Zaerpour, A. Zareei & A. Hajinezhad (2010) Parallel machines scheduling to minimize job tardiness and machine deteriorating cost with deteriorating jobs. Applied Mathematical Modelling, 34, 1498–1510.
16
Mosheiov, G. (2001) Scheduling problems with a learning effect. European Journal of Operation Research, 132, 687-693.
17
Peng, J. & B. Liu (2004) Parallel machine scheduling models with fuzzy processing times. Information Sciences, 166, 49–66.
18
Prot, D., O. Bellenguez-Morineau & C. Lahlou (2013) New complexity results for parallel identical machine scheduling problems with preemption, release dates and regular criteria. European Journal of Operational Research, 231, 282–287.
19
Rodriguez, F. J., M. Lozano, C. Blum & C. Garcı´a-Martı´nez (2013) An iterated greedy algorithm for the large-scale unrelated parallel machines scheduling problem. Computers & Operations Research, 40, 1829–1841.
20
Rostami, M., A. E. Pilerood & M. M. Mazdeh (2015) Multi-objective parallel machine scheduling problem with job deterioration and learning effect under fuzzy environment. Computers & Industrial Engineering, 85, 206-215.
21
Ruiz-Torres, A. J., G. Paletta & E. Pe´rez (2013) Parallel machine scheduling to minimize the makespan with sequence dependent deteriorating effects. Computers & Operations Research, 40, 2051–2061.
22
Sarıçiçek, I. & C. Çelik (2011) Two meta-heuristics for parallel machine scheduling with job splitting to minimize total tardiness. Applied Mathematical Modelling, 35, 4117–4126.
23
Shen, L., D. Wang & X.-Y. Wang (2013) Parallel-machine scheduling with non-simultaneous machine available time. Applied Mathematical Modelling, 37, 5227–5232.
24
Tian, W. & C. S. Yeo (2015) Minimizing total busy time in offline parallel scheduling with application to energy efficiency in cloud computing. Concurrency and Computation: Practice and Experience, 27, 2470-2488.
25
Toksari, M. D. & E. G¨uner (2009) Parallel machine earliness/tardiness scheduling problem under the effects of position based learning and linear/nonlinear deterioration. Computers & Operations Research, 36, 2394 -- 2417.
26
Unlu, Y. & S. J. Mason (2010) Evaluation of mixed integer programming formulations for non-preemptive parallel machine scheduling problems. Computers & Industrial Engineering, 58, 785–800.
27
Vallada, E. & R. Ruiz (2011) A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times. European Journal of Operational Research, 211, 612–622.
28
Yeh, W.-C., P.-J. Lai, W.-C. Lee & M.-C. Chuang (2013) Parallel-machine scheduling to minimize makespan with fuzzy processing times and learning effects. Information Sciences.
29
Zadeh, L. A. (1965) Fuzzy sets. Inf. Control, 8, 338–353.
30
Zhang, M. & C. Luo (2013) Parallel-machine scheduling with deteriorating jobs, rejection and a ﬁxed non-availability interval. Applied Mathematics and Computation, 224, 405–411.
31
Zhu, H. & J. Zhang (2009) A credibility-based fuzzy programming model for APP problem. In International conference on artiﬁcial intelligence and computational intelligence.
32
ORIGINAL_ARTICLE
Sustainability in paper industry closed-loop supply chain (case study: East Azerbaijan province, Iran)
Governments and customers are forcing the paper manufacturers to become more sustainable. Accordingly, there still exists a gap in the quantitative modeling of these issues. In this paper, this gap is covered through simultaneously considering economical, environmental and social impacts in the paper closed-loop supply chain network design. The proposed multi-objective, multi-echelon, multi-product and single-period model is composed of suppliers, plants, regional wholesalers, retailers, customer zones, collection sites, centralized collection points, recycling facilities, energy recovery and disposal centers.The objectives considered are minimization of total cost; environmental benefit through maximizing coverage of collected waste paper by opened centralized collection centers; and maximization of the social impact of the network in a way that would prefer the location of facilities in the less populated regions.The proposed model is applied to an illustrative example designed utilizing real data of the paper industry in East Azerbaijan of Iran and interactive fuzzy goal programming approach is used to solve the developed model. Sensitivity analysis of the proposed model is also performed by considering key parameters.
http://www.jise.ir/article_44917_a8748d16c84e88f59bc31c80fe805ea6.pdf
2017-09-18T11:23:20
2020-01-28T11:23:20
33
49
Closed-loop supply chain
Multi-objective Programming
location model
paper recovery
Arezoo
Rahmani-Ahranjani
arezoorahmani@ymail.com
true
1
Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran
Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran
Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran
AUTHOR
Ali
Bozorgi-Amiri
alibozorgi@ut.ac.ir
true
2
School of Industrial Engineering, College of Engineering, University of Tehran,corresponding author
School of Industrial Engineering, College of Engineering, University of Tehran,corresponding author
School of Industrial Engineering, College of Engineering, University of Tehran,corresponding author
LEAD_AUTHOR
Mehdi
Seifbarghy
m.seifbarghy@alzahra.ac.ir
true
3
Department of Industrial Engineering, Alzahra University, Tehran, Iran
Department of Industrial Engineering, Alzahra University, Tehran, Iran
Department of Industrial Engineering, Alzahra University, Tehran, Iran
AUTHOR
Esmaeil
Najafi
najafi1515@yahoo.com
true
4
Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran
Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran
Department of Industrial Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran
AUTHOR
Ageron, B.,Gunasekaran, A., &Spalanzani, A. (2011). Sustainable Supply Management: An Empirical Study. International Journal of Production Economics, 140 (1), 168-182.
1
Amin, S.H.,& Zhang, G. (2013). A multi-objective facility location model for closed loop supply chain networkunder uncertain demand and return.Appl. Math. Model, 37, 4165–4176.
2
Bai, C., &Sarkis, J., (2010). Integrating sustainability into supplier selection with grey system and rough set methodologies. International Journal of Production Economics, 124 (1), 252-264.
3
Cardoso, S.R., Barbosa-Póvoa, A.P.F., &Relvas, S. (2013). Design and planning of supply chains withintegrationofreverse logistics activities under demand uncertainty.European Journal of Operational Research, 226,436-451.
4
Cruz, J., &Matsypura, D. (2009). Supply chain networks with corporate social responsibility through integrated environmental decision-making. International Journal of Production Research, 47 (3), 621-648.
5
Chaabane, A., Ramudhin, A., &Paquet, M.( 2012). Design of sustainable supply chains under the emission trading scheme. International Journal of Production Economics, 135 (1), 37-49.
6
Davari S., Zarandi M.H.F.,&Hemmati A. (2011). Maximal covering location problem (MCLP) with fuzzy travel times. ExpSystAppl,38,14535–41.
7
Dehghanian, F. & Mansour, S. (2009). Designing sustainable recovery network of end-of-life products usinggenetic algorithm.Resources, Conservation andRecycling, 53(10), 559–570.
8
Devika, K., Jafarian, A., &Nourbakhsh, V. (2014). Designing a sustainable closed-loop supply chainnetwork based on triple bottom line approach: A comparison of metaheuristics hybridizationtechniques.European Journal of Operational Research, 235, 594-615.
9
Erol, I., Sencer, S., &Sari, R. (2011). A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain,Ecological Economics, 70(6), 1088-1100.
10
Esfahbodi, A. Zhang Y., &Watson, G. (2016). Sustainable supply chain management in emerging economies: Trade-offs between environmental and cost performance.International Journal of Production Economics, 181, 350-366.
11
Ferrao P., Ribeiro, P., &Silva P.A. (2008). Management system for end-of-life tyres: A Portuguese case study.Waste Management, 28, 604-614.
12
Fonseca, M. C., García-Sánchez, Á., Ortega-Mier, M., &Saldanha-da-Gama, F. (2010). A stochastic biobjective location model for strategic reverse logistics. Top, 18(1), 158–184.
13
Garg, K., Kannan, D., Diabat, A., &Jha, P.C. (2015). A Multi-criteria optimization approach to manageenvironmental issues in closed loop supply chain network design. Journal of Cleaner Production, 100, 297-314
14
Govindan, K. Soleimani, H. & Kannan, D. (2015a). Reverse logistics and closed-loop supply chain: A comprehensivereview to explore the future.European Journal of Operational Research, 240 (3), 603–626.
15
Govindan, K., Jafarian, A., Nourbakhsh (2015b). Bi-objective integrating sustainable order allocation and sustainable supply chain networkstrategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic.Computers & Operations Research,62, 112-130.
16
Govindan, K., Jafarian, A., Khodaverdi, R., &Devika K. (2014). Two-echelon multiple vehicle location-routing problem with time windows for optimization of sustainable supply chain network of perishable food.International Journal of Production Economics, 152, 9-28
17
Kannan, D., Diabat, A., Alrefaei, M., Govindan, K.,& Yong, G. (2012). A carbon footprint based reverselogistics network design model. Resources, Conservation and Recycling, 67, 75–79.
18
Krikke, H., Bloemhof-Ruwaard, J., &Van Wassenhove, L.N. (2003) .Concurrent product and closed-loop supplychain design with an application to refrigerators.International Journal ofProduction Research, 41(16), 3689-3719.
19
Linton, J.D., Klassen, R., &Jayaraman, V. (2007). Sustainable supply chains: An introduction. Journal of Operations Management, 25 (6), 1075-1082.
20
Pishvaee, M. S., Razmi, J., &Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets and Systems, 206(0), 1–20.
21
Raut, R.D., Narkhede, B., &Gardas, B.B. (2017).To identify the critical success factors of sustainable supply chain management practices in the context of oil and gas industries: ISM approach.Renewable and Sustainable Energy Reviews, 68 (1),33-47.
22
Seuring, S. (2013). A review of modeling approaches for sustainable supply chain management. Decision Support Systems, 54 (4), 1513-1520.
23
Seuring, S. & Müller, M. (2008).From a literature review to a conceptual framework for sustainable supplychainmanagement.Journal of Cleaner Production, 16(15),1699–1710.
24
Sgarbossa, F., & Russo, I. (2017). A proactive model in sustainable food supply chain: Insight from a case study. International Journal of Production Economics, 183(B), 596-606.
25
Tseng, M. L., Tan, R.R., &Siriban-Manalang, A.B. (2012). Sustainable consumption and production for Asia: sustainability through green design and practice. Journal of Cleaner Production,40,1-5.
26
Teuteberg, F., &Wittstruck, D. (2010). A Systematic Review of Sustainable Supply Chain Management, MultikonferenzWirtschaftsinformatik2010. 203.
27
ORIGINAL_ARTICLE
A two-stage pricing and inventory optimization model for both ameliorating and deteriorating items in a competing environment
This paper develops a pricing and inventory model of a supply chain composing one supplier and two manufacturers in which the supplier sells its product to two competing manufacturers. Each manufacturer faces a deterministic demand that depends on his/her own and the other competitor sale price. Selling price for the supplier is time dependent and increment function.Both amelioration and deterioration effects are seen simultaneously for this model. In this paper, it is assumed that shortages are not allowed in whichthe objective ismaximizing the total profit of supplier and manufacturers in two stages. In first stage, the total profit of supplier is maximized and then in the second stage, two manufacturers maximize their total profits with regard to the optimal decision variables obtained in the first stage. A case study of trout fish breeding is presented andnumerical example is solved by Mathematica optimization tool. Sensitivity analysis is finally conducted to study the effect of various parameters on the optimal solution.
http://www.jise.ir/article_44918_9ca549e8f0e89a7455fabd0cadaa3c0b.pdf
2017-09-21T11:23:20
2020-01-28T11:23:20
50
71
pricing
inventory model
Deteriorating and ameliorating items
Competing environment
Bertrand game
Mohammadmahdi
Malekitabar
m_malekitabar@iust.ac.ir
true
1
Iran University Science & Technology
Iran University Science & Technology
Iran University Science & Technology
AUTHOR
Saeed
Yaghoubi
yaghoubi@iust.ac.ir
true
2
Iran university of science and technology
Iran university of science and technology
Iran university of science and technology
LEAD_AUTHOR
Abad, P.L., (1996). Optimal pricing and lot-sizing under conditions of perishability and partial backordering. Management Science, 42(8), pp.1093-1104.
1
Basiri, Z. and Heydari, J., 2017. A mathematical model for green supply chain coordination with substitutable products. Journal of Cleaner Production.
2
Bernstein, F. and Federgruen, A., (2003). Pricing and replenishment strategies in a distribution system with competing retailers. Operations Research, 51(3), pp.409-426.
3
Bertrand, J., (1883). iBook review of theoriemathematique de la richessesociale and of recherchessur les principles mathematiques de la theorie des richessesj. Journal de Savants, 67.
4
Bhunia, A.K. and Maiti, M., (1999). An inventory model of deteriorating items with lot-size dependent replenishment cost and a linear trend in demand. Applied Mathematical Modelling, 23(4), pp.301-308.
5
Caldart, A.A. and Oliveira, F., 2010. Analysing industry profitability: A “complexity as cause” perspective. European Management Journal, 28(2), pp.95-107.
6
Chen, X. and Simchi-Levi, D., (2012). Pricing and inventory management. The Oxford handbook of pricing management, pp.784-822.
7
Chen, X., Pang, Z. and Pan, L., (2014). Coordinating inventory control and pricing strategies for perishable products. Operations Research, 62(2), pp.284-300.
8
Choi, S.C., (1991). Price competition in a channel structure with a common retailer. Marketing Science, 10(4), pp.271-296.
9
Chung, K.J. and Liao, J.J., (2006). The optimal ordering policy in a DCF analysis for deteriorating items when trade credit depends on the order quantity. International Journal of Production Economics, 100(1), pp.116-130.
10
Cohen, M.A., (1977). Joint pricing and ordering policy for exponentially decaying inventory with known demand. Naval Research Logistics Quarterly, 24(2), pp.257-268.
11
Dong, L., Narasimhan, C. and Zhu, K., (2009). Product line pricing in a supply chain. Management Science, 55(10), pp.1704-1717.
12
Dye, C.Y. and Ouyang, L.Y., (2005). An EOQ model for perishable items under stock-dependent selling rate and time-dependent partial backlogging. European Journal of Operational Research, 163(3), pp.776-783.
13
Feng, L., Zhang, J. and Tang, W., 2016. Optimal inventory control and pricing of perishable items without shortages. IEEE Transactions on Automation Science and Engineering, 13(2), pp.918-931.
14
Heydari, J. and Norouzinasab, Y., 2016. Coordination of pricing, ordering, and lead time decisions in a manufacturing supply chain. Journal of Industrial and Systems Engineering, 9, pp.1-16.
15
Ingene, C.A. and Parry, M.E., (1995). Channel coordination when retailers compete. Marketing Science, 14(4), pp.360-377.
16
Jiang, L., Wang, Y. and Yan, X., (2014). Decision and coordination in a competing retail channel involving a third-party logistics provider. Computers & Industrial Engineering, 76, pp.109-121.
17
Kang, S. and Kim, I.T., (1983). A study on the price and production level of the deteriorating inventory system. The International Journal of Production Research, 21(6), pp.899-908.
18
Lee, E. and Staelin, R., (1997). Vertical strategic interaction: Implications for channel pricing strategy. Marketing Science, 16(3), pp.185-207.
19
Mahata, G.C. and De, S.K., (2016). An EOQ inventory system of ameliorating items for price dependent demand rate under retailer partial trade credit policy. OPSEARCH, pp.1-28.
20
Maihami, R. and Kamalabadi, I.N., (2012). Joint pricing and inventory control for non-instantaneous deteriorating items with partial backlogging and time and price dependent demand. International Journal of Production Economics, 136(1), pp.116-122.
21
Mondal, B., Bhunia, A.K. and Maiti, M., (2003). An inventory system of ameliorating items for price dependent demand rate. Computers & industrial engineering, 45(3), pp.443-456.
22
Moon, I., Giri, B.C. and Ko, B., 2005. Economic order quantity models for ameliorating/deteriorating items under inflation and time discounting. European Journal of Operational Research, 162(3), pp.773-785.
23
Mukhopadhyay, S., Mukherjee, R.N. and Chaudhuri, K.S., (2004). Joint pricing and ordering policy for a deteriorating inventory. Computers & Industrial Engineering, 47(4), pp.339-349.
24
Padmanabhan, G. and Vrat, P., (1995). EOQ models for perishable items under stock dependent selling rate. European Journal of Operational Research, 86(2), pp.281-292.
25
Padmanabhan, V. and Png, I.P., (1997). Manufacturer's return policies and retail competition. Marketing Science, 16(1), pp.81-94.
26
Pang, Z., (2011). Optimal dynamic pricing and inventory control with stock deterioration and partial backordering. Operations Research Letters, 39(5), pp.375-379.
27
Rabbani, M., Zia, N.P. and Rafiei, H., 2015. Coordinated replenishment and marketing policies for non-instantaneous stock deterioration problem. Computers & Industrial Engineering, 88, pp.49-62.
28
Rajan, A., Rakesh and Steinberg, R., (1992). Dynamic pricing and ordering decisions by a monopolist. Management Science, 38(2), pp.240-262.
29
Sana, S.S., Sarkar, B.K., Chaudhuri, K. and Purohit, D., (2009). The effect of stock, price and advertising on demand-an EOQ model. International Journal of Modelling, Identification and Control, 6(1), pp.81-88.
30
Taleizadeh, A.A., Mohammadi, B., Cárdenas-Barrón, L.E. and Samimi, H., (2013). An EOQ model for perishable product with special sale and shortage. International Journal of Production Economics, 145(1), pp.318-338.
31
Taleizadeh, A.A., Noori-daryan, M. and Cárdenas-Barrón, L.E., (2015). Joint optimization of price, replenishment frequency, replenishment cycle and production rate in vendor managed inventory system with deteriorating items. International Journal of Production Economics, 159, pp.285-295.
32
Teng, J.T., Chang, C.T. and Goyal, S.K., (2005). Optimal pricing and ordering policy under permissible delay in payments. International Journal of Production Economics, 97(2), pp.121-129.
33
Valliathal, M. and Uthayakumar, R., 2010. The production—inventory problem for ameliorating/deteriorating items with non-linear shortage cost under inflation and time discounting. Applied Mathematical Sciences, 4(6), pp.289-304.
34
Valliathal, M. and Uthayakumar, R., 2011. Optimal pricing and replenishment policies of an EOQ model for non-instantaneous deteriorating items with shortages. The International Journal of Advanced Manufacturing Technology, 54(1-4), pp.361-371.
35
Wee, H.M., (1999). Deteriorating inventory model with quantity discount, pricing and partial backordering. International Journal of Production Economics, 59(1), pp.511-518.
36
Wee, H.M., Lo, S.T., Yu, J. and Chen, H.C., (2008). An inventory model for ameliorating and deteriorating items taking account of time value of money and finite planning horizon. International Journal of Systems Science, 39(8), pp.801-807.
37
Woynarovich, A., Hoitsy, G. and Moth-Poulsen, T., (2011). Small-scale rainbow trout farming. Food and Agriculture Organization of the United Nations.
38
Xiao, T. and Xu, T., (2013). Coordinating price and service level decisions for a supply chain with deteriorating item under vendor managed inventory. International Journal of Production Economics, 145(2), pp.743-752.
39
Yao, Z., Leung, S.C. and Lai, K.K., (2008). Manufacturer’s revenue-sharing contract and retail competition. European Journal of Operational Research, 186(2), pp.637-651.
40
Yu, J.C., Lin, Y.S. and Wang, K.J., (2013). Coordination-based inventory management for deteriorating items in a two-echelon supply chain with profit sharing. International Journal of Systems Science, 44(9), pp.1587-1601.
41
Zhang, J., Wei, Q., Zhang, Q. and Tang, W., (2016). Pricing, service and preservation technology investments policy for deteriorating items under common resource constraints. Computers & Industrial Engineering, 95, pp.1-9.
42
Zhu, S.X., 2015. Integration of capacity, pricing, and lead-time decisions in a decentralized supply chain. International Journal of Production Economics, 164, pp.14-23.
43
ORIGINAL_ARTICLE
A New model for integrated lot sizing and scheduling in flexible job shop problem
In this paper an integrated lot-sizing and scheduling problem in a flexible job shop environment with machine-capacity-constraint is studied. The main objective is to minimize the total cost which includes the inventory costs, production costs and the costs of machine’s idle times. First, a new mixed integer programming model,with small bucket time approach,based onProportional Lot sizing and Scheduling Problems (PLSP), is proposed to formulate the problem. Since the problem under study is NP-hard, a modified harmony search algorithm, with a new built-in local search heuristic is proposed as solution technique.In this algorithm,it is improvised a New Harmony vector in two phases to enhance search ability.Additionally, Taguchi method is used to calibrate the parameters of the modified harmony search algorithm. Finally, comparative results demonstrate the effectiveness of the modified harmony search algorithm in solving the problem.It is also demonstrated that the proposed algorithm can find good quality solutions for all size problems. The objective values obtained by proposed algorithm are better from HS algorithm and exact method results.
http://www.jise.ir/article_44919_857b533b08afe51c115e8bcc215a98f9.pdf
2017-09-22T11:23:20
2020-01-28T11:23:20
72
91
Lot-sizing
scheduling
flexible job shop
harmony search algorithm
Rashed
Sahraeian
sahraeian@shahed.ac.ir
true
1
Industrial Engineering, Shahed University
Industrial Engineering, Shahed University
Industrial Engineering, Shahed University
LEAD_AUTHOR
Mohammad
Rohaninejad
rohaninejad.sm@gmail.com
true
2
Industrial Engineering, Shahed University
Industrial Engineering, Shahed University
Industrial Engineering, Shahed University
AUTHOR
Mahmoud
Fadavi
m.fadavi@hotmail.com
true
3
Industrial Engineering, Shahed University
Industrial Engineering, Shahed University
Industrial Engineering, Shahed University
AUTHOR
Adetunji, O. A. B., &Yadavalli, V. (2012). An integrated utilisation, scheduling and lot-sizing algorithm for pull production.International Journal of Industrial Engineering: Theory, Applications and Practice, 19(3).
1
Akrami, B., Karimi, B., &Hosseini, S. M. (2006). Two metaheuristic methods for the common cycle economic lot sizing and scheduling in flexible flow shops with limited intermediate buffers: The finite horizon case. Applied Mathematics and Computation, 183(1), 634-645.
2
Almada-Lobo, B., & James, R. J. (2010). Neighbourhood search meta-heuristics for capacitated lot-sizing with sequence-dependent setups.International Journal of Production Research, 48(3), 861-878.
3
Beraldi, P., Ghiani, G., Grieco, A., &Guerriero, E. (2008). Rolling-horizon and fix-and-relax heuristics for the parallel machine lot-sizing and scheduling problem with sequence-dependent set-up costs.Computers & Operations Research, 35(11), 3644-3656.
4
Drexl, A., &Haase, K. (1995).Proportional lotsizing and scheduling.International Journal of Production Economics, 40(1), 73-87.
5
Fandel, G., &Stammen-Hegene, C. (2006).Simultaneous lot sizing and scheduling for multi-product multi-level production.International Journal of Production Economics, 104(2), 308-316.
6
Fattahi, P., Jolai, F., &Arkat, J. (2009).Flexible job shop scheduling with overlapping in operations.Applied Mathematical Modelling, 33(7), 3076-3087.
7
Fattahi, P., Mehrabad, M. S., &Jolai, F. (2007). Mathematical modeling and heuristic approaches to flexible job shop scheduling problems. Journal of Intelligent Manufacturing, 18(3), 331-342.
8
Gao, K. Z., Suganthan, P. N., Pan, Q. K., Chua, T. J., Cai, T. X., & Chong, C. S. (2014). Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives.Journal of Intelligent Manufacturing, 1-12.
9
Geem, Z. W., Kim, J. H., &Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60-68.
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Haase, K., &Kimms, A. (2000).Lot sizing and scheduling with sequence-dependent setup costs and times and efficient rescheduling opportunities.International Journal of Production Economics, 66(2), 159-169.
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13
Karimi, B., Ghomi, S. F., & Wilson, J. M. (2003). The capacitated lot sizing problem: a review of models and algorithms. Omega, 31(5), 365-378.
14
Karimi-Nasab, M., Seyedhoseini, S. M., Modarres, M., &Heidari, M. (2013). Multi-period lot sizing and job shop scheduling with compressible process times for multilevel product structures. International Journal of Production Research, 51(20), 6229-6246.
15
Kovács, A., Brown, K. N., &Tarim, S. A. (2009).An efficient MIP model for the capacitated lot-sizing and scheduling problem with sequence-dependent setups.International Journal of Production Economics, 118(1), 282-291.
16
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17
Modrak, V. (2012). Alternative Constructive Heuristic Algorithm for Permutation Flow-Shop Scheduling Problem with Make-Span Criterion. International Journal of Industrial Engineering: Theory, Applications and Practice, 19(7).
18
Morais, M. D. F., GodinhoFilho, M., &Boiko, T. J. P. (2014). Hybrid flow shop scheduling problems involving setup considerations: a literature review and analysis. International Journal of Industrial Engineering: Theory, Applications and Practice, 20(11-12).
19
Petrovic, S., Fayad, C., Petrovic, D., Burke, E., & Kendall, G. (2008).Fuzzy job shop scheduling with lot-sizing.Annals of Operations Research, 159(1), 275-292.
20
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21
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22
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23
Ramezanian, R., Saidi-Mehrabad, M., &Fattahi, P. (2013). MIP formulation and heuristics for multi-stage capacitated lot-sizing and scheduling problem with availability constraints. Journal of Manufacturing Systems, 32(2), 392-401.
24
Rohaninejad, M., Kheirkhah, A. S., VahediNouri, B., &Fattahi, P. (2015a). Two hybrid tabu search–firefly algorithms for the capacitated job shop scheduling problem with sequence-dependent setup cost. International Journal of Computer Integrated Manufacturing, 28(5), 470-487.
25
Rohaninejad, M., Kheirkhah, A., &Fattahi, P. (2015b). Simultaneous lot-sizing and scheduling in flexible job shop problems. The International Journal of Advanced Manufacturing Technology,78(1-4), 1-18.
26
Rohaninejad, M., Kheirkhah, A., Fattahi, P., &Vahedi-Nouri, B. (2015c).A hybrid multi-objective genetic algorithm based on the ELECTRE method for a capacitated flexible job shop scheduling problem.The International Journal of Advanced Manufacturing Technology, 77(1-4), 51-66.
27
Sikora, R., Chhajed, D., & Shaw, M. J. (1996).Integrating the lot-sizing and sequencing decisions for scheduling a capacitated flow line.Computers & Industrial Engineering, 30(4), 659-679.
28
Toledo, C. F. M., França, P. M., Morabito, R., &Kimms, A. (2009). Multi-population genetic algorithm to solve the synchronized and integrated two-level lot sizing and scheduling problem.International Journal of Production Research, 47(11), 3097-3119.
29
Wang, L., &Zheng, D. Z. (2001).An effective hybrid optimization strategy for job-shop scheduling problems.Computers & Operations Research, 28(6), 585-596.
30
Wang, L., Pan, Q. K., &Tasgetiren, M. F. (2010).Minimizing the total flow time in a flow shop with blocking by using hybrid harmony search algorithms.Expert Systems with Applications, 37(12), 7929-7936.
31
Wang, L., Pan, Q. K., &Tasgetiren, M. F. (2011).A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem.Computers & Industrial Engineering, 61(1), 76-83.
32
Weidenhiller, A., &Jodlbauer, H. (2009). Equivalence classes of problem instances for a continuous-time lot sizing and scheduling problem. European Journal of Operational Research, 199(1), 139-149.
33
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34
Zhu, C. (2012). Applying Genetic Local Search Algorithm to Solve the Job-Shop Scheduling Problem.International Journal of Industrial Engineering: Theory, Applications and Practice, 19(9).
35
ORIGINAL_ARTICLE
Hybrid algorithms for Job shop Scheduling Problem with Lot streaming and A Parallel Assembly Stage
In this paper, a Job shop scheduling problem with a parallel assembly stage and Lot Streaming (LS) is considered for the first time in both machining and assembly stages. Lot Streaming technique is a process of splitting jobs into smaller sub-jobs such that successive operations can be overlapped. Hence, to solve job shop scheduling problem with a parallel assembly stage and lot streaming, decision makers not only need to determine the processing sequences on machines in first stage, but also need to assign each product to a machine and determine the assembly sequences of the products in second stage and the sub-lot sizes of all jobs and products to minimize the makespan. At first, this problem is modeled as a mixed integer linear programming and GAMS software is applied to solve small problems. Since this problem is classified as NP-hard, four hybrid algorithms based on iterative procedures are suggested to solve the problem in medium and large dimensions. In order to verify the effectiveness of the proposed algorithms, a statistical analysis is used along with Relative Percentage Deviation (RPD) factor. Computational results revealed that the hybrid genetic and parallel simulated annealing algorithm (HGAPSA) and the hybrid genetic and parallel variable neighborhood search algorithm (HGAPVNS) perform better than the other proposed algorithms with respect to the objective function. Also, considering lot streaming for both stages instead of applying it only to the first stage leads to achieve better solutions. Finally, the HGAPSA algorithm is compared with a hybrid genetic algorithm (HGA). Experimental results showed that the HGAPSA outperforms the HGA in terms of solution quality.
http://www.jise.ir/article_44932_ed78ac8c669c75f423446db44af6249f.pdf
2017-09-23T11:23:20
2020-01-28T11:23:20
92
112
Job shop
Parallel Assembly
Lot streaming
HGAPSA
HGAPVNS
Parviz
Fattahi
p.fattahi@alzahra.ac.ir
true
1
Alzahra University
Alzahra University
Alzahra University
LEAD_AUTHOR
Fatemeh
Daneshamooz
f.daneshamooz@gmail.com
true
2
Bu-Ali Sina University
Bu-Ali Sina University
Bu-Ali Sina University
AUTHOR
Al-Anzi, F.S. &Allahverdi, A. (2013). An artificial immune system heuristic for two-stage multi-machine assembly scheduling problem to minimize total completion time. Journal of Manufacturing Systems, 32 (4), 825– 830.
1
Buscher, U.,&Shen, L. (2009). An integrated tabu search algorithm for the lot streaming problem in job shops.European Journal of Operational Research, 199 (2), 385-399.
2
Cummings, D.H.,&Egbelu, P.J. (1998). Minimizing production flow time in a process and assembly jobshop. International Journal of Production Research, 36(8), 2315–2332.
3
Chan, F.T.S., Wong, T.C., &Chan, L.Y. (2008). Lot streaming for product assembly in job shop environment. Robotics and Computer-Integrated Manufacturing, 24 (3), 321–331.
4
Chan, F.T.S., Wong, T.C., &Chan, L.Y. (2009). An evolutionary algorithm for assembly job shop with part sharing. Computers & Industrial Engineering, 57 (3), 641–651. Chen, J., &Steiner, G. (1997). Lot streaming with detached setups in three-machine flow shops. European Journal of Operational Research. 96(3), 591-611.
5
Daneshamooz , F., Jabbari, M., &Fattahi, P. (2013). A model for jobshop scheduling with a parallel assembly stage to minimize makespan. Journal of Industrial Engineering Research in Production Systems, 2(4), 39-53.
6
Dauzere-Peres, S.,&Lasserre, J.B. (1993). An iterative procedure for lot streaming in job-shop scheduling. Computers & Industrial Engineering, 25 (4), 231-234. Demir, Y., &Isleyen, S.K. (2014). An effective genetic algorithm for flexible job-shop scheduling with overlapping in operations. International Journal of Production Research, 52(13), 3905-3921.
7
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8
Fattahi, P., Hosseini, S.M.H., &Jolai, F. (2013). Amathematical model and extension algorithm for assembly flexible flow shop scheduling problem. International Journal of Advanced Manufacturing Technology, 65 (5),787-802.
9
Garey, M.R., Johnson, D.S., &sethi, R. (1976). The Complexity of flow shop and job shop scheduling. Mathematics of Operation Research, 1 (2), 117-129.
10
Jeong, H., Park, J., &Leachman, R.C. (1999). A batch splitting method for a job shop scheduling problem in an MRP environment. International Journal of Production Research, 37 (15), 3583-3598.
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Lee, C.Y., Cheng, T.C.E., &Lin, B.M.T. (1993). Minimizing the makespan in the 3-machine assembly-type flow shop scheduling problem. Management Science, 39 (5), 616-625. Lei, D., &Guo, X. (2013). Scheduling job shop with lot streaming and transportation through a modified artificial bee colony. International Journal of Production Research, 51(16), 4930-4941.
13
Maleki-Darounkolaei, A., Modiri, M., Tavakkoli-Moghadam, R.,&Seyyedi, I. (2012). A three-stage assembly flow shop scheduling problem with blocking and sequence depended setup times. Journal of Industrial Engineering International, 8:26.
14
Manne, A.S. (1960).On the job shop scheduling problem. Operational Research, 8 (2), 219-223.
15
Mohammadi,E. (2016).Multi objective job shop scheduling problem with an assembly stage and lot streaming .Master of Science Thesis, Buali-Sina University.
16
Navaei, J., Fatemi-Ghomi, S.M.T., Jolai, F., &Mozdgir, A. (2014). Heuristics for an assembly flowshop with non-identical assembly machines and sequence dependent setup times to minimize sum of holding and delay costs. Computers & Operations Research, 44, 52–65. Nejati, M., Mahdavi, I., Hassanzadeh, R., &Mahdavi-Amiri, N. (2016). Lot streaming in a two-stage assembly hybrid flow shop scheduling problem with a work shift constraint. International Journal of Production Research, 33(7), 459-471.
17
Reiter, S. (1966). A system for managing job-shop production. Journal of Business, 39 (3), 371–393.
18
Seyedi, I., Maleki-Daronkolaei, A., &Kalashi, F. (2012). Tabu search and simulated annealing for new three-stage assembly flow shop scheduling with blocking. Interdisciplinary Journal of Contemporary Research In Business, 4 (8), 394-402.
19
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20
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21
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22
Wong, T.C., Chan, F.T.S., &Chan, L.Y. (2009). A resource-constrained assembly job shop scheduling problem with Lot Streaming technique. Computers & Industrial Engineering, 57 (3), 983–995.
23
Wong, T.C.,&Ngan, S.C. (2013). A comparison of hybrid genetic algorithm and hybrid particle swarm optimization to minimize makespan for assembly job shop. Applied Soft Computing, 13(3), 1391–1399.
24
Xiong, F., Xing, K., &Wang, F. (2015). Scheduling a hybrid assembly-differentiation flow shop to minimize total flow time. European Journal of Operational Research, 240, 338-354
25
Yao L.,&Sarin, C.S. (2014). Multiple-Lot Lot Streaming in a Two-stage Assembly System. Essays inProduction Project Planning and Scheduling, Springer US.
26
Yazdani, M., Amiri, M., &Zandieh, M. (2010). Flexible job shop scheduling with parallel variable neighborhood search algorithm. Expert system with applications, 37 (1), 678-687.
27
Yokoyama, M., &Santos, D.L. (2005). Three-stage flow-shop scheduling with assembly operations to minimize the weighted sum of product completion times. European Journal of Operational Research, 161, 754–770. Zhang , C.Y. , Li, P., Guan, Z., Rao, Y. (2007). A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem. Computers & Operations Research, 34 (11), 3229-3242.
28
Zhang, R.,&Cheng, W. (2011). A simulated annealing algorithm based on blocking properties for the jobshop scheduling problem with total weighted tardiness objective. Computer and operation research, 38 (5), 854-867.
29
ORIGINAL_ARTICLE
Honey global supply chain network design using fuzzy optimization approach
From the past, honey has been known as a healthy product for human life. Iran has a suitable climate for beekeeping and is among the high-ranked countries in honey production. However, due to failure to comply with quality issues, export of honey from Iran is associated with many problems. According to this issue, this paper presents a robust possibilistic optimization network design model for honey global supply chain regarding global issues (e.g. Incoterms) and quality problems. The proposed network design model considers the product quality and its effect on the amount of demand. Numerical results from the robust model compared with the deterministic model show that the proposed robust model provides appropriate solutions with low risk for the decision makers.
http://www.jise.ir/article_44935_9a46e952ececd5d9ed7361ec3f998075.pdf
2017-09-24T11:23:20
2020-01-28T11:23:20
113
139
Agricultural products
Honey supply chain
Supply chain network design
Robust probabilistic programming
alireza
grivani
alim13777@gmail.com
true
1
Iran University of Science and Technology,School of Industrial Engineering, Tehran, iran
Iran University of Science and Technology,School of Industrial Engineering, Tehran, iran
Iran University of Science and Technology,School of Industrial Engineering, Tehran, iran
AUTHOR
Mir Saman
Pishvaee
pishvaee@iust.ac.ir
true
2
iran university of science and technology
iran university of science and technology
iran university of science and technology
LEAD_AUTHOR
Badri, M. A. (1999). Combining the analytic hierarchy process and goal programming for global facility locationallocation problem. International Journal of Production Economics, 62(3), 237-248.
1
Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical programming, 88(3), 411-424.
2
Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35-53.
3
Canel, C., & Khumawala, B. M. (1996). A mixed-integer programming approach for the international facilities location problem. International Journal of Operations & Production Management, 16(4), 49-68.
4
Caniato, F., Golini, R., & Kalchschmidt, M. (2013). The effect of global supply chain configuration on the Eq.ship between supply chain improvement programs and performance. International Journal of Production Economics, 143(2), 285-293.
5
Chang, C. T., & Chang, C. C. (2000). A linearization method for mixed 0–1 polynomial programs. Computers & Operations Research, 27(10), 1005-1016.
6
Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation (pp. 265-275).Gabler.
7
Dubois, D., & Prade, H. (1987). Linear programming with fuzzy data. Analysis of fuzzy information, 3, 241-263.
8
Farahani, R. Z., Asgari, N., & Davarzani, H. (Eds.). (2009). Supply chain and logistics in national, international and governmental environment: concepts and models. Springer Science & Business Media.
9
Goh, M., Lim, J. Y., & Meng, F. (2007). A stochastic model for risk management in global supply chain networks. European Journal of Operational Research, 182(1), 164-173.
10
Gui-xia, Q., Y.-p. ZHANG, W. Jian-guo and P. Yue-hong (2013). "Revenue sharing in dairy industry supply chain-a case study of Hohhot, China." Journal of Integrative Agriculture 12(12): 2300-2309.
11
Hammami, R., & Frein, Y. (2013). An optimisation model for the design of global multi-echelon
12
supply chainsunder lead time constraints. International Journal of Production Research, 51(9), 2760-2775.
13
Hammami, R., & Frein, Y. (2014). Redesign of global supply chains with integration of transfer pricing: Mathematical modeling and managerial insights. International Journal of Production Economics, 158, 267-277.
14
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15
Hodder, J. E., & Dincer, M. C. (1986). A multifactor model for international plant location and financing under uncertainty. Computers & Operations Research, 13(5), 601-609.
16
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17
Iakovou, E., Vlachos, D., Achillas, C., & Anastasiadis, F. (2012). A methodological framework for the design of green supply chains for the agrifood sector. Working paper.
18
Jamalnia, A., Mahdiraji, H. A., Sadeghi, M. R., Hajiagha, S. H. R., & Feili, A. (2014). An integrated fuzzy QFD and fuzzy goal programming approach for global facility location-allocation problem. International Journal of Information Technology & Decision Making, 13(02), 263-290.
19
Leung, S. C., Tsang, S. O., Ng, W. L., & Wu, Y. (2007). A robust optimization model for multi-site production planning problem in an uncertain environment. European Journal of Operational Research, 181(1), 224-238.
20
Meixell, M. J., & Gargeya, V. B. (2005). Global supply chain design: A literature review and critique.Transportation Research Part E: Logistics and Transportation Review, 41(6), 531-550.
21
Meepetchdee, Y., & Shah, N. (2007). Logistical network design with robustness and complexity considerations. International Journal of Physical Distribution & Logistics Management, 37(3), 201-222.
22
Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281
23
Munson, C. L., & Rosenblatt, M. J. (1997). The impact of local content rules on global sourcing decisions. Production and Operations Management, 6(3), 277-290.
24
Perron, S., Hansen, P., Le Digabel, S., & Mladenović, N. (2010). Exact and heuristic solutions of the global supplychain problem with transfer pricing. European Journal of Operational Research, 202(3), 864-879.
25
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems, 206, 1-20.
26
Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modelling, 37(1), 328-344.
27
Sheu, J. B., & Lin, A. Y. S. (2012). Hierarchical facility network planning model for global logistics network configurations. Applied Mathematical Modelling, 36(7), 3053-3066.
28
Shunko, M. A. S. H. A., & Gavirneni, S. R. I. N. A. G. E. S. H. (2007). Role of transfer prices in global supply chains with random demands. Journal of Industrial and Management Optimization, 3(1), 99.
29
Syam, S. S. (1997). A model for the capacitated p-facility location problem in global
30
environments. Computers & operations research, 24(11), 1005-1016.
31
Taylor, D. H. (1997). Global cases in logistics and supply chain management. Cengage Learning EMEA.
32
Tsolakis, N. K., Keramydas, C. A., Toka, A. K., Aidonis, D. A., & Iakovou, E. T. (2014). Agri-food supply chain management: a comprehensive hierarchical decision-making framework and a critical taxonomy. Biosystems Engineering, 120, 47–64.
33
Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information sciences, 24(2), 143-161.
34
ORIGINAL_ARTICLE
A location-allocation model in the multi-level supply chain with multi-objective evolutionary approach
In the current competitive conditions, all the manufacturers’ efforts are focused on increasing the customer satisfaction as well as reducing the production and delivery costs; thus, there is an increasing concentration on the structure and principles of supply chain (SC). Accordingly, the present research investigated simultaneous optimization of the total costs of a chain and customer satisfaction. The basic innovation of the present research is in the development of the hierarchical location problem of factories and warehouses in a four-level SC with multi-objective approach as well as the use of the multi-objective evolutionary metaheuristic algorithms. The main features of the resulting developed model would include determination of the number and location of the required factories, flow of the raw material from suppliers to factories, determination of the number and location of the distribution centers, flow of the material from factories to distribution centers, and finally allocation of the customers to distribution centers. In order to obtain optimal solutions of the model, a multi-objective hybrid particle swarm algorithm (MOHPSO) was presented; then, to assess performance of the algorithm, its results were compared with those of the NSGA-II algorithm. The numerical results showed that this algorithm had acceptable performance in terms of time and solution quality. On this basis, a real case study was implemented and analyzed for supplying the mountain bikes with the proposed algorithm.
http://www.jise.ir/article_44936_dba158adf1a832eef0ee37dc83c4e5de.pdf
2017-09-28T11:23:20
2020-01-28T11:23:20
140
160
Location and allocation
multi-level supply chain
non-dominated solution
Pareto optimal solution
hybrid particle swarm algorithm
NSGA-II metaheuristic algorithm
Mohammad
Saeedi Mehrabad
mehrabad@iust.ac.ir
true
1
Iran university of science and technology
Iran university of science and technology
Iran university of science and technology
LEAD_AUTHOR
Adel
Aazami
a_aazami@ind.iust.ac.ir
true
2
Iran University of Science and Technology
Iran University of Science and Technology
Iran University of Science and Technology
AUTHOR
Alireza
Goli
a.goli@stu.yazd.ac.ir
true
3
Department of Industrial Engineering, Yazd University, Yazd
Department of Industrial Engineering, Yazd University, Yazd
Department of Industrial Engineering, Yazd University, Yazd
AUTHOR
Che, Z. H. (2012). A particle swarm optimization algorithm for solving unbalanced supply chain planning problems. Applied Soft Computing Journal, 1279–1287.
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