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Uncertain Centralized/Decentralized Production-Distribution Planning Problem in Multi-Product Supply Chains: Fuzzy Mathematical Optimization Approaches

  • Khalili-Damghani, Kaveh (Department of Industrial Engineering, South-Tehran Branch, Islamic Azad University) ;
  • Ghasemi, Peiman (Department of Industrial Engineering, South-Tehran Branch, Islamic Azad University)
  • Received : 2015.09.23
  • Accepted : 2016.05.09
  • Published : 2016.06.30

Abstract

Complex and uncertain issues in supply chain result in integrated decision making processes in supply chains. So decentralized (distributed) decision making (DDM) approach is considered as a crucial stage in supply chain planning. In this paper, an uncertain DDM through coordination mechanism is addressed for a multi-product supply chain planning problem. The main concern of this study is comparison of DDM approach with centralized decision making (CDM) approach while some parameters of decision making are assumed to be uncertain. The uncertain DDM problem is modeled through fuzzy mathematical programming in which products' demands are assumed to be uncertain and modeled using fuzzy sets. Moreover, a CDM approach is customized and developed in presence of fuzzy parameters. Both approaches are solved using three fuzzy mathematical optimization methods. Hence, the contribution of this paper can be summarized as follows: 1) proposing a DDM approach for a multi-product supply chain planning problem; 2) Introducing a coordination mechanism in the proposed DDM approach in order to utilize the benefits of a CDM approach while using DDM approach; 3) Modeling the aforementioned problem through fuzzy mathematical programming; 4) Comparing the performance of proposed DDM and a customized uncertain CDM approach on multi-product supply chain planning; 5) Applying three fuzzy mathematical optimization methods in order to address and compare the performance of both DDM and CDM approaches. The results of these fuzzy optimization methods are compared. Computational results illustrate that the proposed DDM approach closely approximates the optimal solutions generated by the CDM approach while the manufacturer's and retailers' decisions are optimized through a coordination mechanism making lasting relationship.

Keywords

Supply Chain Management;Distributed Decision Making (DDM);Coordination Mechanism in Supply Chain;Fuzzy Mathematical Programming;Fuzzy Optimization

Acknowledgement

Supported by : Islamic Azad University

References

  1. Arikan, F. (2013), A fuzzy solution approach for multi objective supplier selection, Expert Systems with Applications, 40(3), 947-952. https://doi.org/10.1016/j.eswa.2012.05.051
  2. Bassett, M. and Gardner, L. (2013), Designing optimal global supply chains at Dow Agro Sciences, Annals of Operations Research, 203(1), 187-216. https://doi.org/10.1007/s10479-010-0802-2
  3. Cachon, G. P. (2001), Stock wars: Inventory competition in a two-echelon supply chainmultiple retailers, Operations Research, 49(5), 658-674. https://doi.org/10.1287/opre.49.5.658.10611
  4. Cao, D. and Chen, M. (2006), Capacitated plant selection in a decentralized manufacturing environment: A bilevel optimization approach, European Journal of Operational Research, 169(1), 97-110. https://doi.org/10.1016/j.ejor.2004.05.016
  5. Clark, A. J. and Scarf, H. (1960), Optimal policies for multi-echelon inventory problem, Management Science, 6(4), 475-490. https://doi.org/10.1287/mnsc.6.4.475
  6. Ertogral, K. and Wu, S. D. (2000), Auction-theoretic coordination of production planning in the supply chain, IEE Trans, 32(10), 931-940.
  7. Hegeman, J., Peidro, D., Alemany, M., and Diaz-Madronero, M. (2014), A Decentralized Production and Distribution Planning Model in an Uncertain Environment, Studies in Fuzziness and Soft Computing, 313, Springer-Verlag, Berlin.
  8. Jimenez, M., Arenas, M., Bilbao, A., and Rodriguez, M. (2007), Linear programming with fuzzy parameters: an interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609. https://doi.org/10.1016/j.ejor.2005.10.002
  9. Jung, H., Chen, F., and Jeong, B. (2008), Decentralized supply chain planning framework for third party logistics partnership, Computers and Industrial Engineering, 55(2), 348-364. https://doi.org/10.1016/j.cie.2007.12.017
  10. Khalili-Damghani, K. and Shahrokh, A. (2014), Solving a New Multi-Period Multi-Objective Multi-Product Aggregate Production Planning Problem Using Fuzzy Goal Programming, Industrial Engineering and Management Systems, 13(4), 369-382. https://doi.org/10.7232/iems.2014.13.4.369
  11. Li, J., Wang, S., and Cheng, T. C. E. (2010), Competition and cooperation in a single-retailer two-supplier supply chain with supply disruption, International Journal of Production Economics, 124(1), 137-150. https://doi.org/10.1016/j.ijpe.2009.10.017
  12. Lin, C.-C. and Wang, T.-H. (2011), Build-to-order supply chain network design under supply and demand uncertainties, Transportation Research: Part B, 45(8), 1-15. https://doi.org/10.1016/j.trb.2010.06.001
  13. Lu, S., Lau, H., and Yiu, C. (2012), A hybrid solution to collaborative decision-making in a decentralized supply chain, Journal of Engineering and Technology Management, 29(1), 95-111. https://doi.org/10.1016/j.jengtecman.2011.09.008
  14. Luhandjula, M. K. (2007), Mathematical programming: theory, applications and extension, Journal of Uncertain Systems, 1(2), 124-136.
  15. Meredith, J. R. and Shafer, S. M. (2007), Operations Management for MBAs, 3rd edn. John Wiley, New Jersey.
  16. Min, D. (2015), Supply Chain Coordination Under the Cap-and-trade Emissions Regulation, Industrial Engineering and Management Systems, 41(3), 243-252.
  17. Nishi, T., Konishi, M., and Ago, M. (2007), A distributed decision making system for integrated optimization of production scheduling and distribution for aluminum production line, Computers and Chemical Engineering, 31(10), 1205-1221. https://doi.org/10.1016/j.compchemeng.2006.10.006
  18. Peidro, D., Mula, J., Poler, R., and Verdegay, J.-L. (2009), Fuzzy optimization for supply chain planning under supply, demand and process uncertainties, Fuzzy Sets and Systems, 160(1), 2640-2657. https://doi.org/10.1016/j.fss.2009.02.021
  19. Pibernik, R. and Sucky, E. (2006), Centralised and decentralised supply chain planning, Int. J. IntegratedSupply Management, 2(1/2), 6-27.
  20. Pishvae, M. S., Rabbani, M., and Torabi, S. A. (2011), A robust optimization approach to closed-loop supply chain network design under uncertainty, Applied Mathematical Modelling, 35(2), 637-649. https://doi.org/10.1016/j.apm.2010.07.013
  21. Salema, M. I. G., Barbosa-Povoa, A. P., and Novais, Q. A. (2007). An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty, European Journal of Operational Research, 179(3), 1063-1077. https://doi.org/10.1016/j.ejor.2005.05.032
  22. Sawik, T. (2011), Selection of supply portfolio under disruption risks, Omega, 41(2), 194-208.
  23. Schneeweiss, C. (2003), Distributed decision making-a unified approach, European Journal of Operational Research, 150(2), 237-252. https://doi.org/10.1016/S0377-2217(02)00501-5
  24. Schneeweiss, C. and Zimmer, K. (2004), Hierarchical coordination mechanisms within the supply chain, European Journal of Operational Research, 153(3), 687-703. https://doi.org/10.1016/S0377-2217(02)00801-9
  25. Shams, H., Doosti Mogouee, M., Jamali, F., and Haji, A. (2012), A Survey on Fuzzy Linear Programming, American Journal of Scientific Research, 75(1), 117-133.
  26. Shu, J., Teo, C. P., and Shen, Z. J. (2005), Stochastic transportation-inventory network design problem, Operations Research, 53(1), 48-60. https://doi.org/10.1287/opre.1040.0140
  27. Stadtler, H. (2009), A framework for collaborative planning and state-of-the-art, OR Spectrum, 31(1), 5-30. https://doi.org/10.1007/s00291-007-0104-5
  28. Tan Y.-F. and Cao, B.-Y. (2005), Another Discussion about Optimal Solution to Fuzzy Constraints Linear Programming, Fuzzy Systems and Knowledge DiscoveryLecture Notes in Computer Science, Springer-Verlag Berlin Heidelberg, 3613, 156-159.
  29. Vahdani, B., Tavakkoli-Moghaddam, R., Modarres, M., and Baboli, A. (2012), Reliable design of a forward/reverse logistics network under uncertainty: a robust-M/M/C queuing model, Transport Res E-Log, 48(6), 1152-1168. https://doi.org/10.1016/j.tre.2012.06.002
  30. Wang, F., Lai, X., and Shi, N. (2011), A multi-objective optimization for green supply chain network design, Decision Support Systems, 51(2), 262-269. https://doi.org/10.1016/j.dss.2010.11.020
  31. Werners, B. (1987), An interactive fuzzy programming system, Fuzzy Sets and Systems, 23(1), 131-147. https://doi.org/10.1016/0165-0114(87)90105-9
  32. Xu, J., Liu, Q., and Wang, R. (2008), A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor, Information Sciences, 178(8), 2022-2043. https://doi.org/10.1016/j.ins.2007.11.025
  33. Zadeh, L. (1965), Fuzzy sets, Information Control, 8(1), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
  34. Zanjani, M., Ait-Kadi, D., and Nourelfath, M. (2010), Robust production planning in a manufacturing environment with random yield: A case in sawmill production planning, European Journal of Operational Research, 201(3), 882-891. https://doi.org/10.1016/j.ejor.2009.03.041

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