Recent Models and Solution Methodologies for Optimization Problems in Supply Chain Management Under Fuzziness

  • Seda Yanık UğurluEmail author
  • Ayca Altay
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 313)


Supply chain (SC) involves collaborating with business partners which uniquely specialize on only a few key strategic activities. The network structures formed in SC’s have emerged in the last decade with the accelerated developments in globalization, outsourcing and information technology. The complex network structures have introduced novel problems to both industry and academia while traditional complications are yet investigated. The intensification points of SC problems are mainly configuration of distribution networks, forming distribution strategies, trade-off analyses, managing inventory and cash-flow. One of the main challenges in modeling and solving these problems is to deal with the uncertainties involved in the complex nature of SC. Demand has been the main uncertain aspect of the problems of the related literature followed by internal parameters, supplier related parameters, environmental parameters and price. The uncertainty issues have been commonly dealt with fuzzy approaches in the literature. Fuzzy approaches become beneficial under uncertainties such as the absence of data, use of qualitative data or the need for subjective judgments. Hence, fuzzy techniques in SC optimization problems are vastly implemented in the literature. The purpose of this study is basically to summarize the fuzzy techniques employed for SC optimization models, their past applications, solutions algorithms and offer directions for future research.


Supply chain management Fuzzy set theory Fuzzy optimization Fuzzy mathematical programming Chance-constrained programming 


  1. Aliev, R.A., Fazlollahi, B., Guirimov, B.G., Aliev, R.R.: Fuzzy-genetic approach to aggregate production–distribution planning in supply chain management. Inf Sci 177(20), 4241–4255 (2007)CrossRefzbMATHGoogle Scholar
  2. Arikan, F.: A fuzzy solution approach for multi objective supplier selection. Expert Sys Appl 40(3), 947–952 (2013)CrossRefGoogle Scholar
  3. Bilgen, B.: Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem. Expert Syst Appl 37(6), 4488–4495 (2010)CrossRefGoogle Scholar
  4. Björk, K.-M.: A multi-item fuzzy economic production quantity problem with a finite production rate. Int J Prod Econ 135(2), 702–707 (2012)Google Scholar
  5. Chandran, S., Kandaswamy, G.: A fuzzy approach to transport optimization problem. Optim Eng 1–16 (2012). doi: 10.1007/s11081-012-9209-6
  6. Charnes, A., Cooper, W.W.: Chance constrained programming. Manag Sci 6, 73–79 (1959)CrossRefzbMATHMathSciNetGoogle Scholar
  7. Chen, C.-L., Lee, W.-C.: Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Comput Chem Eng 28(6–7), 1131–1144 (2004)CrossRefGoogle Scholar
  8. Chen, C.-L., Yuan, T.-W., Lee, W.-C.: Multi-criteria fuzzy optimization for locating warehouses and distribution centers in a supply chain network. Chin J Chem Eng 38(5–6), 393–407 (2007)CrossRefGoogle Scholar
  9. Friedman, N., Halpern, J.Y.: Plausibility measures: a user’s guide. In: Proceedings of 11th Conference on Uncertainty in Artificial Intelligence (UAI 95) (1995)Google Scholar
  10. Georgescu, I., Kinnunen, J.: Credibility measures in portfolio analysis: from possibilistic to probabilistic models. J Appl Oper Res 3(2), 91–102 (2011)Google Scholar
  11. Ghatee, M., Hashemi, S.M.: Application of fuzzy minimum cost flow problems to network design under uncertainty. Fuzzy Set Syst 460(22), 3263–3289 (2009)CrossRefMathSciNetGoogle Scholar
  12. Gong, Y., Huang, D., Wang, W., Peng, Y.-G.: A fuzzy chance constraint programming approach for location-allocation problem under uncertainty in a closed-loop supply chain. In: International Joint Conference on Computational Sciences and Optimization, pp. 836–840 (2009)Google Scholar
  13. Gumus, A.T., Guneri, A.F., Keles, S.: Supply chain network design Ysing an integrated neuro-fuzzy and MILP approach: a comparative design study. Expert Syst Appl 36(10), 12570–12577 (2009)CrossRefGoogle Scholar
  14. Handfield, R., Warsing, D., Wu, X.: Inventory policies in a fuzzy uncertain supply chain environment. Eur J Oper Res 197(2), 609–619 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  15. Huang, X.: Fuzzy chance-constrained portfolio selection. Appl Math Comput 177(2), 500–507 (2006)CrossRefzbMATHMathSciNetGoogle Scholar
  16. Iris, C., Serdar-Asan, S.: A review of genetic algorithm applications in supply chain network design. In: Kahraman, C. (ed.) Computational Intelligence Systems in Industrial Engineering, pp. 203–230. Atlantis Press, Paris (2012)CrossRefGoogle Scholar
  17. Jazemi, R., Ghodsypour, S.H., Gheidar-Kheljani, J.: Considering supply chain benefit in supplier selection problem by using information sharing benefits. IEEE Trans Ind Inform 7(3), 517–526 (2011)CrossRefGoogle Scholar
  18. Jing-min, W., Dan, Z., Li, T.: A simulation-based robust optimization model for supply chain network design. In: ICNC’08, Fourth International Conference on Natural Computation, pp. 515–519 (2008)Google Scholar
  19. Kabak, Ö., Ülengin, F.: Possibilistic linear-programming approach for supply chain networking decisions. Eur J Oper Res 209(3), 253–264 (2011)CrossRefzbMATHGoogle Scholar
  20. Klir, G.J., Yuan, B.: Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall, Upper Saddle River (1995)zbMATHGoogle Scholar
  21. Kubat, C., Yuce, B.: A hybrid intelligent approach for supply chain management system. J Intel Manuf 23(4), 1237–1244 (2012)CrossRefGoogle Scholar
  22. Lambert, D.M., Stock, J.R., Ellram, L.M.: Fundamentals of logistics management. Irwin/McGraw-Hill Publishing, Boston (1998)Google Scholar
  23. Lau, H.C.W., Chan, T.M., Tsui, W.T., Chan, F.T.S., Ho, G.T.S., Choy, K.L.: A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem. Expert Syst Appl 36(4), 8255–8268 (2009)CrossRefGoogle Scholar
  24. Li, X., Ralescu, D.: Credibility measure of fuzzy sets and applications. Int J Adv Intel Paradigms 1(3), 241–250 (2009)CrossRefGoogle Scholar
  25. Liang, T.-F.: Integrated manufacturing/distribution planning decisions with multiple imprecise goals in an uncertain environment. Qual Quant 46(1), 137–153 (2012)CrossRefGoogle Scholar
  26. Liu, D., Chen, Y., Mao, H., Zhang, Z., Gu, X.: Optimization of the supply chain production planning programming under hybrid uncertainties. In: International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 1235–1239 (2008)Google Scholar
  27. Luhandjula, M.K.: Mathematical programming: theory, applications and extension. J Uncertain Syst 1(2), 124–136 (2007)Google Scholar
  28. Mahnam, M., Yadollahpour, M.R., Famil-Dardashti, V., Hejazi, S.R.: Supply chain modeling in uncertain environment with bi-objective approach. Comput Ind Eng 56(4), 1535–1544 (2009)CrossRefGoogle Scholar
  29. Makkar, S., Jha, P.C., Arora, N.: Single-source, single-destination coordination of EOQ Model for perishable products with quantity discounts incorporating partial/full truckload policy under fuzzy environment. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds.) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011), pp. 971–982 (2012)Google Scholar
  30. Melo, M.T., Nickel, S., Saldanha-da-Gama, F.: Facility location and supply chain management: a review. Eur J Oper Res 196(2), 401–412 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  31. Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G.: Defining supply chain management. J Bus Logist 22(2), 1–25 (2001)CrossRefGoogle Scholar
  32. Miller, S., John, R.: An interval type-2 fuzzy multiple echelon supply chain model. Knowl Based Syst 23(4), 363–368 (2010)CrossRefGoogle Scholar
  33. Mitra, K., Gudi, R.D., Patwardhan, S.C., Sardar, G.: Towards resilient supply chains: uncertainty analysis using fuzzy mathematical programming. Chem Eng Res Des 87(7), 967–981 (2009)CrossRefGoogle Scholar
  34. Mula, J., Poler, R., García-Sabater, J.P., Lario, F.C.: Models for production planning under uncertainty: a review. Int J Prod Econ 103(1), 271–285 (2006)CrossRefGoogle Scholar
  35. Mula, J., Peidro, D., Poler, R.: The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand. Int J Prod Econ 128(1), 136–143 (2010)CrossRefGoogle Scholar
  36. Nepal, B., Monplaisir, L., Famuyiwa, O.: Matching product architecture with supply chain design. Eur J Oper Res 216(2), 315–325 (2012)CrossRefMathSciNetGoogle Scholar
  37. Özkır, V., Başlıgil, H.: Multi-objective optimization of closed-loop supply chains in uncertain environment. J Clean Prod 41, 114–125 (2013)CrossRefGoogle Scholar
  38. Paksoy, T., Yapici-Pehlivan, N.: A fuzzy linear programming model for the optimization of multi-stage supply chain networks with triangular and trapezoidal membership functions. J Franklin Inst 349(1), 93–109 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  39. Paksoy, T., Yapici Pehlivan, N., Özceylan, E.: Application of fuzzy optimization to a supply chain network design: a case study of an edible vegetable oils manufacturer. Appl Math Model 36(6), 2762–2776 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  40. Peidro, D., Mula, J., Poler, R., Lario, F.-C.: Quantitative models for supply chain planning under uncertainty: a review. Int J Adv Manuf Tech 43(3–4), 400–420 (2009)CrossRefGoogle Scholar
  41. Petrovic, D., Xie, Y., Burnham, K., Petrovic, R.: Coordinated control of distribution supply chains in the presence of fuzzy customer demand. Eur J Oper Res 185(19), 146–158 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  42. Pinto-Varela, T., Barbosa-Póvoa, A.P.F.D., Novais, A.Q.: Bi-objective optimization approach to the design and planning of supply chains: economic versus environmental performances. Comput Chem Eng 35(8), 1454–1468 (2011)CrossRefGoogle Scholar
  43. Pishvaee, M.S., Razmi, J.: Environmental supply chain network design using multi-objective fuzzy mathematical programming. Appl Math Model 36(8), 3433–3446 (2012)CrossRefzbMATHMathSciNetGoogle Scholar
  44. Pishvaee, M.S., Torabi, S.A.: A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Set Syst 161(20), 2668–2683 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  45. Qin, Z., Ji, X.: Logistics network design for product recovery in fuzzy environment. Eur J Oper Res 202(2), 479–490 (2010)CrossRefzbMATHGoogle Scholar
  46. Rao, S.S.: Engineering Optimization: Theory and Practice. Wiley, New York (2009)CrossRefGoogle Scholar
  47. Ross, T.J.: Fuzzy logic with engineering applications, 3rd edn. Wiley, West Sussex (2002)Google Scholar
  48. Ross, T.J., Sellos, K.F., Booker, J.M.: Fuzzy logic and probability applications: bridging the gap. In: Ross, T.J., Booker, J.M., Parkinson, W.J. (eds.) ASA-SIAM series on statistics and applied probability. American Statistical Association, Alexandria and The Society for Industrial and Applied Mathematics, Philadelphia (2002)Google Scholar
  49. Selim, H., Ozkarahan, I.: Application of fuzzy multi-objective programming approach to supply chain distribution network design problem. In: Gelbukh, A., Reyes-García, C. (eds.) Advances in Artificial Intelligence, pp. 415–425. MICAI, Mexico (2006)Google Scholar
  50. Selim, H., Ozkarahan, I.: A supply chain distribution network design model: an interactive fuzzy goal programming-based solution approach. Int J Adv Manuf Tech 36(3–4), 401–418 (2008)CrossRefGoogle Scholar
  51. Türkşen, I.B.: Belief, plausibility, and probability measures on interval-valued type-2 fuzzy sets. Int J Intel Syst 19(7), 681–699 (2004)CrossRefzbMATHGoogle Scholar
  52. Vahdani, B., Tavakkoli-Moghaddam, R., Modarres, M., Baboli, A.: 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 (2012)CrossRefGoogle Scholar
  53. Wang, Z., Klir, G.J.: Fuzzy measure theory. Springer, New York (1992)CrossRefzbMATHGoogle Scholar
  54. Wang, J., Shu, Y.-F.: A possibilistic decision model for new product supply chain design. Eur J Oper Res 177(2), 1044–1061 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  55. Wong, J.-T.: The distribution processing and replenishment policy of the supply chain under asymmetric information and deterioration: insight into the information value. Expert Syst Appl 37(3), 2347–2353 (2010)CrossRefGoogle Scholar
  56. Xie, Y., Petrovic, D., Burnham, K.: A heuristic procedure for the two-level control of serial supply chains under fuzzy customer demand. Int J Prod Econ 102(1), 37–50 (2006)CrossRefGoogle Scholar
  57. Xu, R., Zhai, X.: Optimal models for single-period supply chain problems with fuzzy demand. Inf Sci 178(17), 3374–3381 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  58. Xu, R., Zhai, X.: Analysis of supply chain coordination under fuzzy demand in a two-stage supply chain. Appl Math Model 34(1), 129–139 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  59. Xu, J., Liu, Q., Wang, R.: A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of chinese liquor. Inf Sci 178(8), 2022–2043 (2008)CrossRefzbMATHGoogle Scholar
  60. Xu, J., He, Y., Gen, M.: A class of random fuzzy programming and its application to supply chain design. Comput Ind Eng 56(3), 937–950 (2009)CrossRefGoogle Scholar
  61. Yuansheng, H., Zilong, Q., Qingchao, L.: Supply chain network design based on fuzzy neural network and PSO. In: IEEE International Conference on Automation and Logistics, 2008, ICAL 2008, pp. 2189–2193 (2008)Google Scholar
  62. Yugang, L., Guang, H.: A planning model for distribution network design based on fuzzy multi-objective lattice-order decision. Syst Eng 7, 271–280 (2006)Google Scholar
  63. Zadeh, L.A.: Fuzzy sets. Inf Control 8, 338–353 (1965)CrossRefzbMATHMathSciNetGoogle Scholar
  64. Zhao, R., Tang, W.: Redundancy Optimization Problems with Uncertain Lifetimes. In: Levitin, G. (ed.) Computational Intelligence in Reliability Engineering, pp. 329–374. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  65. Zheng, Y.-J., Ling, H.-F.: Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach. Soft Comput 17(7), 1301–1314 (2013)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Industrial Engineering DepartmentIstanbul Technical UniversityIstanbulTurkey

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