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Fuzzy decision modeling for supply chain management. (English) Zbl 1075.90532
Summary: Managing a supply chain (SC) is very difficult, since various sources of uncertainty and complex interrelationships between various entities exist in the SC. Moreover, the reducing product life cycle and the heightened expectations of customers have also made the SC even harder to manage, especially for innovative products. Although the innovative products can enable a company to achieve higher profit margins, they make demands for them unpredictable, because no historical data is available. This paper develops a fuzzy decision methodology that provides an alternative framework to handle SC uncertainties and to determine SC inventory strategies, while there is lack of certainty in data or even lack of available historical data. Fuzzy set theory is used to model SC uncertainty. A fuzzy SC model based on possibility theory is developed to evaluate SC performances. Based on the proposed fuzzy SC model, a genetic algorithm approach is developed to determine the order-up-to levels of stock-keeping units in the SC to minimize the SC inventory cost subject to the restriction of fulfilling the target fill rate of the finished product. The proposed model allows decision makers to express their risk attitudes and to analyze the trade-off between customer service level and inventory investment in the SC and better SC inventory strategies can be made. A simulation approach is used to validate the concept developed.

90B50 Management decision making, including multiple objectives
03E72 Theory of fuzzy sets, etc.
FULPAL; Genocop
Full Text: DOI
[1] Beamon, B.M., Supply chain design and analysismodels and methods, Int. J. prod. econom., 55, 281-294, (1998)
[2] Carlsson, C.; Fuller, R., Reducing the bullwhip effect by means of intelligent, soft computing methods, ()
[3] Cheng, F.; Ettl, M.; Lin, G.; Yao, D.D., Inventory-service optimization in configure-to-order systems, Manuf. service oper. manage., 4, 2, 114-132, (2002)
[4] Dimopoulos, C.; Zalzala, A.M.S., Recent developments in evolutionary computation for manufacturing optimizationproblems, solutions, and comparisons, IEEE trans. evol. comput., 4, 2, 93-113, (2000)
[5] Dubois, D.; Prade, H., Operations on fuzzy numbers, Int. J. systems sci., 9, 613-626, (1978) · Zbl 0383.94045
[6] Dubois, D.; Prade, H., Fuzzy sets and statistical data, Eur. J. oper. res., 25, 3, 345-356, (1986) · Zbl 0588.62002
[7] Dubois, D.; Prade, H., Possibility theoryan approach to computerized processing of uncertainty, (1988), Plenum Press New York
[8] Fisher, M.L., What is the right supply chain for your product, Harvard bus. rev., 75, 105-116, (1997)
[9] Fortemps, P., Jobshop scheduling with imprecise durationsa fuzzy approach, IEEE trans. fuzzy systems, 5, 4, 557-569, (1997)
[10] French, S., Decision theoryan introduction to the mathematics of rationality, (1993), Ellis Horwood New York
[11] Giachetti, R.E.; Young, R.E., Analysis of the error in the standard approximation used for multiplication of triangular and trapezoidal fuzzy numbers and the development of a new approximation, Fuzzy sets and systems, 91, 1-13, (1997) · Zbl 0915.04003
[12] Giachetti, R.E.; Young, R.E., A parametric representation of fuzzy numbers and their arithmetic operators, Fuzzy sets and systems, 91, 185-202, (1997) · Zbl 0920.04008
[13] Giannoccaro, I.; Pontrandolfo, P.; Scozzi, B., A fuzzy echelon approach for inventory management in supply chains, Eur. J. oper. res., 149, 1, 185-196, (2003) · Zbl 1035.90002
[14] Goetschalckx, M.; Vidal, C.J.; Dogan, K., Modeling and design of global logistics systemsa review of integrated strategic and tactical models and design algorithms, Eur. J. oper. res., 143, 1, 1-18, (2002) · Zbl 1073.90501
[15] Goldberg, D.E., Genetic algorithms in search, optimization, and machine learning, (1989), Addison-Wesley Reading, MA · Zbl 0721.68056
[16] Graves, S.C.; Willems, S.P., Optimizing strategic safety stock placement in supply chains, Manuf. service oper. manage., 2, 1, 68-83, (2000)
[17] Kaufmann, A.; Gupta, M.M., Introduction to fuzzy arithmetic, (1991), Van Nostrand Reinhold New York · Zbl 0754.26012
[18] Klir, G.J.; Yuan, B., Fuzy sets and fuzzy logictheory and applications, (1995), Prentice-Hall Englewood Cliffs, NJ
[19] Lee, H.L.; Billington, C., Material management in decentralized supply chain, Oper. res., 41, 5, 835-847, (1993) · Zbl 0800.90548
[20] Lee, H.L.; Billington, C., The evolution of supply-chain-management models and practice at hewlett-packard, Interfaces, 25, 5, 42-63, (1995)
[21] Lee, H.L.; Padmanabhan, V.; Whang, S., Information distortion in a supply chainthe bullwhip effect, Manage. sci., 43, 546-558, (1997) · Zbl 0888.90047
[22] Michalewicz, Z., Genetic algorithms+ data structures= evolution programs, (1992), Springer Berlin · Zbl 0763.68054
[23] Petrovic, D., Simulation of supply chain behaviour and performance in an uncertain environment, Int. J. prod. econ., 71, 1-3, 429-438, (2001)
[24] Petrovic, D.; Roy, R.; Petrovic, R., Modelling and simulation of a supply chain in an uncertain environment, Eur. J. oper. res., 109, 1, 200-309, (1998) · Zbl 0937.90047
[25] Petrovic, D.; Roy, R.; Petrovic, R., Supply chain modelling using fuzzy sets, Int. J. prod. econom., 59, 1-3, 443-453, (1999)
[26] Rommelfanger, H., Fulpalan interactive method for solving (multiobjective) fuzzy linear programming problems, (), 279-299
[27] Silver, E.A.; Peterson, R., Decision systems for inventory management and production planning, (1985), Wiley New York
[28] Simchi-Levi, D.; Kaminsky, P.; Simchi-Levi, E., Designing and managing the supply chain: concepts, strategies, and case studies, (2000), McGraw-Hill New York
[29] Syswerda, G., Uniform crossover in genetic algorithms, ()
[30] Thomas, D.J.; Griffin, P.M., Coordinated supply chain management, Eur. J. oper. res., 94, 1, 1-15, (1996) · Zbl 0929.90004
[31] Vidal, C.J.; Goetschalckx, M., Strategic production-distribution modelsa critical review with emphasis on global supply chain models, Eur. J. oper. res., 98, 1, 1-18, (1997) · Zbl 0922.90062
[32] Zadeh, L.A., Fuzzy sets as a basis for a theory of possibility, Fuzzy sets and systems, 1, 1, 3-28, (1978) · Zbl 0377.04002
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