zbMATH — the first resource for mathematics

Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management. (English) Zbl 1142.90416
Summary: Aggregate production-distribution planning (APDP) is one of the most important activities in supply chain management (SCM). When solving the problem of APDP, we are usually faced with uncertain market demands and capacities in production environment, imprecise process times, and other factors introducing inherent uncertainty to the solution. Using deterministic and stochastic models in such conditions may not lead to fully satisfactory results. Using fuzzy models allows us to remove this drawback. It also facilitates the inclusion of expert knowledge. However, the majority of existing fuzzy models deal only with separate aggregate production planning without taking into account the interrelated nature of production and distribution systems. This limited approach often leads to inadequate results. An integration of the two interconnected processes within a single production-distribution model would allow better planning and management. Due to the need for a joint general strategic plan for production and distribution and vague planning data, in this paper we develop a fuzzy integrated multi-period and multi-product production and distribution model in supply chain. The model is formulated in terms of fuzzy programming and the solution is provided by genetic optimization (genetic algorithm). The use of the interactive aggregate production-distribution planning procedure developed on the basis of the proposed fuzzy integrated model with fuzzy objective function and soft constraints allows sound trade-off between the maximization of profit and fillrate. The experimental results demonstrate high efficiency of the proposed method.

90B50 Management decision making, including multiple objectives
90C59 Approximation methods and heuristics in mathematical programming
90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
Full Text: DOI
[1] Abbasbandy, S.; Asady, B., Ranking of fuzzy numbers by sign distance, Inform. sci., 176, 16, 2405-2416, (2006) · Zbl 1293.62008
[2] Aliev, R.A.; Fazlollahi, B.; Aliev, R.R., Soft computing and its application in business and economics, (2004), Verlag · Zbl 1054.68107
[3] Bergstrom, G.L.; Smith, B.E., Multi-item production planning – an extension of the HMMS rules, Manage. sci., 16, 614-629, (1970)
[4] Bitran, G.R.; Yanassee, H.H., Deterministic approximations to stochastic production problems, Oper. res., 32, 999-1018, (1984) · Zbl 0562.90081
[5] Chandra, P.M.; Fisher, L., Coordination of production and distribution planning, Euro. J. oper. res., 72, 503-517, (1994) · Zbl 0805.90051
[6] Chen, S.-H., Computational intelligence in economics and finance: carrying on the legacy of herbert Simon, Inform. sci., 170, 1, 121-131, (2005)
[7] Chen, S.-P.; Chang, P.C., A mathematical programming approach to supply chain models with fuzzy parameters, Eng. optimiz., 38, 6, 647-669, (2006)
[8] Chen, Z-L., Integrated scheduling of production and distribution operations, Manage. sci., 51, 4, 614-628, (2005) · Zbl 1145.90380
[9] Cohen, M.A.; Lee, H.L., Strategic analysis of integrated production-distribution systems: models and methods, Oper. res., 36, 2, 216-228, (1988)
[10] Disney, S.M.; Naim, M.M.; Potter, A., Assessing the impact of e-business on supply chain dynamics, Int. J. prod. econ., 89, 2, 109-118, (2004)
[11] Dornier, P.-P.; Ernst, Ricardo; Fender, Michel; Konvelis, Panos, Global operations and logistics. text and cases, (1998), John Wiley and Sons Inc.
[12] Dotoli, M.; Fanti, M.P.; Meloni, C.; Zhou, M., Design and optimization of integrated e-supply chain for agile and environmentally conscious manufacturing, IEEE trans. syst. man cyb. part A: syst. hum., 36, 1, 62-75, (2006)
[13] Fazel Zarandi, M.H.; Turksen, I.B.; Saghiri, S., Supply chain: crisp and fuzzy aspects, Int. J. appl. math. comput. sci., 12, 3, 423-435, (2002)
[14] Fung, R.Y.K.; Tang, J.; Wang, D., Multiproduct aggregate production planning with fuzzy demand and fuzzy capacities, IEEE trans. syst. man cyb. part A: syst. hum., 33, 3, (2003)
[15] Hausman, W.H.; McClain, J.D., A note on the Bergstrom-Smith multi-item production planning model, Manage. sci., 17, 783-785, (1971)
[16] Jung, H.; Jeong, B., Decentralised production-distribution planning system using collaborative agents in supply chain network, Int. J. adv. manuf. technol. Springer London, 25, 1-2, 167-173, (2005)
[17] Gao, M.; Zhou, M.C.; Tang, Y., Intelligent decision making in disassembly process based on fuzzy reasoning Petri nets, IEEE trans. syst. man. cyb. B: cyb., 34, 5, 2029-2084, (2004)
[18] Gaonkar, R.; Viswanadham, N., Strategic sourcing and collaborative planning in Internet-enabled supply chain networks producing multigeneration products, IEEE trans. autom. sci. eng., 2, 1, 54-66, (2005)
[19] Gottwald, S., Mathematical fuzzy logic as a tool for the treatment of vague information, Inform. sci., 172, 1-2, 41-71, (2005) · Zbl 1079.03014
[20] Y.H. Lee, S.H. Kim, Optimal production-distribution planning in supply chain management using a hybrid simulation-analytic approach. In: Proceedings of the 2000 Winter Simulation Conference, 2000, 1252-1259.
[21] Y.Y. Lee, Fuzzy set theory approach to aggregate production planning and inventory control. Ph.D. Dissertation, Department of Industrial Engineer, Kansas State University, Manhatten, 1990.
[22] Liang, T.-F., Distribution planning decisions using interactive fuzzy multi-objective linear programming, Fuzzy sets syst., 157, 10, 1303-1316, (2006) · Zbl 1132.90384
[23] Holt, C.C., Planning production inventories and workforce, (1960), Prentice Hall Englewood Cliffs, NJ
[24] Ozdamar, L.; Bozyel, M.A.; Birbil, S.I., A hierarchical decision support system for production planning (with case study), Euro. J. oper. res., 104, 403-422, (1988) · Zbl 0960.90505
[25] Park, Y.B., An integrated approach for production and distribution planning in supply chain management, Int. J. prod. res., 43, 6, 1205-1224, (2005) · Zbl 1068.90557
[26] (), 318
[27] Rinks, D.B., The performance of fuzzy algorithm models for aggregate planning and differing cost structures, (), 267-278
[28] Sarmiento, A.M.; Nagi, R., A review of integrated analysis of production-distributed systems, IIE trans., 31, 1061-1074, (1999)
[29] Sheen, J.N., Fuzzy financial profitability analyses of demand side management alternatives from participant perspective, Inform. sci., 169, 3-4, 329-364, (2005) · Zbl 1114.91322
[30] Simpson, N.C.; Vakharia, A.J., Integrated production/distribution planning in supply chains: an invited review, Euro. J. oper. res., 115, 219-236, (1999) · Zbl 0949.90658
[31] Tomas, D.J.; Griffin, P.N., Coordinated supply chain management, Euro. J. oper. res., 94, 1-15, (1996) · Zbl 0929.90004
[32] Vidal, C.J.; Goetschalckx, M., Strategic production-distribution models: a critical review with emphasis on global supply chain models, Euro. J. oper. res., 98, 1-18, (1998) · Zbl 0922.90062
[33] Wang, D.; Fang, S.-C., A genetics-based approach or aggregate production planning in fuzzy environment, IEEE trans. syst. man. cyb. A, 27, 636-645, (1997)
[34] Zadeh, L., Toward a generalized theory of uncertainty (GTU) - an outline, Inform. sci., 172, 1-2, 1-40, (2005) · Zbl 1074.94021
[35] Zadrożnya, S.; Kacprzyk, J., Computing with words for text processing: an approach to the text categorization, Inform. sci., 176, 4, 415-437, (2006)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.