Centralized versus decentralized production planning
RAIRO - Operations Research - Recherche Opérationnelle, Volume 40 (2006) no. 2, pp. 113-128.

In the course of globalization, many enterprises change their strategies and are coupled in partnerships with suppliers, subcontractors and customers. This coupling forms supply chains comprising several geographically distributed production facilities. Production planning in a supply chain is a complicated and difficult task, as it has to be optimal both for the local manufacturing units and for the whole supply chain network. In this paper two analytical models are used to solve the production planning problem in supply chain involving several enterprises. Generally in practice, for competitive and/or practical reasons, frequently each enterprise prefers to optimize its production plan with little care about the other members of the supply chain. This case is presented through a simple model of decentralized optimization. The aim of this study is to analyze and compare the two types of optimization: centralized and decentralized. The initial question is: what are the profit and the optimal policy of global (centralized) optimization in contrast to local (decentralized)? We characterize this gain by comparing the optimal profits obtained in both cases.

DOI: 10.1051/ro:2006017
Keywords: global-local optimization, production planning, centralized-decentralized models
Saharidis, Georgios K. ; Dallery, Yves ; Karaesmen, Fikri 1

1 Department of Industrial Engineering, Koç University, 34450 Istanbul, Turkey
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     title = {Centralized versus decentralized production planning},
     journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
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     publisher = {EDP-Sciences},
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Saharidis, Georgios K.; Dallery, Yves; Karaesmen, Fikri. Centralized versus decentralized production planning. RAIRO - Operations Research - Recherche Opérationnelle, Volume 40 (2006) no. 2, pp. 113-128. doi : 10.1051/ro:2006017. http://archive.numdam.org/articles/10.1051/ro:2006017/

[1] T. Altiok and R. Ranjan, Multi-stage, pull-type production/inventory system. IIE Transaction 27 (1995) 190-200.

[2] M. Beamon Benita, Supply chain design and analysis: Models and methods. Int. J. Prod. Econ. 55 (1998) 281-294.

[3] M.A. Byrne and M.D. Bakir, Production planning using a hybrid simulation-analytical approach. Int. J. Prod. Econ. 59 (1999) 305-311.

[4] T.E. Camm, J.D. Chorman, F.A. Dull and J.R. Evans, Blending OR/MS, judgement, and GIS: Restucturing P&G's supply chain. Interfaces 27 (1997) 128-142.

[5] M.A. Cohen and H.l. Lee, Resource deployment analysis of global manufacturing and distribution networks. J. Man. Oper. Manag. 2 (1989) 81-104.

[6] M.A. Cohen and S. Moon, Impact of production scale economies, manufacturing complexity, and transportation costs on supply chain facility networks. J. Man. Oper. Manag. 3 (1990) 269-292.

[7] R. Gnonia, M.G. Iavagnilio, G. Mossa, G. Mummolo and A. Dileva, Production planning of a multi-site manufacturing system by hybrid modelling: A case study from the automotive industry. Int. J. Prod. Econ. 85 (2003) 251-262.

[8] J. Hax, A.C. Moder and S. Elmaghraby, Handbook of operations research New York. Van Nostrand Reinhold, 1th edition, 1978. | MR | Zbl

[9] K. Ishii, K. Takahashi and R. Muramatsu, Integrated production, inventory and distribution systems. Int. J. Prod. Res. 26 (1988) 473-482.

[10] S. Kim and B. Kim, Extended model of hybrid production planning approach. Working paper (2002).

[11] C. Lee and H.L. Billington, Material management in decentralized supply chain. Oper. Res. 41 (1993) 835-847. | Zbl

[12] C. Lee, H.L. Billington and B. Carter, Hewlett-packard gains control of inventory and service through design for localization. Interfaces 23 (1993) 18-49.

[13] E. Lee and H.L. Feitzinger, Product configuration and postponement for supply chain efficiency, in Fourth Industrial Engineering Research Coference Proceeding, Institute of Industrial Engineers (1995) 43-48.

[14] S.H. Lee and Y.H. Kim, Production-distribution planning in supply chain considering capacity constraints. Comput. Industrial Engrg. 43 (2002) 196-190.

[15] I. Kamien Morton and L. Lode, Subcontracting, coordination, flexibility and production smoothing in aggregate planning. Manag. Sci. 36 (1990).

[16] A. Riane, F. Artiba and S. Iassinovski, Hybrid auto-adaptable simulated annealing based heuristic. Comput. Industrial Engrg. 37 (1999) 277-280.

[17] M. Rupp, T.M. Ristic, Fine planning for supply chains in semiconductor manufacture. J. Materials Processing Technology 107 (2000) 390-397.

[18] Y. Saharidis, G.K. Dallery and F. Karaesmen, Centralized versus decentralized production planning in supply chains. Technical report, page École Centrale Paris (2003).

[19] P. Svoronos and A. Zipkin, Evaluation of one-for-one replenishment policies for multi-echelon inventory systems. Manag. Sci. 37 (1991) 68-83.

[20] D.R. Towill, Supply chain dynamics. Int. J. Comput. Integrated Manufacturing 4 (1991) 197-208.

[21] M.M. Towill, D.R. Naim and J. Wikner, Industrial dynamics simulation models in the design of supply chains. Int. J. Phys. Distribution Logistics Management 22 (1992) 3-13.

[22] V.T. Voudouris, Mathematical programming techniques to debottleneck the supply chain of the chemical industries. Comput. Chem. Engrg. 20 (1996) S1269-S1274.

[23] D.R. Wikner, J. Towill and M. Naim, Smoothing supply chain dynamics. Int. J. Prod. Econ. 22 (1991) 231-248.

[24] J.F. Williams, Heuristic techniques for simultaneous scheduling of production and distribution in multi-echelon structures: Theory and empirical comparisons. Manag. Sci. 27 (1981) 336-352. | Zbl

[25] J.F. Williams, A hybrid algorithm for simultaneous scheduling of production and distribution in multi-echelon structures. Manag. Sci. 29 (1983) 77-92. | Zbl

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