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.
@article{RO_2006__40_2_113_0,
author = {Saharidis, Georgios K. and Dallery, Yves and Karaesmen, Fikri},
title = {Centralized versus decentralized production planning},
journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
pages = {113--128},
publisher = {EDP-Sciences},
volume = {40},
number = {2},
year = {2006},
doi = {10.1051/ro:2006017},
zbl = {1137.90561},
language = {en},
url = {http://archive.numdam.org/articles/10.1051/ro:2006017/}
}
TY - JOUR AU - Saharidis, Georgios K. AU - Dallery, Yves AU - Karaesmen, Fikri TI - Centralized versus decentralized production planning JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2006 SP - 113 EP - 128 VL - 40 IS - 2 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/ro:2006017/ DO - 10.1051/ro:2006017 LA - en ID - RO_2006__40_2_113_0 ER -
%0 Journal Article %A Saharidis, Georgios K. %A Dallery, Yves %A Karaesmen, Fikri %T Centralized versus decentralized production planning %J RAIRO - Operations Research - Recherche Opérationnelle %D 2006 %P 113-128 %V 40 %N 2 %I EDP-Sciences %U http://archive.numdam.org/articles/10.1051/ro:2006017/ %R 10.1051/ro:2006017 %G en %F RO_2006__40_2_113_0
Saharidis, Georgios K.; Dallery, Yves; Karaesmen, Fikri. Centralized versus decentralized production planning. RAIRO - Operations Research - Recherche Opérationnelle, Tome 40 (2006) no. 2, pp. 113-128. doi : 10.1051/ro:2006017. http://archive.numdam.org/articles/10.1051/ro:2006017/
[1] and, Multi-stage, pull-type production/inventory system. IIE Transaction 27 (1995) 190-200.
[2] , Supply chain design and analysis: Models and methods. Int. J. Prod. Econ. 55 (1998) 281-294.
[3] and, Production planning using a hybrid simulation-analytical approach. Int. J. Prod. Econ. 59 (1999) 305-311.
[4] ,, and, Blending OR/MS, judgement, and GIS: Restucturing P&G's supply chain. Interfaces 27 (1997) 128-142.
[5] and H.l. Lee, Resource deployment analysis of global manufacturing and distribution networks. J. Man. Oper. Manag. 2 (1989) 81-104.
[6] and, Impact of production scale economies, manufacturing complexity, and transportation costs on supply chain facility networks. J. Man. Oper. Manag. 3 (1990) 269-292.
[7] ,,, and, 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] , and, Handbook of operations research New York. Van Nostrand Reinhold, 1th edition, 1978. | MR | Zbl
[9] , and, Integrated production, inventory and distribution systems. Int. J. Prod. Res. 26 (1988) 473-482.
[10] and, Extended model of hybrid production planning approach. Working paper (2002).
[11] and, Material management in decentralized supply chain. Oper. Res. 41 (1993) 835-847. | Zbl
[12] , and, Hewlett-packard gains control of inventory and service through design for localization. Interfaces 23 (1993) 18-49.
[13] and, Product configuration and postponement for supply chain efficiency, in Fourth Industrial Engineering Research Coference Proceeding, Institute of Industrial Engineers (1995) 43-48.
[14] and, Production-distribution planning in supply chain considering capacity constraints. Comput. Industrial Engrg. 43 (2002) 196-190.
[15] and, Subcontracting, coordination, flexibility and production smoothing in aggregate planning. Manag. Sci. 36 (1990).
[16] , and, Hybrid auto-adaptable simulated annealing based heuristic. Comput. Industrial Engrg. 37 (1999) 277-280.
[17] ,, Fine planning for supply chains in semiconductor manufacture. J. Materials Processing Technology 107 (2000) 390-397.
[18] , and, Centralized versus decentralized production planning in supply chains. Technical report, page École Centrale Paris (2003).
[19] and, Evaluation of one-for-one replenishment policies for multi-echelon inventory systems. Manag. Sci. 37 (1991) 68-83.
[20] , Supply chain dynamics. Int. J. Comput. Integrated Manufacturing 4 (1991) 197-208.
[21] , and, Industrial dynamics simulation models in the design of supply chains. Int. J. Phys. Distribution Logistics Management 22 (1992) 3-13.
[22] , Mathematical programming techniques to debottleneck the supply chain of the chemical industries. Comput. Chem. Engrg. 20 (1996) S1269-S1274.
[23] , and, Smoothing supply chain dynamics. Int. J. Prod. Econ. 22 (1991) 231-248.
[24] , 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] , 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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