Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-24T12:32:35.962Z Has data issue: false hasContentIssue false
  • English
  • Français

CentralizedVERSUSdecentralized productionplanning

Published online by Cambridge University Press:  12 October 2006

Georgios K. Saharidis ,
Yves Dallery  and
Fikri Karaesmen
Georgios K. Saharidis
Affiliation:
Laboratoire Génie Industriel, École Centrale Paris, Grande Voie des Vignes, 92295 Chatenay-Malabry Cedex, France; e-mail:
Yves Dallery
Affiliation:
Laboratoire Génie Industriel, École Centrale Paris, Grande Voie des Vignes, 92295 Chatenay-Malabry Cedex, France; e-mail:
Fikri Karaesmen
Affiliation:
Department of Industrial Engineering, Koç University, 34450 Istanbul, Turkey; [email protected]
Get access

Abstract

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

Keywords

Global-local optimizationproduction planningcentralized-decentralized models.
Type
Research Article
Information
RAIRO - Operations Research , Volume 40 , Issue 2: ROADEF 03 , April 2006 , pp. 113 - 128
Copyright
© EDP Sciences, 2006

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Altiok, T. and Ranjan, R., Multi-stage, pull-type production/inventory system. IIE Transaction 27 (1995) 190–200. CrossRef
Beamon Ben, M.ita, Supply chain design and analysis: Models and methods. Int. J. Prod. Econ. 55 (1998) 281–294. CrossRef
Byrne, M.A. and Bakir, M.D., Production planning using a hybrid simulation-analytical approach. Int. J. Prod. Econ. 59 (1999) 305–311. CrossRef
Camm, T.E., Chorman, J.D., Dull, F.A. and Evans, J.R., Blending OR/MS, judgement, and GIS: Restucturing P&G's supply chain. Interfaces 27 (1997) 128–142. CrossRef
Cohen, M.A. and H.l. Lee, Resource deployment analysis of global manufacturing and distribution networks. J. Man. Oper. Manag. 2 (1989) 81–104.
Cohen, M.A. and Moon, S., Impact of production scale economies, manufacturing complexity, and transportation costs on supply chain facility networks. J. Man. Oper. Manag. 3 (1990) 269–292.
Gnonia, R., Iavagnilio, M.G., Mossa, G., Mummolo, G. and DiLeva, A., 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. CrossRef
J. Hax, A.C. Moder and S. Elmaghraby, Handbook of operations research New York. Van Nostrand Reinhold, 1th edition, 1978.
Ishii, K., Takahashi, K. and Muramatsu, R., Integrated production, inventory and distribution systems. Int. J. Prod. Res. 26 (1988) 473–482. CrossRef
S. Kim and B. Kim, Extended model of hybrid production planning approach. Working paper (2002).
Lee, C. and Billington, H.L., Material management in decentralized supply chain. Oper. Res. 41 (1993) 835–847. CrossRef
Lee, C., Billington, H.L. and Carter, B., Hewlett-packard gains control of inventory and service through design for localization. Interfaces 23 (1993) 18–49. CrossRefPubMed
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.
Lee, S.H. and Kim, Y.H., Production-distribution planning in supply chain considering capacity constraints. Comput. Industrial Engrg. 43 (2002) 196–190.
I. Kamien Morton and L. Lode, Subcontracting, coordination, flexibility and production smoothing in aggregate planning. Manag. Sci. 36 (1990).
Riane, A., Artiba, F. and Iassinovski, S., Hybrid auto-adaptable simulated annealing based heuristic. Comput. Industrial Engrg. 37 (1999) 277–280. CrossRef
Rupp, M., Ristic, T.M., Fine planning for supply chains in semiconductor manufacture. J. Materials Processing Technology 107 (2000) 390–397. CrossRef
Y. Saharidis, G.K. Dallery and F. Karaesmen, Centralized versus decentralized production planning in supply chains. Technical report, page École Centrale Paris (2003).
Svoronos, P. and Zipkin, A., Evaluation of one-for-one replenishment policies for multi-echelon inventory systems. Manag. Sci. 37 (1991) 68–83. CrossRef
Towill, D.R., Supply chain dynamics. Int. J. Comput. Integrated Manufacturing 4 (1991) 197–208. CrossRef
Towill, M.M., Naim, D.R. and Wikner, J., Industrial dynamics simulation models in the design of supply chains. Int. J. Phys. Distribution Logistics Management 22 (1992) 3–13. CrossRef
Voudouris, V.T., Mathematical programming techniques to debottleneck the supply chain of the chemical industries. Comput. Chem. Engrg. 20 (1996) S1269–S1274. CrossRef
Wikner, D.R., Towill, J. and Naim, M., Smoothing supply chain dynamics. Int. J. Prod. Econ. 22 (1991) 231–248. CrossRef
Williams, J.F., Heuristic techniques for simultaneous scheduling of production and distribution in multi-echelon structures: Theory and empirical comparisons. Manag. Sci. 27 (1981) 336–352. CrossRef
Williams, J.F., A hybrid algorithm for simultaneous scheduling of production and distribution in multi-echelon structures. Manag. Sci. 29 (1983) 77–92. CrossRef