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Supply Chain Management: An Opportunity for Metaheuristics

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Author Info
Helena Ramalhinho-Lourenço ()
Abstract

In today’s highly competitive and global marketplace the pressure on organizations to find new ways to create and deliver value to customers grows ever stronger. In the last two decades, logistics and supply chain has moved to the center stage. There has been a growing recognition that it is through an effective management of the logistics function and the supply chain that the goal of cost reduction and service enhancement can be achieved. The key to success in Supply Chain Management (SCM) require heavy emphasis on integration of activities, cooperation, coordination and information sharing throughout the entire supply chain, from suppliers to customers. To be able to respond to the challenge of integration there is the need of sophisticated decision support systems based on powerful mathematical models and solution techniques, together with the advances in information and communication technologies. The industry and the academia have become increasingly interested in SCM to be able to respond to the problems and issues posed by the changes in the logistics and supply chain. We present a brief discussion on the important issues in SCM. We then argue that metaheuristics can play an important role in solving complex supply chain related problems derived by the importance of designing and managing the entire supply chain as a single entity. We will focus specially on the Iterated Local Search, Tabu Search and Scatter Search as the ones, but not limited to, with great potential to be used on solving the SCM related problems. We will present briefly some successful applications.

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Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 538.

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Date of creation: Mar 2001
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Handle: RePEc:upf:upfgen:538

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Related research
Keywords: Supply chain management; metaheuristics; iterated local search; tabu search and scatter search;

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Find related papers by JEL classification:
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques
M29 - Business Administration and Business Economics; Marketing; Accounting - - Business Economics - - - Other

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  1. Fleischmann, Moritz & Bloemhof-Ruwaard, Jacqueline M. & Dekker, Rommert & van der Laan, Erwin & van Nunen, Jo A. E. E. & Van Wassenhove, Luk N., 1997. "Quantitative models for reverse logistics: A review," European Journal of Operational Research, Elsevier, vol. 103(1), pages 1-17, November. [Downloadable!] (restricted)
  2. Slats, Piet A. & Bhola, Bis & Evers, Joseph J. M. & Dijkhuizen, Gert, 1995. "Logistic chain modelling," European Journal of Operational Research, Elsevier, vol. 87(1), pages 1-20, November. [Downloadable!] (restricted)
  3. Rita Ribeiro & Helena Ramalhinho-Lourenço, 2001. "A Multi-objective Model for a Multi-period Distribution Management Problem," Economics Working Papers 532, Department of Economics and Business, Universitat Pompeu Fabra. [Downloadable!]
  4. Thomas, Douglas J. & Griffin, Paul M., 1996. "Coordinated supply chain management," European Journal of Operational Research, Elsevier, vol. 94(1), pages 1-15, October. [Downloadable!] (restricted)
  5. Bloemhof-Ruwaard, Jacqueline M. & van Beek, Paul & Hordijk, Leen & Van Wassenhove, Luk N., 1995. "Interactions between operational research and environmental management," European Journal of Operational Research, Elsevier, vol. 85(2), pages 229-243, September. [Downloadable!] (restricted)
  6. M.P. de Brito & S.D.P. Flapper & R. Dekker, 2002. "Reverse logistics," Econometric Institute Report 272, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
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