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Reactive Tabu Search for Large Scale Service Parts Logistics Network Design and Inventory Problems

  • Yi Sui
  • Erhan Kutanoglu
  • J. Wesley Barnes
Chapter

Abstract

This chapter documents a study of a reactive tabu search (RTS) approach to the integrated service part logistics (SPL) network design and inventory stocking problem. The integrated problem of designing and stocking an SPL network has attracted more attention recently. The two sets of decisions (network design and inventory stocking) usually have been considered separately and sequentially in practice as well as in the research literature, although interdependency between them exists and integration is necessary for overall system performance optimization. However, the integrated mathematical programming model and solution development for this problem are often intractable due to the time-based service constraints which confine the lower bound of the demand percentage satisfied within the specified time windows. We use a RTS method to efficiently find very good solutions to this problem. Tabu search combines a hill climbing strategy with a memory structure which guides the search. The reactive mechanism dynamically adjusts the tabu tenure during the search. An escape mechanism is activated when the search is trapped in a local attractor basin. We also apply heuristic techniques to construct the initial solution and use rule based comparisons to determine the best non-tabu solution in a neighborhood about the current incumbent solution. By applying this metaheuristic method to problem sets of different sizes, we obtain high-quality solutions with remarkably small amounts of computational effort. For the smaller problems, the tabu search solution is identical or very close to the optimal solution provided by classical optimization-based methods. For the larger problems, RTS obtains solutions superior to those obtained by classical approaches.

Keywords

Tabu Search Service Level Mixed Integer Programming Fill Rate Network Design Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.MicroStrategy, IncMcLeanUSA
  2. 2.The University of Texas at AustinAustinUSA

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