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The Use of Meta-heuristics to Solve Economic Lot Scheduling Problem

  • Conference paper
Evolutionary Computation in Combinatorial Optimization (EvoCOP 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3448))

Abstract

Economic lot scheduling problem has been an important topic in production planning and scheduling research for more than four decades. The problem is known to be NP-hard due to it’s combinatorial nature. In this paper, two meta-heuristics algorithms – Tabu Search and Simulated Annealing – are proposed. To investigate the effect of control parameters to the performance of tabu search and simulated annealing algorithms, a general factorial design of experiment study is used. Two Neighborhood Search heuristics that differ in rounding off scheme of the production frequencies are also tested. Experimental study shows that both tabu search and simulated annealing algorithms outperform two best known solution methods – Dobson’s Heuristic and Hybrid Genetic Algorithm.

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Author information

Authors and Affiliations

  1. Concordia University, 1455 de Maisonneuve Blvd. W., Montreal, Quebec, H3G 1M8, Canada

    Syed Asif Raza & Ali Akgunduz

Authors
  1. Syed Asif Raza
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  2. Ali Akgunduz
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Editor information

Editors and Affiliations

  1. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstraße 9–11/1861, 1040, Vienna, Austria

    Günther R. Raidl

  2. SAP AG, Neurottstr. 16, P.O. Box, Walldorf, Germany

    Jens Gottlieb

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© 2005 Springer-Verlag Berlin Heidelberg

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Raza, S.A., Akgunduz, A. (2005). The Use of Meta-heuristics to Solve Economic Lot Scheduling Problem. In: Raidl, G.R., Gottlieb, J. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2005. Lecture Notes in Computer Science, vol 3448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31996-2_18

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  • DOI: https://doi.org/10.1007/978-3-540-31996-2_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25337-2

  • Online ISBN: 978-3-540-31996-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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