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A review of applications of genetic algorithms in lot sizing

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Abstract

Lot sizing problems are production planning problems with the objective of determining the periods where production should take place and the quantities to be produced in order to satisfy demand while minimizing production, setup and inventory costs. Most lot sizing problems are combinatorial and hard to solve. In recent years, to deal with the complexity and find optimal or near-optimal results in reasonable computational time, a growing number of researchers have employed meta-heuristic approaches to lot sizing problems. One of the most popular meta-heuristics is genetic algorithms which have been applied to different optimization problems with good results. The focus of this paper is on the recent published literature employing genetic algorithms to solve lot sizing problems. The aim of the review is twofold. First it provides an overview of recent advances in the field in order to highlight the many ways GAs can be applied to various lot sizing models. Second, it presents ideas for future research by identifying gaps in the current literature. In reviewing the relevant literature the focus has been on the main features of the lot sizing problems and the specifications of genetic algorithms suggested in solving these problems.

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Authors and Affiliations

  1. Department of Industrial Engineering, Dokuz Eylul University, 35100, Bornova, Izmir, Turkey

    Hacer Guner Goren & Semra Tunali

  2. HEC Montréal, 3000, Chemin de la Côte-Sainte-Catherine, Montreal, QC, Canada, H3T 2A7

    Raf Jans

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  1. Hacer Guner Goren
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  2. Semra Tunali
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  3. Raf Jans
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Correspondence to Hacer Guner Goren.

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Guner Goren, H., Tunali, S. & Jans, R. A review of applications of genetic algorithms in lot sizing. J Intell Manuf 21, 575–590 (2010). https://doi.org/10.1007/s10845-008-0205-2

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