2007
DOI: 10.1016/j.ejor.2005.12.008
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Meta-heuristics for dynamic lot sizing: A review and comparison of solution approaches

Abstract: Proofs from complexity theory as well as computational experiments indicate that most lot sizing problems are hard to solve. Because these problems are so difficult, various solution techniques have been proposed to solve them. In the past decade, meta-heuristics such as tabu search, genetic algorithms and simulated annealing, have become popular and efficient tools for solving hard combinational optimization problems.

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Cited by 240 publications
(95 citation statements)
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“…Such reviews were given by Bahl et al (1987), Brahimi et al (2006), Drexl & Kimms (1997), Jans & Degraeve (2007), Karimi et al (2003), Kuik et al (1994), Maes & van Wassenhove (1988), Salomon et al (1991), Staggemeier & Clark (2001), Wolsey (1995) and recently Buschkuehl et al (2010).…”
Section: Review Of the Literaturementioning
confidence: 99%
“…Such reviews were given by Bahl et al (1987), Brahimi et al (2006), Drexl & Kimms (1997), Jans & Degraeve (2007), Karimi et al (2003), Kuik et al (1994), Maes & van Wassenhove (1988), Salomon et al (1991), Staggemeier & Clark (2001), Wolsey (1995) and recently Buschkuehl et al (2010).…”
Section: Review Of the Literaturementioning
confidence: 99%
“…It is, therefore, critical that metaheuristic testing occurs on the large problems for which optimal solutions could not be calculated in reasonable runtimes. As discussed in [19], it is not enough to test on small problem instances and extrapolate the results for larger instances; algorithms can perform differently in both runtime and solution quality on large problem instances.…”
Section: Goals In Creating the Testbedmentioning
confidence: 99%
“…Since some dynamic lot sizing problems may be difficult to solve, several authors have developed meta-heuristics to find good solutions to these problems. Jans and Degraeve (2007) gave an overview of meta-heuristics for dynamic lot sizing and showed that especially simulated annealing, tabu search and genetic algorithms have frequently been used to solve this type of lot sizing problem. Neural networks and threshold accepting, in contrast, have only been used very infrequently in this domain.…”
Section: Content Analysismentioning
confidence: 99%