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Scientific article
English

Metaheuristics for a job scheduling problem with smoothing costs relevant for the car industry

Published inNetworks, vol. 67, no. 3, p. 246-261
Publication date2016
Abstract

on nonidentical machines with applications in the car industry, inspired by the problem proposed by the car manufacturer Renault in the ROADEF 2005 Challenge. Makespan, smoothing costs and setup costs are minimized following a lexicographic order, where smoothing costs are used to balance resource utilization. We first describe a mixed integer linear programming (MILP) formulation and a network interpretation as a variant of the well-known vehicle routing problem. We then propose and compare several solution methods, ranging from greedy procedures to a tabu search and an adaptive memory algorithm. For small instances (with up to 40 jobs) whose MILP formulation can be solved to optimality, tabu search provides remarkably good solutions. The adaptive memory algorithm, using tabu search as an intensification procedure, turns out to yield the best results for large instances.

Keywords
  • Job scheduling
  • Smoothing cost
  • Unrelated machine
  • Metaheuristic
Citation (ISO format)
RESPEN, Jean, ZUFFEREY, Nicolas, AMALDI, Edoardo. Metaheuristics for a job scheduling problem with smoothing costs relevant for the car industry. In: Networks, 2016, vol. 67, n° 3, p. 246–261. doi: 10.1002/net.21656
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Article (Accepted version)
accessLevelRestricted
Identifiers
ISSN of the journal0028-3045
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