Two local search approaches for solving real-life car sequencing problems

Estellon, Bertrand; Gardi, Frédéric; Nouioua, Karim

doi:10.1016/j.ejor.2007.04.043

Cited by 51 publications

(51 citation statements)

References 15 publications

(29 reference statements)

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“…We believe that this gap may be explained by the lack of intensification of the search for this type of approach. By combining GA-NCPX MO with a local search procedure inspired from the one proposed by Estellon et al (2007) and using the mutation operators presented in Section 4.4 to explore the neighbourhood, we obtain the results shown in the last column of Table 3. We mention here that GA-NCPX MO +LS was executed with the same time limit as the other algorithms presented in this chapter.…”

Section: Experimental Comparisonmentioning

confidence: 77%

“…Among the resolution methods proposed by the participants of the challenge, one finds essentially neighbourhood search methods as simulated annealing, iterative tabu search, iterative local search and variable neighbourhood search (Briant et al, 2007;Cordeau et al, 2007;Estellon et al, 2007;Ribiero et al, 2007a;Gavranović, 2007;Benoist, 2007), an ant colony optimization algorithm (ACO) and a genetic algorithm (GA) (Jaszkiewicz et al, 2004). Since the work of all the participating teams was not published, the previous enumeration is not exhaustive.…”

Section: Introductionmentioning

confidence: 99%

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Design of an Efficient Genetic Algorithm to Solve the Industrial Car Sequencing Problem

Zinflou¹,

Gagné²,

Gravel³

2008

Advances in Evolutionary Algorithms

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Section: Experimental Comparisonmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Design of an Efficient Genetic Algorithm to Solve the Industrial Car Sequencing Problem

Zinflou¹,

Gagné²,

Gravel³

2008

Advances in Evolutionary Algorithms

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“…Our past experiences in engineering high-performance local-search algorithms (Estellon et al 2006(Estellon et al , 2008(Estellon et al , 2009Benoist et al 2009a) have convinced us that neglecting one of these three layers may yield a significant decrease in terms of performance. Then, designing and implementing local-search heuristics is a complex, time-consuming task for OR practitioners.…”

Section: Prior Work and Contributionsmentioning

confidence: 98%

LocalSolver 1.x: a black-box local-search solver for 0-1 programming

et al. 2011

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This paper introduces LocalSolver 1.x, a black-box local-search solver for general 0-1 programming. This software allows OR practitioners to focus on the modeling of the problem using a simple formalism, and then to defer its actual resolution to a solver based on efficient and reliable local-search techniques. Started in 2007, the goal of the LocalSolver project is to offer a model-and-run approach to combinatorial optimization problems which are out of reach of existing black-box tree-search solvers (integer or constraint programming). Having outlined the modeling formalism and the main technical features behind LocalSolver, its effectiveness is demonstrated through an extensive computational study. The version 1.1 of LocalSolver can be freely downloaded at http://www.localsolver.com and used for educational, research, or commercial purposes.

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“…Attaining these two goals in realistic time is an important challenge for any multi-objective algorithm. However, in the literature, the ICSP, despite its multi-objective character, has been treated as a problem with a single goal or with several goals lexicographically ordered (Benoist, 2008;Briant et al, 2008;Cordeau et al, 2008;Estellon et al, 2008;Ribeiro et al, 2008). To our knowledge, the only references that treat the ICSP from a purely multi-objective viewpoint are those of Zinflou et al (2009) and of de Oliveira dos Reis ( 2007); the latter only examines small instances (fewer than 60 automobiles).…”

Section: Introductionmentioning

confidence: 99%