CP with ACO

Khichane, Madjid; Albert, Patrick; Solnon, Christine

doi:10.1007/978-3-540-68155-7_32

Cited by 11 publications

(9 citation statements)

References 10 publications

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“…Additional experiments were conducted with a subset of instances from Perron and Shaw (2004) which is considered to be one of the datasets with the hardest instances to solve (Khichane et al 2008). To further understand the algorithms, we consider a subset from Perron and Shaw (2004) which is the same subset of instances used by Thiruvady et al (2011).…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…From amongst the datasets considered above, Perron and Shaw (2004) are known to contain the hardest instances (Khichane et al 2008). Furthermore, since ACO is stochastic, several runs with the ACO algorithms are required to determine statistical significance.…”

Section: Resultsmentioning

confidence: 99%

“…Various hybrids involving ACO have been investigated. These include integrations with other meta-heuristics (Blum 2005;Blum et al 2008;López-Ibáñez et al 2009) and exact methods such as integer programming (Anghinolfi et al 2011) and constraint programming (Khichane et al 2008;Meyer and Ernst 2004;Thiruvady et al 2009Thiruvady et al , 2012). An LR-ACO hybrid was examined on the multi-choice multi-dimensional problem (Ren et al 2012).…”

Section: Introductionmentioning

confidence: 99%

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A Lagrangian-ACO matheuristic for car sequencing

Thiruvady

Ernst

Wallace

2014

EURO Journal on Computational Optimization

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In this study, we investigate a hybrid Lagrangian relaxation ant colony optimisation for optimisation version of the car sequencing problem. Several cars are required to be scheduled on an assembly line and each car requires a number of options such as sunroof and/or air conditioning. These cars are required to be sequenced such that sub-sequences of specific sizes may only include a limited number of any option. While this is usually a hard constraint, in this study we treat it as a soft constraint and further require the utilisation of options be modulated across the sequence leading to the optimisation problem. We investigate various Lagrangian heuristics, ant colony optimisation (ACO) and hybrids of these methods. The results show that the Lagrangian-ACO hybrid is the best performing method for up to 300 cars.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Lagrangian-ACO matheuristic for car sequencing

Thiruvady

Ernst

Wallace

2014

EURO Journal on Computational Optimization

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“…Ce dernier, est un problème de référence utilisé au sein de la communauté PPC pour tester les performances et l'efficacité de leurs algorithmes. Cette première contribution à été publiée dans [KAS08a,KAS07,KAS08b,KAS08c].…”

Section: Intégration D'aco à Un Langage De Ppc Pour Résoudre Des Cspunclassified

“…Les travaux que nous avons développés sur l'intégration de ACO dans la PPC pour résoudre les CSP ont été publiés dans [KAS08a,KAS07,KAS08b,KAS08c].…”

Section: Nos Contributionsunclassified