A Multi-Agent Evolutionary Algorithm for Job Shop Scheduling with Operators - Couverture souple

Cuche Cartagena, Daniel Cesar

 
9786202300315: A Multi-Agent Evolutionary Algorithm for Job Shop Scheduling with Operators
Couverture souple
ISBN 10 : 6202300310 ISBN 13 : 9786202300315
Editeur : Scholars' Press, 2018

Synopsis

This study considers the job shop scheduling problem in which processing of an operation on a given machine has to be assisted by one of a limited number of operators, called job shop scheduling with operators. In this problem, besides determining the sequence of the jobs assigned to each machine, it is also needed to assign the jobs to operators and determine the sequence of the jobs assigned to each operator. After representing the problem using an extended disjunctive graph and providing an integer programming model for the objective of minimizing makespan, I suggest a multi-agent evolutionary algorithm that incorporates a new neighborhood generation method. To test the performance of the algorithm suggested in this study, computational experiments were done on benchmark instances, and the results show that the algorithm gives better solutions than the current best ones for the test instances in which the number of operators is up to around a half of the number of machines. In particular, the proposed algorithm gave the optimal solutions for most test instances with smaller number of operators.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

This study considers the job shop scheduling problem in which processing of an operation on a given machine has to be assisted by one of a limited number of operators, called job shop scheduling with operators. In this problem, besides determining the sequence of the jobs assigned to each machine, it is also needed to assign the jobs to operators and determine the sequence of the jobs assigned to each operator. After representing the problem using an extended disjunctive graph and providing an integer programming model for the objective of minimizing makespan, I suggest a multi-agent evolutionary algorithm that incorporates a new neighborhood generation method. To test the performance of the algorithm suggested in this study, computational experiments were done on benchmark instances, and the results show that the algorithm gives better solutions than the current best ones for the test instances in which the number of operators is up to around a half of the number of machines. In particular, the proposed algorithm gave the optimal solutions for most test instances with smaller number of operators.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Scholars' Press
Date d'édition
2018
Langue
anglais
ISBN 10 
6202300310
ISBN 13 
9786202300315
Reliure
Broché
Nombre de pages
52
Coordonnées du fabricant
non disponible
Personne responsable
non disponible