Research on multi-agent genetic algorithm based on tabu search for the job shop scheduling problem


Autoři: Chong Peng aff001;  Guanglin Wu aff001;  T. Warren Liao aff002;  Hedong Wang aff001
Působiště autorů: School of Mechanical Engineering and Automation, Beihang University, Beijing, China aff001;  Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223182

Souhrn

The solution to the job shop scheduling problem (JSSP) is of great significance for improving resource utilization and production efficiency of enterprises. In this paper, in view of its non-deterministic polynomial properties, a multi-agent genetic algorithm based on tabu search (MAGATS) is proposed to solve JSSPs under makespan constraints. Firstly, a multi-agent genetic algorithm (MAGA) is proposed. During the process, a multi-agent grid environment is constructed based on characteristics of multi-agent systems and genetic algorithm (GA), and a corresponding neighbor interaction operator, a mutation operator based on neighborhood structure and a self-learning operator are designed. Then, combining tabu search algorithm with a MAGA, the algorithm MAGATS are presented. Finally, 43 benchmark instances are tested with the new algorithm. Compared with four other algorithms, the optimization performance of it is analyzed based on obtained test results. Effectiveness of the new algorithm is verified by analysis results.

Klíčová slova:

Agent-based modeling – Algorithms – Built structures – Evolutionary genetics – Islands – Optimization – Genetic algorithms – Insertion mutation


Zdroje

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PLOS One


2019 Číslo 9

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