A particle swarm-based genetic algorithm for scheduling in an agile environment

• Published in: Computers & industrial engineering Vol. 55; no. 3; pp. 707 - 720
• Main Authors: Gaafar, Lotfi K., Masoud, Sherif A., Nassef, Ashraf O.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.10.2008; Pergamon Press Inc
• Subjects: Agile manufacturing; Comparative analysis; Digraph; Factorial experiments; Genetic algorithms; Heuristic; Particle swarm optimization; Production scheduling; Scheduling; Studies; Agile manufacturing; Scheduling; Digraph; Particle swarm optimization; Genetic algorithms
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2008.01.020

In this paper, genetic algorithms are applied to the scheduling of a manufacturing system that is designed to support an assembly-driven differentiation strategy in the context of agile manufacturing. The system consists of a single flexible machine followed by multiple identical assembly stations. The objective of the scheduling problem is to minimize the makespan. A modified version of the genetic algorithm, inspired by the particle swarm optimization approach, is applied to the problem in addition to the general application of genetic algorithms. The objective is to investigate the potential that the particle swarm optimization concepts may have in improving the performance of genetic algorithms when applied to the chosen problem. The performance of these algorithms is compared to existing heuristics in the literature. A 2 3 factorial experiment, replicated twice, is used to compare the performance of the various approaches and identify the significant factors that affect the average percentage deviation from a lower bound. The results show that both versions of genetic algorithms applications outperform the existing heuristics in many instances and provide schedules that are shorter by as much as 15.5% in the cases considered. In addition, the modified application of genetic algorithms outperforms the regular application with shorter schedules by as much as 3.6% in many instances.