Solving the dual-resource constrained flexible job shop scheduling problem with learning effect by a hybrid genetic algorithm

• Published in: Advances in mechanical engineering Vol. 10; no. 10
• Main Authors: Wu, Rui, Li, Yibing, Guo, Shunsheng, Xu, Wenxiang
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.10.2018; Sage Publications Ltd; SAGE Publishing
• Subjects: Authorship; Crossovers; Genetic algorithms; Heuristic; Integer programming; Job shop scheduling; Job shops; Machine learning; Manufacturing; Mutation; Operators (mathematics); Optimization algorithms; Production scheduling; Resource scheduling; Searching; Variance analysis; Workers; Flexible job shop scheduling problem; genetic algorithm; dual-resource constrained; variable neighborhood search; learning ability
• ISSN: 1687-8132; 1687-8140
• DOI: 10.1177/1687814018804096

In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker’s learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a three-dimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.