Solving a Novel Multi-Objective Scheduling Problem in a Cellular Manufacturing System by a Hybrid Algorithm

Abstract

In this paper a novel mathematical model has been proposed for multi-objective scheduling problem in a cellular manufacturing system (CMS) with the aim of minimizing the maximum completion time of jobs (i.e., makespan or Cmax), the earliness cost and the tardiness cost. Due to the complexity of such a hard problem, a hybrid algorithm, based on a genetic algorithm (GA) and particle swarm optimization (PSO), has been proposed to solve the presented model in a reasonable computational time. Furthermore, non-dominated sorting genetic algorithm (NSGA-II) as a well-known multi-objective evolutionary algorithm has been used to analyze and highlight the efficiency of the proposed hybrid algorithm. The associated results of the algorithms have been compared and analyzed. Finally, conclusions have been made and suggestions for further study have been presented.

Keywords

• Multi-objective scheduling problem
• Cellular manufacturing system
• Genetic algorithm
• Particle swarm optimization
• NSGA-II