Recent Advances in Multiobjective Genetic Algorithms for Manufacturing Scheduling Problems

Abstract

Manufacturing scheduling is one of the important and complex combinatorial optimization problems in manufacturing system, where it can have a major impact on the productivity of a production process. Moreover, most of scheduling problems fall into the class of NP-hard combinatorial problems. In this paper, we concern with the design of multiobjective genetic algorithms (MOGAs) to solve a variety of manufacturing scheduling problems. In particularly, the fitness assignment mechanism and evolutionary representations as well as the hybrid evolutionary operations are introduced. Also, several applications of EAs to the different types of manufacturing scheduling problems are illustrated. Through a variety of numerical experiments, the effectiveness of these hybrid genetic algorithms (HGAs) in the widely applications of manufacturing scheduling problems are demonstrated. This paper also summarizes a classification of scheduling problems and the design way of GAs for the different types of manufacturing scheduling problems in which we apply GAs to a multiobjective flexible job-shop scheduling problem (MoFJSP; operation sequencing with resources assignment) and multiobjective assembly line balancing models (MoALB; shipments grouping and assignment). It is useful to guide how to investigate an effective GA for the practical manufacturing scheduling problems.