An effective hybrid algorithm for multi-objective flexible job-shop scheduling problem

Xiabao Huang,Lixi Yang,Zailin Guan

doi:10.1177/1687814018801442

Abstract

Genetic algorithm is one of primary algorithms extensively used to address the multi-objective flexible job-shop scheduling problem. However, genetic algorithm converges at a relatively slow speed. By hybridizing genetic algorithm with particle swarm optimization, this article proposes a teaching-and-learning-based hybrid genetic-particle swarm optimization algorithm to address multi-objective flexible job-shop scheduling problem. The proposed algorithm comprises three modules: genetic algorithm, bi-memory learning, and particle swarm optimization. A learning mechanism is incorporated into genetic algorithm, and therefore, during the process of evolution, the offspring in genetic algorithm can learn the characteristics of elite chromosomes from the bi-memory learning. For solving multi-objective flexible job-shop scheduling problem, this study proposes a discrete particle swarm optimization algorithm. The population is partitioned into two subpopulations for genetic algorithm module and particle swarm optimization module. These two algorithms simultaneously search for solutions in their own subpopulations and exchange the information between these two subpopulations, such that both algorithms can complement each other with advantages. The proposed algorithm is evaluated on some instances, and experimental results demonstrate that the proposed algorithm is an effective method for multi-objective flexible job-shop scheduling problem.

Highlights

Flexible job-shop scheduling problem (FJSP) is an extension and generalization of the classical job-shop scheduling problem (JSP).[1,2,3] Generally, in a classical job-shop model, all jobs follow fixed routes, but not necessarily the same for each job, and every operation of all jobs is required to be processed on one predetermined machine.[4,5] JSP determines a sequence of operations on each machine to optimize one or multiple objectives
Considering that genetic algorithm (GA) and particle swarm optimization (PSO) are different in terms of optimization mechanism, information sharing scheme, and capabilities in explorative search and the exploitative search, and GA and PSO have achieved promising results by combining another algorithm,[10,15,19] it is meaningful to investigate the hybridization of GA and PSO to solve multi-objective flexible job-shop scheduling problem (MOFJSP)
We present the notations as well as the details of the mathematical formulation of MOFJSP in section ‘‘Problem statement and mathematical formulation.’’ The framework of the proposed approach is described in section ‘‘Teaching–learning-based hybrid geneticparticle swarm optimization algorithm for MOFJSP.’’ Section ‘‘Computational results’’ deals with a comparative study of the computational results

Summary

Introduction

Flexible job-shop scheduling problem (FJSP) is an extension and generalization of the classical job-shop scheduling problem (JSP).[1,2,3] Generally, in a classical job-shop model, all jobs follow fixed routes, but not necessarily the same for each job, and every operation of all jobs is required to be processed on one predetermined machine.[4,5] JSP determines a sequence of operations on each machine to optimize one or multiple objectives. Considering that GA and PSO are different in terms of optimization mechanism, information sharing scheme, and capabilities in explorative search and the exploitative search, and GA and PSO have achieved promising results by combining another algorithm,[10,15,19] it is meaningful to investigate the hybridization of GA and PSO to solve MOFJSP. In this context, considering the advantages and limitations of GA and PSO, this study aims to develop a novel hybrid GA with PSO to address MOFJSP, which hybridizes both algorithms to complement each other. Pijh Processing time of the operation Ojh by the machine Mi Sjh Starting time of the operation Ojh (release time)

Objective functions

Conclusion and future research directions