An Effective Multi-Population Based Hybrid Genetic Algorithm for Job Shop Scheduling Problem

Abstract

The job shop scheduling problem is a well known practical planning problem in the manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this paper, a multi-population based hybrid genetic algorithm is developed for solving the JSSP. The population is divided in several groups at first and the hybrid algorithm is applied to the disjoint groups. Then the migration operator is used. The proposed approach, MP-HGA, have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 15 benchmark problems and compared results obtained with a number of algorithms established in the literature. The experimental results show that MP-HGA could gain the best known makespan in 13 out of 15 problems.