Solving the Job-Shop Scheduling Problem Based on Cellular Genetic Algorithm

Abstract

Cellular genetic algorithm (cGA) is a subclass of genetic algorithm (GA) in which the population diversity and exploration are enhanced thanks to the existence of small overlapped neighborhoods. Such a kind of structured algorithms is specially well suited for complex problems. Shop scheduling problem is a kind of problem with practical significance, and it belongs to a combinational optimization problem called NP-hard problem. In this paper we establish the model of job-shop problem (JSP) and solve the job-shop scheduling problem with cGA and traditional genetic algorithms (sGA).From the experimental results and analysis, we find cGA has better search efficiency and convergence performance than sGA.