An improved genetic algorithm with dynamic neighborhood search for job shop scheduling problem-Reference-Cited by-同舟云学术

An improved genetic algorithm with dynamic neighborhood search for job shop scheduling problem

Published:2023 Issue:9 Volume:20 Page:17407-17427
ISSN:1551-0018
Container-title:Mathematical Biosciences and Engineering
language:
Short-container-title:MBE

Author:

Hu Kongfu¹,Wang Lei¹,Cai Jingcao¹²,Cheng Long¹

Affiliation:

1. School of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China

2. AnHui Key Laboratory of Detection Technology and Energy Saving Devices, AnHui Polytechnic University, Wuhu 241000, China

Abstract

<abstract> <p>The job shop scheduling problem (JSP) has consistently garnered significant attention. This paper introduces an improved genetic algorithm (IGA) with dynamic neighborhood search to tackle job shop scheduling problems with the objective of minimization the makespan. An inserted operation based on idle time is introduced during the decoding phase. An improved POX crossover operator is presented. A novel mutation operation is designed for searching neighborhood solutions. A new genetic recombination strategy based on a dynamic gene bank is provided. The elite retention strategy is presented. Several benchmarks are used to evaluate the algorithm's performance, and the computational results demonstrate that IGA delivers promising and competitive outcomes for the considered JSP.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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