《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

Introduction

International Standard Serial Number:

ISSN 1001-4551

Sponsor:

Zhejiang University;

Zhejiang Machinery and Electrical Group

Edited by:

Editorial of Journal of Mechanical & Electrical Engineering

Chief Editor:

ZHAO Qun

Vice Chief Editor:

TANG ren-zhong,

LUO Xiang-yang

Tel:

86-571-87041360,87239525

Fax:

86-571-87239571

Add:

No.9 Gaoguannong,Daxue Road,Hangzhou,China

P.C:

310009

E-mail:

meem_contribute@163.com

Low carbon flexible job shop scheduling based on improved multiobjective jellyfish search algorithm

Published:2023-09-20 author:ZHANG Liang, WANG Ni-ya, MAO Jian-lin, et al. Browse: 345 Check PDF documents

Low carbon flexible job shop scheduling based on

improved multi-objective jellyfish search algorithm

ZHANG Liang1, WANG Ni-ya1, MAO Jian-lin1, LI Rui-qi2

(1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;

2.Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China)

Abstract: Aiming at the problem that multiple objectives in the flexible job shop scheduling problem were conflicting and difficult to solve, a flexible job shop scheduling method was designed based on Pareto and multiobjective improved jellyfish algorithm. First, a flexible job shop scheduling model was established to minimize the maximum completion time, minimum energy consumption and minimum total machine load. Second, an initialization method based on the uniform distribution of completion time and machine load was used to effectively improve the quality of the initial population. Considering the low efficiency of continuous jellyfish algorithm in solving discrete problems, a discrete jellyfish algorithm was designed, and a passive motion method based on Pareto level was proposed to further improve the search efficiency. The concept of learning was introduced to guide the population to move to the Pareto front. Finally, in order to verify the effectiveness of improved multi-objective jellyfish search (IMOJS) algorithm in solving flexible job shop scheduling, the simulation experiments were conducted to compare with NSGAII algorithm. The experimental results show that the algorithm was used to solve the NSGAII results which dominated by the MK06 example (completion time is 76, the machine load is 424, and the energy consumption is 565.8), and the quality of the solution is higher. The results show that the discrete jellyfish algorithm with uniform distribution initialization and learning guidance has uniform initial population distribution, fast convergence speed, high solution quality and strong optimization ability, which can effectively deal with multiobjective flexible job shop scheduling problem.

Key words: flexible job shop scheduling problem(FJSP); improved multiobjective jellyfish search(IMOJS) algorithm; variable neighborhood search; learning jellyfish search algorithm; Pareto; time control function

Advanced Search

Online Office System

Chinese Core Periodicals
Chinese Sci-tech Core Periodicals
SA, INSPEC Indexed
CSA: T Indexed
UPD:Indexed