A hybrid many-objective evolutionary algorithm for flexible job-shop scheduling problem with transportation and setup times

•A hybrid many-objective evolutionary algorithm (HMEA) is proposed for the MaOFJSP_T/S.•A new mathematical model and network graph model are formulated.•A neighborhood structure with four move types is defined.•Extensive numerical experiments on 28 benchmarks have confirmed the effectiveness of the...

Full description

Saved in:
Bibliographic Details
Published in:Computers & operations research Vol. 132; p. 105263
Main Authors: Sun, Jinghe, Zhang, Guohui, Lu, Jiao, Zhang, Wenqiang
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.08.2021
Pergamon Press Inc
Subjects:
Evolutionary algorithms
Flexible job-shop scheduling problem
Genetic algorithms
Job shop scheduling
Job shops
Many-objective evolutionary algorithm
Mathematical models
Neighborhood structure
Neighborhoods
Operations research
Reference-point
Setup times
Sorting algorithms
Tabu search
Transportation
Transportation and setup times
Workload
Workloads
Reference-point
Flexible job-shop scheduling problem
Transportation and setup times
Many-objective evolutionary algorithm
Neighborhood structure
ISSN:0305-0548, 0305-0548
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•A hybrid many-objective evolutionary algorithm (HMEA) is proposed for the MaOFJSP_T/S.•A new mathematical model and network graph model are formulated.•A neighborhood structure with four move types is defined.•Extensive numerical experiments on 28 benchmarks have confirmed the effectiveness of the HMEA. This paper addresses a many-objective flexible job-shop scheduling problem with transportation and setup times (MaOFJSP_T/S) where the objective is to minimize the makespan, total workload, workload of the critical machine, and penalties of earliness/tardiness. We first present a mathematical model as the representation of the problem, and then establish a network graph model to describe the structural characteristics of the problem and develop a new neighborhood structure. The neighborhood structure defines four move types for different objectives. Next, we propose a hybrid many-objective evolutionary algorithm (HMEA), which is designed to better balance exploitation and exploration. In this algorithm, the tabu search with the neighborhood structure is proposed to improve the local search ability. A reference-point based non-dominated sorting selection is presented to guide the algorithm to search towards the Pareto-optimal front and maintain diversity of solutions. Through three sets of experiments based on 28 benchmark instances, the partial and overall effects of this algorithm are evaluated. The experimental results demonstrate the effectiveness of the proposed HMEA in solving the MaOFJSP_T/S.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0305-0548
0305-0548
DOI:10.1016/j.cor.2021.105263