Feature selection approach for evolving reactive scheduling policies for dynamic job shop scheduling problem using gene expression programming

Dispatching rules are one of the most widely applied methods for solving Dynamic Job Shop Scheduling problems (DJSSP) in real-world manufacturing systems. Hence, the automated design of effective rules has been an important subject in the scheduling literature for the past several years. High comput...

Full description

Saved in:
Bibliographic Details
Published in:International Journal of Production Research Vol. 61; no. 15; pp. 5029 - 5052
Main Authors: Shady, Salama, Kaihara, Toshiya, Fujii, Nobutada, Kokuryo, Daisuke
Format: Journal Article
Language:English
Published: London Taylor & Francis 03.08.2023
Informa UK Limited
Taylor & Francis LLC
Subjects:
Algorithms
Automation
discrete event simulation
Dispatching rules
dynamic job shop scheduling
Feature selection
Gene expression
gene expression programming
genetic programming
Job shop scheduling
Job shops
Lateness
Policies
Scheduling
Search process
System effectiveness
ISSN:0020-7543, 1366-588X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Dispatching rules are one of the most widely applied methods for solving Dynamic Job Shop Scheduling problems (DJSSP) in real-world manufacturing systems. Hence, the automated design of effective rules has been an important subject in the scheduling literature for the past several years. High computational requirements and difficulty in interpreting generated rules are limitations of literature methods. Also, feature selection approaches in the field of automated design of scheduling policies have been developed for the tree-based GP approach only. Therefore, the aim of this study is to propose a feature selection approach for the Gene Expression Programming (GEP) algorithm to evolve high-quality rules in simple structures with an affordable computational budget. This integration speeds up the search process by restricting the GP search space using the linear representation of the GEP algorithm and creates concise rules with only meaningful features using the feature selection approach. The proposed algorithm is compared with five algorithms and 30 rules from the literature under different processing conditions. Three performance measures are considered including total weighted tardiness, mean tardiness, and mean flow time. The results show that the proposed algorithm can generate smaller rules with high interpretability in a much shorter training time.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2022.2092041