A Monte-Carlo tree search algorithm for the flexible job-shop scheduling in manufacturing systems

Flexible job-shop scheduling problem (FJSP) is an extension of the simple JSP with additional features of routing flexibility. It is an essential class of sequencing and planning problems that can apply in many real-life applications, especially in the field of manufacturing systems and production m...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Flexible services and manufacturing journal Jg. 35; H. 2; S. 548 - 571
Hauptverfasser: Saqlain, M., Ali, S., Lee, J. Y.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.06.2023
Springer Nature B.V
Schlagworte:
Algorithms
Completion time
Engineering
HyperText Markup Language
Job shop scheduling
Machines
Manufacturing
Operations Management
Operations Research/Decision Theory
Performance evaluation
Processes
Production management
Production scheduling
Scheduling
Search algorithms
Flexible job-shop scheduling
Production scheduling
Monte-Carlo tree search
Reinforcement learning
Smart manufacturing
ISSN:1936-6582, 1936-6590
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Flexible job-shop scheduling problem (FJSP) is an extension of the simple JSP with additional features of routing flexibility. It is an essential class of sequencing and planning problems that can apply in many real-life applications, especially in the field of manufacturing systems and production management. Finding a scheduling solution of sequential operations of various jobs by processing them on a defined number of machines and following various constraints with the goal to minimize the completion time of all operations, known as Makespan, is a big challenging issue. To address this issue, we proposed a Monte Carlo Tree Search-based flexible job-shop scheduling algorithm called MCTS-FJS for scheduling highly complex jobs in a real-time job-shop environment. An MCTS is a tree search technique aimed at making sequential decisions with uncertainty, calculate reward values from sub-tees, and regularly explore the most promising sub-tree. Experimental results showed that MCTS-scheduler outperformed various baseline scheduling algorithms and got the best evaluation performance for our sample dataset. More importantly, results showed that the performance of the proposed algorithm improved with increasing the number of jobs. Hence, this novel approach can be used to solve the complex FJSP in manufacturing systems.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1936-6582
1936-6590
DOI:10.1007/s10696-021-09437-4