Solving flexible job-shop scheduling problem using hybrid particle swarm optimisation algorithm and data mining

S. Karthikeyan, P. Asokan, S. Nickolas and Tom Page

International Journal of Manufacturing Technology and Management, 2012, vol. 26, issue 1/2/3/4, 81-103

Abstract: Flexible job-shop scheduling problem (FJSSP) is an extension of the classical job-shop scheduling problem that allows an operation to be processed by any machine from a given set along different routes. It is very important in both fields of production management and combinatorial optimisation. This paper presents a new approach based on a hybridisation of the particle swarm optimisation (PSO) algorithm with data mining (DM) technique to solve the multi-objective flexible job-shop scheduling problem. Three minimisation objectives - the maximum completion time, the total workload of machines and the workload of the critical machines are considered simultaneously. In this study, PSO is used to assign operations and to determine the processing order of jobs on machines. The objectives are optimised by data mining technique which extracts the knowledge from the solution sets to find the near optimal solution of combinatorial optimisation problems. The computational results have shown that the proposed method is a feasible and effective approach for the multi-objective flexible job-shop scheduling problems.

Keywords: flexible scheduling; job shop scheduling; FJSP; particle swarm optimisation; PSO; data mining; multi-objective optimisation; attribute-oriented induction. (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=51445 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmtma:v:26:y:2012:i:1/2/3/4:p:81-103

Access Statistics for this article

More articles in International Journal of Manufacturing Technology and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().