Integration of production planning and scheduling using an expert system and a genetic algorithm

A Ławrynowicz ()
Additional contact information
A Ławrynowicz: University of Technology and Life Sciences

Journal of the Operational Research Society, 2008, vol. 59, issue 4, 455-463

Abstract: Abstract In the traditional approaches, processes of planning and scheduling are done sequentially, where the process plan is determined before the actual scheduling is performed. This simple approach ignores the relationship between the scheduling and planning. Practical scheduling systems need to be able to react to significant real-time events within an acceptable response time and revise schedules appropriately. Therefore, the author proposes a new methodology with artificial intelligence to support production planning and scheduling in supply net. In this approach, the production planning problem is first solved, and then the scheduling problem is considered with the constraint of the solution. The approach is implemented as a combination of expert system and genetic algorithm. The research indicates that the new system yields better results in real-life supply net than using a traditional method. The results of experiments provide that the proposed genetic algorithm produces schedules with makespan that is average 21% better than the methods based on dispatching rules.

Keywords: production planning; scheduling; artificial intelligence (search for similar items in EconPapers)
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1057/palgrave.jors.2602423 Abstract (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:pal:jorsoc:v:59:y:2008:i:4:d:10.1057_palgrave.jors.2602423

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

DOI: 10.1057/palgrave.jors.2602423

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook

More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().