Managing risk in production scheduling under uncertain disruption. Issue 3 (9th June 2015)

• Record Type: Journal Article
• Authors: Sarker, Ruhul; Essam, Daryl; Hasan, S.M. Kamrul; Karim, A.N. Mustafizul
• Editors: Eckert, Claudia; Jankovic, Marija
• Is Part Of: AI EDAM. Volume 30:Issue 3(2016)
• Page Start: 289
• Page End: 299
• Publication Date: 2015-06-09
• Subjects: Disruption, -- Genetic Algorithm, -- Job Scheduling, -- Memetic Algorithm, -- Risk Analysis; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060415000323
• Languages: English
• ISSNs: 0890-0604

Abstract

Abstract: The job scheduling problem (JSP) is considered as one of the most complex combinatorial optimization problems. JSP is not an independent task, but is rather a part of a company business case. In this paper, we have studied JSPs under sudden machine breakdown scenarios that introduce a risk of not completing the jobs on time. We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.