Towards Balanced Coevolution

Paredis, Jan

doi:10.1007/3-540-45356-3_49

Cited by 6 publications

(1 citation statement)

References 5 publications

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“…Some popular representations for solving JSPs are operation based, job based, preference-list based, priority-rule based, and job pair-relation based representations (Ponnambalam et al, 2001). We select the job pair-relation based genotype representation due to the flexibility of applying genetic operators (Nakano & Yamada, 1991; Paredis, 1992; Yamada & Nakano, 1997; Yamada, 2003). In this representation, a chromosome is symbolized by a binary string, where each bit stands for the order of a job pair ( u , v ) for a particular machine m .…”

Section: Job Scheduling With a Gamentioning

confidence: 99%

Managing risk in production scheduling under uncertain disruption

Sarker

Essam

Hasan

et al. 2015

AIEDAM

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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.

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Section: Job Scheduling With a Gamentioning

confidence: 99%