Evolutional Algorithm in Solving Flexible Job Shop Scheduling Problem with Uncertainties

Yahong Zheng (), Lian Lian (), Zaifeng Fu () and Khaled Mesghouni ()
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Yahong Zheng: Wuhan University of Technology
Lian Lian: Dalian University of Technology
Zaifeng Fu: Dalian University of Technology
Khaled Mesghouni: LAGIS, UMR CNRS 8219, EcoleCentraledeLille

A chapter in LISS 2013, 2015, pp 1009-1015 from Springer

Abstract: Abstract In recent years, the necessity of considering uncertainty in scheduling problem is recognized by many scholars and practitioners, but there are still not effective methods to deal with uncertainty. This paper focuses on the flexible job shop scheduling problem (FJSP). Uncertainties in FJSP includes many aspects, such as the urgently arrival jobs, the uncertain working condition of the machines, etc. In this paper, we propose an inserting algorithm (IA), which can be used to treat the necessary machine maintenance for reducing unavailability of machines. We use the condition based maintenance (CBM) to reduce unavailability of machines. A problem focused in this paper is the flexible job shop scheduling problem with preventive maintenance (FJSPPM). An inserting algorithm (IA) is utilized to add PM into a preschedule scheme of FJSP which is obtained through an evolutional algorithm. Furthermore, a new better solution for an instance in benchmark of FJSP is obtained.

Keywords: Condition based maintenance; Flexible job shop scheduling problem; Genetic algorithm; Ant colony optimization; Artificial bee algorithm (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-40660-7_151

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DOI: 10.1007/978-3-642-40660-7_151

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