A beam search algorithm to optimize robustness under random machine breakdowns and processing time variability

Date
2007
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Proceedings of the 16th Industrial Engineering Research Conference, IERC 2007
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Publisher
Institute of Industrial Engineers
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Language
English
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Conference Paper
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Abstract

The vast majority of the machine scheduling research assumes complete information about the scheduling problem and a static environment in which scheduling systems operate. In practice, however, scheduling systems are subject to considerable uncertainty in dynamic environments. The ability to cope with the uncertainty in scheduling process is becoming increasingly important in today's highly dynamic and competitive business environments. In the literature, two approaches have appeared as the effective way: reactive and proactive scheduling. The objective in reactive scheduling is to revise schedules as necessary, while proactive scheduling attempts to incorporate future disruptions when generating schedules. In this paper we take a proactive scheduling approach to solve a machine scheduling problem with two sources of uncertainty: processing time variability and machine breakdowns. We define two robustness measures and develop a heuristic based on beam search methodology to optimize them. The computational results show that the proposed algorithms perform significantly better than a number of heuristics available in the literature.

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Proactive scheduling, Dynamical systems, Evolutionary algorithms, Heuristic methods, Heuristic programming, Industrial engineering, Industrial research, Integer programming, Learning algorithms, Machine shop practice, Optimization, Production control, Project management, Systems engineering, Beam search, Competitive business, Complete information, Computational results, Dynamic environments, Machine-scheduling problems, Reactive scheduling, Robustness measures, Scheduling problems, Scheduling process, Scheduling systems
Citation
Published Version (Please cite this version)