Single Machine Models (Stochastic)

Michael L. Pinedo ()
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Michael L. Pinedo: NYU Stern School of Business, Department of Technology, Operations, and Statistics

Chapter Chapter 10 in Scheduling, 2022, pp 271-297 from Springer

Abstract: Abstract Stochastic models, especially with exponential processing times, may often contain more structure than their deterministic counterparts and may lead to results which, at first sight, seem surprising. Models that are NP-hard in a deterministic setting often allow a simple priority policy to be optimal in a stochastic setting. In this chapter single machine models with arbitrary processing times in a nonpreemptive setting are discussed first. Then the preemptive cases are considered, followed by models where the processing times are likelihood ratio ordered. Finally, models with exponentially distributed processing times are analyzed.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-05921-6_10

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DOI: 10.1007/978-3-031-05921-6_10

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