Single machine scheduling with stochastically dependent times

Wei Wei

doi:10.1007/s10951-019-00600-2

Single machine scheduling with stochastically dependent times

Research output: Contribution to journal › Article › peer-review

Abstract

This paper examines the behavior of several single machine scheduling problems when presented with times that are random and potentially dependent. A position-based learning effect model is revisited and optimal schedules are derived under several typical performance measures. A sum-of-processing-time based model is proposed to incorporate the learning effects and deteriorations in one unified framework. Optimal schedules are derived to minimize the maximum lateness or jointly minimize the completion times under the proposed model, and the model is used to solve the optimal issuing problem.

Original language	English (US)
Pages (from-to)	677-689
Number of pages	13
Journal	Journal of Scheduling
Volume	22
Issue number	6
DOIs	https://doi.org/10.1007/s10951-019-00600-2
State	Published - Dec 1 2019
Externally published	Yes

Keywords

Completion times
Dependence
Deteriorations/learning effects
Expected total cost
Maximum lateness
Stochastic scheduling

ASJC Scopus subject areas

Software
General Engineering
Management Science and Operations Research
Artificial Intelligence

Online availability

10.1007/s10951-019-00600-2

Library availability

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abstract = "This paper examines the behavior of several single machine scheduling problems when presented with times that are random and potentially dependent. A position-based learning effect model is revisited and optimal schedules are derived under several typical performance measures. A sum-of-processing-time based model is proposed to incorporate the learning effects and deteriorations in one unified framework. Optimal schedules are derived to minimize the maximum lateness or jointly minimize the completion times under the proposed model, and the model is used to solve the optimal issuing problem.",

keywords = "Completion times, Dependence, Deteriorations/learning effects, Expected total cost, Maximum lateness, Stochastic scheduling",

author = "Wei Wei",

note = "Funding Information: The author acknowledges valuable comments from the two anonymous reviewers, which greatly improve the presentation of this paper. The author also thanks the support by the Research and Creative Activities Support grant (Grant No. AAC2253) and the startup grant (Grant No. PRJ69VR) from the University of Wisconsin-Milwaukee. Funding Information: The author acknowledges valuable comments from the two anonymous reviewers, which greatly improve the presentation of this paper. The author also thanks the support by the Research and Creative Activities Support grant (Grant No. AAC2253) and the startup grant (Grant No. PRJ69VR) from the University of Wisconsin-Milwaukee. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Publisher Copyright: {\textcopyright} 2019, Springer Science+Business Media, LLC, part of Springer Nature.",

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AU - Wei, Wei

N1 - Funding Information: The author acknowledges valuable comments from the two anonymous reviewers, which greatly improve the presentation of this paper. The author also thanks the support by the Research and Creative Activities Support grant (Grant No. AAC2253) and the startup grant (Grant No. PRJ69VR) from the University of Wisconsin-Milwaukee. Funding Information: The author acknowledges valuable comments from the two anonymous reviewers, which greatly improve the presentation of this paper. The author also thanks the support by the Research and Creative Activities Support grant (Grant No. AAC2253) and the startup grant (Grant No. PRJ69VR) from the University of Wisconsin-Milwaukee. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Publisher Copyright: © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

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N2 - This paper examines the behavior of several single machine scheduling problems when presented with times that are random and potentially dependent. A position-based learning effect model is revisited and optimal schedules are derived under several typical performance measures. A sum-of-processing-time based model is proposed to incorporate the learning effects and deteriorations in one unified framework. Optimal schedules are derived to minimize the maximum lateness or jointly minimize the completion times under the proposed model, and the model is used to solve the optimal issuing problem.

AB - This paper examines the behavior of several single machine scheduling problems when presented with times that are random and potentially dependent. A position-based learning effect model is revisited and optimal schedules are derived under several typical performance measures. A sum-of-processing-time based model is proposed to incorporate the learning effects and deteriorations in one unified framework. Optimal schedules are derived to minimize the maximum lateness or jointly minimize the completion times under the proposed model, and the model is used to solve the optimal issuing problem.

KW - Completion times

KW - Dependence

KW - Deteriorations/learning effects

KW - Expected total cost

KW - Maximum lateness

KW - Stochastic scheduling

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Single machine scheduling with stochastically dependent times

Abstract

Keywords

ASJC Scopus subject areas

Online availability

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