Service-oriented robust parallel machine scheduling

Stochastic scheduling optimisation is a hot and challenging research topic with wide applications. Most existing works on stochastic parallel machine scheduling address uncertain processing time, and assume that its probability distribution is known or can be correctly estimated. This paper investig...

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Vydáno v:International journal of production research Ročník 57; číslo 12; s. 3814 - 3830
Hlavní autoři: Liu, Ming, Liu, Xin, Chu, Feng, Zheng, Feifeng, Chu, Chengbin
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 18.06.2019
Taylor & Francis LLC
Témata:
ambiguous processing time
Computation
Computer Science
Covariance matrix
distributionally robust
Job shops
Mixed integer
Operations Research
Optimization
Probability theory
Production scheduling
Scheduling
service level
stochastic optimisation
Service level
Scheduling
Ambiguous processing time
Stochastic optimisation
Distributionally robust
ISSN:0020-7543, 1366-588X
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Abstract Stochastic scheduling optimisation is a hot and challenging research topic with wide applications. Most existing works on stochastic parallel machine scheduling address uncertain processing time, and assume that its probability distribution is known or can be correctly estimated. This paper investigates a stochastic parallel machine scheduling problem, and assumes that only the mean and covariance matrix of the processing times are known, due to the lack of historical data. The objective is to maximise the service level, which measures the probability of all jobs jointly completed before or at their due dates. For the problem, a new distributionally robust formulation is proposed, and two model-based approaches are developed: (1) a sample average approximation method is adapted, (2) a hierarchical approach based on mixed integer second-order cone programming (MI-SOCP) formulation is designed. To evaluate and compare the performance of the two approaches, randomly generated instances are tested. Computational results show that our proposed MI-SOCP-based hierarchical approach can obtain higher solution quality with less computational effect.
AbstractList Stochastic scheduling optimisation is a hot and challenging research topic with wide applications. Most existing works on stochastic parallel machine scheduling address uncertain processing time, and assume that its probability distribution is known or can be correctly estimated. This paper investigates a stochastic parallel machine scheduling problem, and assumes that only the mean and covariance matrix of the processing times are known, due to the lack of historical data. The objective is to maximise the service level, which measures the probability of all jobs jointly completed before or at their due dates. For the problem, a new distributionally robust formulation is proposed, and two model-based approaches are developed: (1) a sample average approximation method is adapted, (2) a hierarchical approach based on mixed integer second-order cone programming (MI-SOCP) formulation is designed. To evaluate and compare the performance of the two approaches, randomly generated instances are tested. Computational results show that our proposed MI-SOCP-based hierarchical approach can obtain higher solution quality with less computational effect.
Author Chu, Feng
Liu, Ming
Liu, Xin
Chu, Chengbin
Zheng, Feifeng
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  organization: School of Economics & Management, Tongji University
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  organization: School of Economics & Management, Tongji University
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  surname: Chu
  fullname: Chu, Feng
  email: feng.chu@univ-evry.fr
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  fullname: Zheng, Feifeng
  organization: Glorious Sun School of Business & Management, Donghua University
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  givenname: Chengbin
  surname: Chu
  fullname: Chu, Chengbin
  organization: Laboratoire Génie Industriel, Centrale Supélec, Université Paris-Saclay
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Issue 12
Keywords Service level
Scheduling
Ambiguous processing time
Stochastic optimisation
Distributionally robust
Language English
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SubjectTerms ambiguous processing time
Computation
Computer Science
Covariance matrix
distributionally robust
Job shops
Mixed integer
Operations Research
Optimization
Probability theory
Production scheduling
Scheduling
service level
stochastic optimisation
Title Service-oriented robust parallel machine scheduling
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