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Capacitated disassembly scheduling under stochastic yield and demand.

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Τίτλος: Capacitated disassembly scheduling under stochastic yield and demand.
Συγγραφείς: Liu, Kanglin1, Zhang, Zhi-Hai1 zhzhang@tsinghua.edu.cn
Πηγή: European Journal of Operational Research. Aug2018, Vol. 269 Issue 1, p244-257. 14p.
Θεματικοί όροι: *Remanufacturing, *Supply & demand, *Stochastic analysis, *Nonlinear programming, *Approximation theory, Disassemblers (Computer programs)
Περίληψη: The disassembly process in remanufacturing has attracted increasing attention in recent years due to the high importance of environmental issues. The paper studies a capacitated single-item multi-period disassembly scheduling problem with random yields and demands in which procured, returned items (root items) are disassembled into components or parts (leaf items) to satisfy their demands in each period. The problem is formulated as a mixed integer nonlinear program (MINLP). Notably, a chance constraint is introduced to ensure that the probability of satisfying the demand is greater than a predetermined service level, and then is approximated by a second-order cone constraint. An outer approximation (OA) based solution algorithm is proposed to solve the resulting model. Furthermore, a special case that has uniformly-distributed yield and normally-distributed demand is considered given a closed-form formulation. Extensive numerical experiments demonstrate that the proposed algorithm can achieve converged optimal solutions within much less CPU time compared with the well-known solver BONMIN. Furthermore, a scheduling process of a valve producer is also conducted to demonstrate the application in practice. In the end, managerial insights are explored and future research directions are outlined. [ABSTRACT FROM AUTHOR]
Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Capacitated disassembly scheduling under stochastic yield and demand.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Kanglin%22">Liu, Kanglin</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Zhi-Hai%22">Zhang, Zhi-Hai</searchLink><relatesTo>1</relatesTo><i> zhzhang@tsinghua.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Operational+Research%22">European Journal of Operational Research</searchLink>. Aug2018, Vol. 269 Issue 1, p244-257. 14p.
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  Data: *<searchLink fieldCode="DE" term="%22Remanufacturing%22">Remanufacturing</searchLink><br />*<searchLink fieldCode="DE" term="%22Supply+%26+demand%22">Supply & demand</searchLink><br />*<searchLink fieldCode="DE" term="%22Stochastic+analysis%22">Stochastic analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Nonlinear+programming%22">Nonlinear programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Approximation+theory%22">Approximation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Disassemblers+%28Computer+programs%29%22">Disassemblers (Computer programs)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The disassembly process in remanufacturing has attracted increasing attention in recent years due to the high importance of environmental issues. The paper studies a capacitated single-item multi-period disassembly scheduling problem with random yields and demands in which procured, returned items (root items) are disassembled into components or parts (leaf items) to satisfy their demands in each period. The problem is formulated as a mixed integer nonlinear program (MINLP). Notably, a chance constraint is introduced to ensure that the probability of satisfying the demand is greater than a predetermined service level, and then is approximated by a second-order cone constraint. An outer approximation (OA) based solution algorithm is proposed to solve the resulting model. Furthermore, a special case that has uniformly-distributed yield and normally-distributed demand is considered given a closed-form formulation. Extensive numerical experiments demonstrate that the proposed algorithm can achieve converged optimal solutions within much less CPU time compared with the well-known solver BONMIN. Furthermore, a scheduling process of a valve producer is also conducted to demonstrate the application in practice. In the end, managerial insights are explored and future research directions are outlined. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.ejor.2017.08.032
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 244
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      – SubjectFull: Remanufacturing
        Type: general
      – SubjectFull: Supply & demand
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      – SubjectFull: Stochastic analysis
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      – SubjectFull: Nonlinear programming
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      – SubjectFull: Approximation theory
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      – SubjectFull: Disassemblers (Computer programs)
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              Text: Aug2018
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