A matheuristic approach for the multi-level capacitated lot-sizing problem with substitution and backorder.

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Title: A matheuristic approach for the multi-level capacitated lot-sizing problem with substitution and backorder.
Authors: Qin, Hu1 (AUTHOR), Zhuang, Haocheng1 (AUTHOR), Yu, Chunlong2 (AUTHOR) chunlong_yu@tongji.edu.cn, Li, Jiliu3 (AUTHOR)
Source: International Journal of Production Research. Jul2024, Vol. 62 Issue 13, p4645-4673. 29p.
Subjects: Back orders, Production planning, Mathematical programming, Factorial experiment designs, Industrial capacity
Abstract: The lot-sizing problem aims at determining the products to be produced and their quantities for each time period, which is a difficult problem in production planning. This problem becomes even more complicated when practical aspects such as limited production capacity, bill of materials, and item substitution are considered. In this paper, we study a new variant of the lot-sizing problem, called the multi-level capacitated lot-sizing problem with substitution and backorder. Unlike previous studies, this variant considers substitutions at both the product and component levels, which is based on the real needs of manufacturers to increase planning flexibility. Backorders are allowed, but should be delivered within a certain time limitation. We formulate this problem using a mathematical programming model. A matheuristic approach is proposed to solve the problem. This first generates an initial feasible solution using a relax-and-fix algorithm, and then improves it using a hybrid fix-and-optimise algorithm. The proposed algorithm is calibrated with a full factorial design of experiments, and its efficiency is well validated. Finally, through extensive numerical experiments, we analyse the properties of this new lot-sizing problem, such as the effect of substitution options, and the influence of backorder time limitation, and provide several useful managerial insights for manufacturing companies to save costs in production planning. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd 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: A matheuristic approach for the multi-level capacitated lot-sizing problem with substitution and backorder.
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  Data: <searchLink fieldCode="DE" term="%22Back+orders%22">Back orders</searchLink><br /><searchLink fieldCode="DE" term="%22Production+planning%22">Production planning</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+programming%22">Mathematical programming</searchLink><br /><searchLink fieldCode="DE" term="%22Factorial+experiment+designs%22">Factorial experiment designs</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+capacity%22">Industrial capacity</searchLink>
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  Data: The lot-sizing problem aims at determining the products to be produced and their quantities for each time period, which is a difficult problem in production planning. This problem becomes even more complicated when practical aspects such as limited production capacity, bill of materials, and item substitution are considered. In this paper, we study a new variant of the lot-sizing problem, called the multi-level capacitated lot-sizing problem with substitution and backorder. Unlike previous studies, this variant considers substitutions at both the product and component levels, which is based on the real needs of manufacturers to increase planning flexibility. Backorders are allowed, but should be delivered within a certain time limitation. We formulate this problem using a mathematical programming model. A matheuristic approach is proposed to solve the problem. This first generates an initial feasible solution using a relax-and-fix algorithm, and then improves it using a hybrid fix-and-optimise algorithm. The proposed algorithm is calibrated with a full factorial design of experiments, and its efficiency is well validated. Finally, through extensive numerical experiments, we analyse the properties of this new lot-sizing problem, such as the effect of substitution options, and the influence of backorder time limitation, and provide several useful managerial insights for manufacturing companies to save costs in production planning. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd 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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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/00207543.2023.2270076
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      – Code: eng
        Text: English
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        PageCount: 29
        StartPage: 4645
    Subjects:
      – SubjectFull: Back orders
        Type: general
      – SubjectFull: Production planning
        Type: general
      – SubjectFull: Mathematical programming
        Type: general
      – SubjectFull: Factorial experiment designs
        Type: general
      – SubjectFull: Industrial capacity
        Type: general
    Titles:
      – TitleFull: A matheuristic approach for the multi-level capacitated lot-sizing problem with substitution and backorder.
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            NameFull: Qin, Hu
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            NameFull: Zhuang, Haocheng
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            NameFull: Yu, Chunlong
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            NameFull: Li, Jiliu
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            – D: 01
              M: 07
              Text: Jul2024
              Type: published
              Y: 2024
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