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Crowdsource-enabled integrated production and transportation scheduling for smart city logistics.

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Title: Crowdsource-enabled integrated production and transportation scheduling for smart city logistics.
Authors: Feng, Xin1 (AUTHOR), Chu, Feng2,3 (AUTHOR) feng.chu@univ-evry.fr, Chu, Chengbin4 (AUTHOR), Huang, Yufei5 (AUTHOR)
Source: International Journal of Production Research. Apr2021, Vol. 59 Issue 7, p2157-2176. 20p. 1 Black and White Photograph, 4 Diagrams, 7 Charts, 2 Graphs.
Subjects: Production scheduling, Transportation schedules, Problem solving, Smart cities, Genetic algorithms, Logistics
Abstract: With city logistics becoming more and more important, increasing attention has been paid to the 'last-mile delivery' in urban areas. We investigate a novel crowdsource-enabled integrated production and transportation scheduling problem in the paper. The problem is first formulated into a mixed-integer linear program and its strong NP-hardness is proved. To better understand this complex problem, two sub-problems: a production and transportation scheduling problem and a crowdsourced bid selection problem are analysed. Based on problem properties, a Genetic Algorithm (GA) and a lower bound (LB) are developed to solve the original problem. Experimental results with up to 100 customers show that the GA outperforms the well-known commercial MIP solver CPLEX. Especially, (1) the GA can yield near-optimal solutions for all the tested instances with an average gap of 10.17% from the lower bound, while CPLEX provides feasible solutions only for instances with no more than 30 customers; (2) the average computation time of the GA is only 0.93% of that required by CPLEX; Besides, sensitivity analysis demonstrates advantages of introducing crowdsourced delivery into city logistics. [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.)
Database: Engineering Source
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DbLabel: Engineering Source
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  Label: Title
  Group: Ti
  Data: Crowdsource-enabled integrated production and transportation scheduling for smart city logistics.
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Feng%2C+Xin%22">Feng, Xin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chu%2C+Feng%22">Chu, Feng</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> feng.chu@univ-evry.fr</i><br /><searchLink fieldCode="AR" term="%22Chu%2C+Chengbin%22">Chu, Chengbin</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Yufei%22">Huang, Yufei</searchLink><relatesTo>5</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Apr2021, Vol. 59 Issue 7, p2157-2176. 20p. 1 Black and White Photograph, 4 Diagrams, 7 Charts, 2 Graphs.
– Name: Subject
  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Production+scheduling%22">Production scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Transportation+schedules%22">Transportation schedules</searchLink><br /><searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+cities%22">Smart cities</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Logistics%22">Logistics</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With city logistics becoming more and more important, increasing attention has been paid to the 'last-mile delivery' in urban areas. We investigate a novel crowdsource-enabled integrated production and transportation scheduling problem in the paper. The problem is first formulated into a mixed-integer linear program and its strong NP-hardness is proved. To better understand this complex problem, two sub-problems: a production and transportation scheduling problem and a crowdsourced bid selection problem are analysed. Based on problem properties, a Genetic Algorithm (GA) and a lower bound (LB) are developed to solve the original problem. Experimental results with up to 100 customers show that the GA outperforms the well-known commercial MIP solver CPLEX. Especially, (1) the GA can yield near-optimal solutions for all the tested instances with an average gap of 10.17% from the lower bound, while CPLEX provides feasible solutions only for instances with no more than 30 customers; (2) the average computation time of the GA is only 0.93% of that required by CPLEX; Besides, sensitivity analysis demonstrates advantages of introducing crowdsourced delivery into city logistics. [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: 10.1080/00207543.2020.1808258
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 20
        StartPage: 2157
    Subjects:
      – SubjectFull: Production scheduling
        Type: general
      – SubjectFull: Transportation schedules
        Type: general
      – SubjectFull: Problem solving
        Type: general
      – SubjectFull: Smart cities
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Logistics
        Type: general
    Titles:
      – TitleFull: Crowdsource-enabled integrated production and transportation scheduling for smart city logistics.
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            NameFull: Feng, Xin
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            NameFull: Chu, Feng
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            NameFull: Chu, Chengbin
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            NameFull: Huang, Yufei
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            – D: 01
              M: 04
              Text: Apr2021
              Type: published
              Y: 2021
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            – TitleFull: International Journal of Production Research
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