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Solving a supply-chain management problem using a bilevel approach

Published: 01 July 2017 Publication History

Abstract

Supply-chain management problems are common to most industries and they involve a hierarchy of subtasks, which must be coordinated well to arrive at an overall optimal solution. Such problems involve a hierarchy of decision-makers, each having its own objectives and constraints, but importantly requiring a coordination of their actions to make the overall supply chain process optimal from cost and quality considerations. In this paper, we consider a specific supply-chain management problem from a company, which involves two levels of coordination: (i) yearly strategic planning in which a decision on establishing an association of every destination point with a supply point must be made so as to minimize the yearly transportation cost, and (ii) weekly operational planning in which, given the association between a supply and a destination point, a decision on the preference of available transport carriers must be made for multiple objectives: minimization of transport cost and maximization of service quality and satisfaction of demand at each destination point. We propose a customized multi-objective bilevel evolutionary algorithm, which is computationally tractable. We then present results on state-level and ZIP-level accuracy (involving about 40,000 upper level variables) of destination points over the mainland USA. We compare our proposed method with current non-optimization based practices and report a considerable cost saving.

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Cited By

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  • (2024)Evolutionary Bilevel Optimization: Algorithms and ApplicationsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3648409(1284-1312)Online publication date: 14-Jul-2024
  • (2020)A Fast Efficient Local Search-Based Algorithm for Multi-Objective Supply Chain Configuration ProblemIEEE Access10.1109/ACCESS.2020.29834738(62924-62931)Online publication date: 2020
  • (2018)Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row LayoutAlgorithms10.3390/a1112021011:12(210)Online publication date: 17-Dec-2018
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    cover image ACM Conferences
    GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference
    July 2017
    1427 pages
    ISBN:9781450349208
    DOI:10.1145/3071178
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    Published: 01 July 2017

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    Author Tags

    1. bi-level optimization
    2. large-scale optimization
    3. supply-chain management
    4. uncertainty handling

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    GECCO '17 Paper Acceptance Rate 178 of 462 submissions, 39%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    View all
    • (2024)Evolutionary Bilevel Optimization: Algorithms and ApplicationsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3648409(1284-1312)Online publication date: 14-Jul-2024
    • (2020)A Fast Efficient Local Search-Based Algorithm for Multi-Objective Supply Chain Configuration ProblemIEEE Access10.1109/ACCESS.2020.29834738(62924-62931)Online publication date: 2020
    • (2018)Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row LayoutAlgorithms10.3390/a1112021011:12(210)Online publication date: 17-Dec-2018
    • (2018)The Trigger Factors and Constraints on e-Supply Chain Processes: A Systematic Literature Review2018 International Conference on Information Management and Technology (ICIMTech)10.1109/ICIMTech.2018.8528136(501-505)Online publication date: Sep-2018

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