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Joint Optimization of Distribution Network Design and Two-Echelon Inventory Control with Stochastic Demand and CO2 Emission Tax Charges

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  • Shuangyan Li
  • Xialian Li
  • Dezhi Zhang
  • Lingyun Zhou

Abstract

This study develops an optimization model to integrate facility location and inventory control for a three-level distribution network consisting of a supplier, multiple distribution centers (DCs), and multiple retailers. The integrated model addressed in this study simultaneously determines three types of decisions: (1) facility location (optimal number, location, and size of DCs); (2) allocation (assignment of suppliers to located DCs and retailers to located DCs, and corresponding optimal transport mode choices); and (3) inventory control decisions on order quantities, reorder points, and amount of safety stock at each retailer and opened DC. A mixed-integer programming model is presented, which considers the carbon emission taxes, multiple transport modes, stochastic demand, and replenishment lead time. The goal is to minimize the total cost, which covers the fixed costs of logistics facilities, inventory, transportation, and CO2 emission tax charges. The aforementioned optimal model was solved using commercial software LINGO 11. A numerical example is provided to illustrate the applications of the proposed model. The findings show that carbon emission taxes can significantly affect the supply chain structure, inventory level, and carbon emission reduction levels. The delay rate directly affects the replenishment decision of a retailer.

Suggested Citation

  • Shuangyan Li & Xialian Li & Dezhi Zhang & Lingyun Zhou, 2017. "Joint Optimization of Distribution Network Design and Two-Echelon Inventory Control with Stochastic Demand and CO2 Emission Tax Charges," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-22, January.
    • Handle: RePEc:plo:pone00:0168526
    DOI: 10.1371/journal.pone.0168526
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    References listed on IDEAS

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    2. Wei Yu & Ruizhu Han, 2017. "Coordinating a Two-Echelon Supply Chain under Carbon Tax," Sustainability, MDPI, vol. 9(12), pages 1-13, December.
    3. Kai Kang & Wei Pu & Yanfang Ma & Xiaoyu Wang, 2018. "Bi-objective inventory allocation planning problem with supplier selection and carbon trading under uncertainty," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-25, November.
    4. Barros, Júlio & Cortez, Paulo & Carvalho, M. Sameiro, 2021. "A systematic literature review about dimensioning safety stock under uncertainties and risks in the procurement process," Operations Research Perspectives, Elsevier, vol. 8(C).
    5. Pintarič, Zorka Novak & Varbanov, Petar Sabev & Klemeš, Jiří Jaromír & Kravanja, Zdravko, 2019. "Multi-objective multi-period synthesis of energy efficient processes under variable environmental taxes," Energy, Elsevier, vol. 189(C).
    6. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    7. Khouloud Dorgham & Issam Nouaouri & Jean-Christophe Nicolas & Gilles Goncalves, 2022. "Collaborative hospital supply chain network design problem under uncertainty," Operational Research, Springer, vol. 22(5), pages 4607-4640, November.
    8. Yi Zhao & Ronghui Liu & Xi Zhang & Anthony Whiteing, 2018. "A chance-constrained stochastic approach to intermodal container routing problems," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-22, February.

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