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Carbon footprint inventory route planning and selection of hot spot suppliers

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  • Kuo, Tsai Chi
  • Chen, Gary Yu-Hsin
  • Wang, Miao Ling
  • Ho, Ming Way

Abstract

In order to achieve the data accuracy on carbon emission from the suppliers, a complete carbon footprint inventory must be compiled at each supplier's site. Generally speaking, to collect the carbon emission inventory, data from various sources must be obtained, resulting in consumption of many resources from enterprises and suppliers. Therefore, to perform the compilation efficiently, a more systematic method for visiting suppliers is required. The carbon footprint inventory routing problem, based the vehicle routing problem (VRP), explores the selection of appropriate suppliers for inventory compilation after the carbon emission reaches a certain accuracy level and determination of the efficient carbon emission inventory route. In this study, the VRP is modified for the selection of the suppliers. Furthermore, by applying the sensitivity analysis, this study discusses the replacement of primary data by secondary data and development of the decision method that can be used to evaluate the route optimization, efficiency maximization, and cost minimization for carbon footprint inventory routing planning.

Suggested Citation

  • Kuo, Tsai Chi & Chen, Gary Yu-Hsin & Wang, Miao Ling & Ho, Ming Way, 2014. "Carbon footprint inventory route planning and selection of hot spot suppliers," International Journal of Production Economics, Elsevier, vol. 150(C), pages 125-139.
    • Handle: RePEc:eee:proeco:v:150:y:2014:i:c:p:125-139
    DOI: 10.1016/j.ijpe.2013.12.005
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    Cited by:

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    2. Tsai-Chi Kuo & Ming-Lang Tseng & Hsiao-Min Chen & Ping-Shun Chen & Po-Chen Chang, 2018. "Design and Analysis of Supply Chain Networks with Low Carbon Emissions," Computational Economics, Springer;Society for Computational Economics, vol. 52(4), pages 1353-1374, December.
    3. Hosseini, Seyedmohsen & Barker, Kash, 2016. "A Bayesian network model for resilience-based supplier selection," International Journal of Production Economics, Elsevier, vol. 180(C), pages 68-87.
    4. Xujing Zhang & Lichuan Wang & Yan Chen, 2019. "Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)," Sustainability, MDPI, vol. 11(9), pages 1-15, May.
    5. Tiwari, Anurag & Chang, Pei-Chann, 2015. "A block recombination approach to solve green vehicle routing problem," International Journal of Production Economics, Elsevier, vol. 164(C), pages 379-387.
    6. Qingjian Zhao & Zuomin Wen & Anne Toppinen, 2018. "Constructing the Embodied Carbon Flows and Emissions Landscape from the Perspective of Supply Chain," Sustainability, MDPI, vol. 10(11), pages 1-17, October.
    7. Shoufeng Ji & Qi Sun, 2017. "Low-Carbon Planning and Design in B&R Logistics Service: A Case Study of an E-Commerce Big Data Platform in China," Sustainability, MDPI, vol. 9(11), pages 1-27, November.
    8. Liu, Aijun & Zhu, Qiuyun & Xu, Lei & Lu, Qiang & Fan, Youqing, 2021. "Sustainable supply chain management for perishable products in emerging markets: An integrated location-inventory-routing model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    9. Lixia Li & Yu Yang & Gaoyuan Qin, 2019. "Optimization of Integrated Inventory Routing Problem for Cold Chain Logistics Considering Carbon Footprint and Carbon Regulations," Sustainability, MDPI, vol. 11(17), pages 1-22, August.

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