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Multi-objective inventory routing problem: A stochastic model to consider profit, service level and green criteria

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  • Rahimi, Mohammad
  • Baboli, Armand
  • Rekik, Yacine

Abstract

The Inventory Routing Problem has been mainly studied in recent decades under an economic performance perspective. In this paper, we develop a multi-objective mathematical framework for the IRP to link: (i) the economic performance, (ii) the achieved server level in terms of shortage and delivery delays and (iii) the environmental footprint. The framework developed addresses the uncertainty by considering fuzzy distributions for certain problem inputs, such as the demand and the transportation costs. We show the negative impact on the economic performance when service level targets are exogenously chosen without coordination with the logistics components (inventory and distribution).

Suggested Citation

  • Rahimi, Mohammad & Baboli, Armand & Rekik, Yacine, 2017. "Multi-objective inventory routing problem: A stochastic model to consider profit, service level and green criteria," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 101(C), pages 59-83.
    • Handle: RePEc:eee:transe:v:101:y:2017:i:c:p:59-83
    DOI: 10.1016/j.tre.2017.03.001
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    Cited by:

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    2. Mohammad Asghari & Seyed Mohammad Javad Mirzapour Al-E-Hashem, 2021. "Green vehicle routing problem: A state-of-the-art review," Post-Print hal-03182944, HAL.
    3. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).
    4. Song, Ruidian & Zhao, Lei & Van Woensel, Tom & Fransoo, Jan C., 2019. "Coordinated delivery in urban retail," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 122-148.
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    6. Fokkema, Jan Eise & Land, Martin J. & Coelho, Leandro C. & Wortmann, Hans & Huitema, George B., 2020. "A continuous-time supply-driven inventory-constrained routing problem," Omega, Elsevier, vol. 92(C).
    7. Raj Kumar Bachar & Shaktipada Bhuniya & Santanu Kumar Ghosh & Biswajit Sarkar, 2022. "Controllable Energy Consumption in a Sustainable Smart Manufacturing Model Considering Superior Service, Flexible Demand, and Partial Outsourcing," Mathematics, MDPI, vol. 10(23), pages 1-29, November.
    8. A. Mor & M. G. Speranza, 2020. "Vehicle routing problems over time: a survey," 4OR, Springer, vol. 18(2), pages 129-149, June.
    9. Micheli, Guido J.L. & Mantella, Fabio, 2018. "Modelling an environmentally-extended inventory routing problem with demand uncertainty and a heterogeneous fleet under carbon control policies," International Journal of Production Economics, Elsevier, vol. 204(C), pages 316-327.
    10. Markov, Iliya & Bierlaire, Michel & Cordeau, Jean-François & Maknoon, Yousef & Varone, Sacha, 2018. "A unified framework for rich routing problems with stochastic demands," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 213-240.
    11. A. Mor & M. G. Speranza, 2022. "Vehicle routing problems over time: a survey," Annals of Operations Research, Springer, vol. 314(1), pages 255-275, July.
    12. Prem Vrat & Rachita Gupta & Aman Bhatnagar & Devendra Kumar Pathak & Vijayta Fulzele, 2018. "Literature review analytics (LRA) on sustainable cold-chain for perishable food products: research trends and future directions," OPSEARCH, Springer;Operational Research Society of India, vol. 55(3), pages 601-627, November.
    13. Touzout, Faycal A. & Ladier, Anne-Laure & Hadj-Hamou, Khaled, 2022. "An assign-and-route matheuristic for the time-dependent inventory routing problem," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1081-1097.
    14. Thays A. Oliveira & Yuri B. Gabrich & Helena Ramalhinho & Miquel Oliver & Miri W. Cohen & Luiz S. Ochi & Serigne Gueye & Fábio Protti & Alysson A. Pinto & Diógenes V. M. Ferreira & Igor M. Coelho & Vi, 2020. "Mobility, Citizens, Innovation and Technology in Digital and Smart Cities," Future Internet, MDPI, vol. 12(2), pages 1-27, January.
    15. Ji, Ying & Du, Jianhui & Han, Xiaoya & Wu, Xiaoqing & Huang, Ripeng & Wang, Shilei & Liu, Zhimin, 2020. "A mixed integer robust programming model for two-echelon inventory routing problem of perishable products," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    16. De, Manoranjan & Giri, B.C., 2020. "Modelling a closed-loop supply chain with a heterogeneous fleet under carbon emission reduction policy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    17. 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.
    18. Gao, Jingzhe & Xiao, Zhongdong & Cao, Binbin & Chai, Qiangfei, 2018. "Green supply chain planning considering consumer’s transportation process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 311-330.
    19. Liu, Ming & Liu, Xin & Chu, Feng & Zheng, Feifeng & Chu, Chengbin, 2019. "Distributionally robust inventory routing problem to maximize the service level under limited budget," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 190-211.
    20. Janssen, Larissa & Diabat, Ali & Sauer, Jürgen & Herrmann, Frank, 2018. "A stochastic micro-periodic age-based inventory replenishment policy for perishable goods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 445-465.
    21. 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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