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Stochastic fleet mix optimization: Evaluating electromobility in urban logistics

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  • Malladi, Satya S.
  • Christensen, Jonas M.
  • Ramírez, David
  • Larsen, Allan
  • Pacino, Dario

Abstract

In this paper, we study the problem of optimizing the size and mix of a mixed fleet of electric and conventional vehicles owned by firms providing urban freight logistics services. Uncertain customer requests are considered at the strategic planning stage. These requests are revealed before operations commence in each operational period. At the operational level, a new model for vehicle power consumption is suggested. In addition to mechanical power consumption, this model accounts for cabin climate control power, which is dependent on ambient temperature, and auxiliary power, which accounts for energy drawn by external devices. We formulate the problem of stochastic fleet size and mix optimization as a two-stage stochastic program and propose a sample average approximation based heuristic method to solve it. An adaptive large neighborhood search algorithm is used for each operational period to determine the operational decisions and associated costs. The applicability of the approach is demonstrated through two case studies within urban logistics services.

Suggested Citation

  • Malladi, Satya S. & Christensen, Jonas M. & Ramírez, David & Larsen, Allan & Pacino, Dario, 2022. "Stochastic fleet mix optimization: Evaluating electromobility in urban logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:transe:v:158:y:2022:i:c:s1366554521003124
    DOI: 10.1016/j.tre.2021.102554
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    1. Mengke Li & Yongkui Shi & Bobin Zhu, 2022. "Research on Multi-Center Mixed Fleet Distribution Path Considering Dynamic Energy Consumption Integrated Reverse Logistics," Sustainability, MDPI, vol. 14(11), pages 1-27, May.

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