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<p>This paper introduces and solves the mixed fleet optimization problem with electric and fuel vehicles for distribution planning, when periodically repeating a given set of routes. This integrated problem determines fleet sizing and scheduling with the objective of minimizing the total cost of fleet, transportation, energy, carbon emissions, and inventory. We use comprehensive calculations of energy consumption and carbon emissions and formulate the mixed fleet optimization problem as a non-linear mixed integer program. Subsequently, analytical results for optimal route cycle time and vehicle type and insights on their effect on scheduling are derived. These findings inform our development of novel scheduling strategies within a powerful multi-start solution framework that effectively links cycle time adjustment and vehicle type optimization. Computational experiments demonstrate the strong performance of our approach and yield valuable insights into the complex cost trade-offs involved. Finally, we evaluate the economic benefits of adopting a m3ixed fleet and investigate the influence of various factors on overall performance.</p>

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Mixed fleet sizing and scheduling for green distribution with time-based shipment consolidation

  • 期刊论文

  • 发表日期：

    2026年7月14日

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  • Anass Kajji,
  • Tarik Aouam,
  • Asmae El Mokrini,
  • Birger Raa

摘要

This paper introduces and solves the mixed fleet optimization problem with electric and fuel vehicles for distribution planning, when periodically repeating a given set of routes. This integrated problem determines fleet sizing and scheduling with the objective of minimizing the total cost of fleet, transportation, energy, carbon emissions, and inventory. We use comprehensive calculations of energy consumption and carbon emissions and formulate the mixed fleet optimization problem as a non-linear mixed integer program. Subsequently, analytical results for optimal route cycle time and vehicle type and insights on their effect on scheduling are derived. These findings inform our development of novel scheduling strategies within a powerful multi-start solution framework that effectively links cycle time adjustment and vehicle type optimization. Computational experiments demonstrate the strong performance of our approach and yield valuable insights into the complex cost trade-offs involved. Finally, we evaluate the economic benefits of adopting a m3ixed fleet and investigate the influence of various factors on overall performance.