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A Sustainable Multi-Objective Model for Capacitated-Electric-Vehicle-Routing-Problem Considering Hard and Soft Time

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This study optimizes electric vehicle routing (EVRP) considering charging and partial recharging. Hybrid meta-heuristics provide efficient solutions for complex, multi-objective delivery problems.

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Area of Science:

  • Operations Research
  • Environmental Science
  • Transportation Engineering

Background:

  • The transportation sector's high pollution necessitates sustainable solutions like electric vehicles (EVs).
  • Electric Vehicle Routing Problems (EVRP) are increasingly studied, focusing on challenges like limited battery capacity and operational domains.
  • Existing EVRP models often overlook practical aspects such as charging station availability and partial recharging strategies.

Purpose of the Study:

  • To address an extended Electric Vehicle Routing Problem (EVRP) incorporating customer deliveries, charging stations, and partial recharging.
  • To develop a multi-objective optimization model considering economic, environmental, and social factors.
  • To evaluate exact and meta-heuristic algorithms for solving small and large-scale EVRP instances.

Main Methods:

  • Formulation of the EVRP as a multi-objective integer linear programming model.
  • Application of Preemptive Fuzzy Goal Programming (PFGP) for small-sized problems.
  • Utilization of hybrid meta-heuristic algorithms (MOSA, MOGWO, MOPSO, NSGAII_TLBO) for large-sized problems.

Main Results:

  • Hybrid meta-heuristic algorithms effectively generated high-quality, non-dominated solutions for the EVRP.
  • MOSA algorithm demonstrated superior performance in terms of solution time.
  • NSGA-II-TLBO algorithm excelled in solution quality metrics (MID, MOCV, HV).

Conclusions:

  • The proposed multi-objective model and algorithms provide efficient solutions for practical EVRP scenarios.
  • Hybrid meta-heuristics are suitable for solving complex, large-scale EVRP with economic, environmental, and social considerations.
  • Algorithm selection depends on specific performance priorities, such as speed versus solution quality.