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This study proposes a dynamic path optimization method using a genetic algorithm to address traffic congestion in urban logistics. The improved algorithm efficiently solves the vehicle scheduling problem with time windows, providing valuable routing schemes.

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Area of Science:

  • Operations Research
  • Computer Science
  • Logistics Management

Background:

  • Urban traffic congestion poses significant challenges to logistics efficiency.
  • Traditional vehicle scheduling models often overlook dynamic time constraints.
  • Optimizing delivery routes is crucial for reducing operational costs and improving service.

Purpose of the Study:

  • To develop a dynamic path optimization model for urban road networks.
  • To enhance the vehicle scheduling problem (VSP) with strict time window considerations.
  • To improve the efficiency and speed of obtaining optimal logistics routing schemes.

Main Methods:

  • A mathematical model integrating decomposition coordination and genetic algorithms was established.
  • Customers were grouped, and service order was determined for express cars.
  • An improved genetic algorithm based on sequence coding was utilized for optimization.
  • Data including customer coordinates, demand, time windows, and costs were collected.

Main Results:

  • The improved genetic algorithm achieved optimal solutions in approximately 140 generations, showing faster convergence.
  • The hybrid genetic algorithm demonstrated good performance in solving the VSP with time windows.
  • Comparative analysis confirmed the algorithm's effectiveness and feasibility.

Conclusions:

  • The proposed dynamic path optimization method is effective for urban logistics.
  • The genetic algorithm-based approach provides a feasible and efficient solution for vehicle routing.
  • The method enables quick generation of valuable vehicle routing and scheduling schemes.