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Optimization of scheduling scheme for self-driving vehicles by simulation algorithm.

Xu Jianqiao1

  • 1College of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.

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|July 26, 2023
PubMed
Summary
This summary is machine-generated.

This study optimizes self-driving electric vehicle (SEV) scheduling for logistics, reducing energy use and emissions. The dynamic programming model and simulation algorithm efficiently plan SEV routes for improved economic and environmental benefits.

Keywords:
dynamic programminglogistics engineeringoverall planningself-driving electric vehiclesimulation algorithmvehicle dispatching

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Area of Science:

  • Logistics technology
  • Autonomous systems
  • Sustainable transportation

Background:

  • Self-driving electric vehicles (SEVs) offer advancements in freight efficiency, energy conservation, and emissions reduction.
  • Optimizing SEV logistics requires comprehensive vehicle scheduling for economic and environmental gains.
  • Existing challenges in SEV logistics include efficient route planning and energy management.

Purpose of the Study:

  • To develop a method for optimizing the scheduling scenario and parameters of self-driving vehicles for logistics.
  • To address a real-world freight problem on a two-way single-lane road with charging stations.
  • To enhance economic and environmental benefits through efficient SEV operations.

Main Methods:

  • Development of an optimization model based on dynamic programming.
  • Design of an optimization simulation algorithm to solve the dynamic programming model.
  • Computer simulations to test the proposed method on a specific freight scenario.

Main Results:

  • The proposed model and algorithm effectively solve the overall vehicle scheduling problem.
  • The simulation algorithm demonstrates good universality across different parameters.
  • The algorithm achieves high efficiency, completing complex simulations rapidly (e.g., 150 vehicles, 1000 hours, 10 km road in 1.5 seconds).

Conclusions:

  • The dynamic programming model and simulation algorithm provide an effective solution for SEV logistics optimization.
  • The method successfully optimizes scheduling schemes and maximizes freight transport.
  • This approach significantly improves the efficiency and sustainability of autonomous electric vehicle logistics.