A deep reinforcement learning-based adaptive search for solving time-dependent green vehicle routing problem

The time-dependent green vehicle routing problem with time windows is a further deepening of the research on vehicle routing problems with time windows. Its simultaneous consideration of vehicle transportation time, carbon emissions, and customer satisfaction under time-dependent variables makes it...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2024-01, Vol.12, p.1-1
Main Authors: Yue, Bin, Ma, Junxu, Shi, Jinfa, Yang, Jie
Format: Article
Language:English
Subjects:
Adaptive search techniques
Algorithms
Carbon dioxide
Convergence
Crossovers
Customer satisfaction
Deep learning
Dependent variables
DQN
Emissions
GVRPTW
Multi-objective optimization
Multiple objective analysis
Optimization
Pareto optimization
Search methods
Search problems
Solution space
Time dependence
Time-dependent
Transportation
Vehicle routing
Windows (intervals)
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The time-dependent green vehicle routing problem with time windows is a further deepening of the research on vehicle routing problems with time windows. Its simultaneous consideration of vehicle transportation time, carbon emissions, and customer satisfaction under time-dependent variables makes it more challenging to solve than traditional vehicle routing problems. This work proposes a multi-objective optimization algorithm that combines the learnable crossover strategy and the adaptive search strategy based on reinforcement learning to overcome the local optima, poor convergence, and reduced variety of solutions that plague the multi-objective optimization algorithms when solving this problem. The proposed approach solves the problem in two stages: In the first stage, a hybrid initialization strategy is used to generate initial solutions with high quality and diversity, and crossover strategies are used to further explore the solution space and improve convergence by learning the characteristics of pareto solutions. In the second stage, the adaptive search is designed and used for learning and searching in the later stage of the algorithm. The experimental results show better solution quality obtained by the proposed approach, and the effectiveness and superiority of the proposed approach over existing methods in terms of solution convergence and diversity are demonstrated through experimental comparisons.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3369474