As a research hotspot across logistics, operations research, and artificial intelligence, route planning has become a key technology for intelligent transportation systems. Recently, data-driven machine learning heuristics, including learning construction methods and learning improvement methods, have achieved remarkable success in solving single-objective route planning problems. However, many practical route planning scenarios must simultaneously consider multiple conflict objectives. For example, modern logistics companies often need to simultaneously minimize time budget, transportation cost, and vehicle pollution. Several learning construction methods are proposed for solving classical multi-objective route planning (MORP) problems, yet no learning improvement heuristics have been developed so far, even though they are acknowledged to be more efficient in narrowing the optimality gap. To fill this gap, this paper proposes a learning improvement MORP method, Pareto Improver (PI). PI employs a population-based mechanism to approximate the Pareto front with a single deep reinforcement learning model. The experimental results on various MORP problems show that PI can significantly outperform other state-of-the-art methods.
Pareto Improver: Learning Improvement Heuristics for Multi-Objective Route Planning
IEEE Transactions on Intelligent Transportation Systems ; 25 , 1 ; 1033-1043
2024-01-01
4532767 byte
Article (Journal)
Electronic Resource
English
Multi-objective route planning problem for cycle-tourists
Taylor & Francis Verlag | 2022
|Multi-Objective Pareto Collaborative Optimization and Its Application
British Library Conference Proceedings | 2006
|Multi Objective Design of Ships; A Pareto Procedure
TIBKAT | 2021
|