Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem

2026-04-14

AI Summary

This study addresses the challenge of jointly optimizing routing and charging decisions in the Electric Capacitated Vehicle Routing Problem (E-CVRP), where these two components exhibit strong interdependence. To tackle this, the authors propose a surrogate-objective-based bilevel optimization framework that explicitly models their coupling by guiding the lower-level search through an upper-level surrogate objective. A three-phase bilevel Late Acceptance Hill Climbing (b-LAHC) algorithm is developed, integrating neighborhood search with greedy descent strategies to achieve high solution efficiency without requiring parameter tuning. Evaluated on the IEEE WCCI-2020 benchmark instances under a fixed computational budget, the proposed method attains near-optimal solutions for small-scale cases and establishes new best-known results on 9 out of 10 large-scale instances, yielding an average improvement of 1.07%.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper tackles the Electric Capacitated Vehicle Routing Problem (E-CVRP) through a bilevel optimization framework that handles routing and charging decisions separately or jointly depending on the search stage. By analyzing their interaction, we introduce a surrogate objective at the upper level to guide the search and accelerate convergence. A bilevel Late Acceptance Hill Climbing algorithm (b-LAHC) is introduced that operates through three phases: greedy descent, neighborhood exploration, and final solution refinement. b-LAHC operates with fixed parameters, eliminating the need for complex adaptation while remaining lightweight and effective. Extensive experiments on the IEEE WCCI-2020 benchmark show that b-LAHC achieves superior or competitive performance against eight state-of-the-art algorithms. Under a fixed evaluation budget, it attains near-optimal solutions on small-scale instances and sets 9/10 new best-known results on large-scale benchmarks, improving existing records by an average of 1.07%. Moreover, the strong correlation (though not universal) observed between the surrogate objective and the complete cost justifies the use of the surrogate objective while still necessitating a joint solution of both levels, thereby validating the effectiveness of the proposed bilevel framework and highlighting its potential for efficiently solving large-scale routing problems with a hierarchical structure.

Problem

• Electric Capacitated Vehicle Routing Problem
• bilevel optimization
• routing and charging decisions
• surrogate objective
• vehicle routing

Innovation

• bilevel optimization
• surrogate objective
• Late Acceptance Hill Climbing
• Electric Vehicle Routing
• metaheuristic algorithm