2023/07/21 by Mohammed Ghannam, Ambros Gleixner, Ghannam, Mohammed +1
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Optimization and Control (math.OC) #Robotic Path Planning Algorithms #Vehicle Routing Optimization Methods
paper · pdf · doi:10.48550/arxiv.2307.11800
openalex publication_date 2023/07/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The dynamic vehicle routing problem with time windows (DVRPTW) is a generalization of the classical VRPTW to an online setting, where customer data arrives in batches and real-time routing solutions are required. In this paper we adapt the Hybrid Genetic Search (HGS) algorithm, a successful heuristic for VRPTW, to the dynamic variant. We discuss the affected components of the HGS algorithm including giant-tour representation, cost computation, initial population, crossover, and local search. Our approach modifies these components for DVRPTW, attempting to balance solution quality and constraints on future customer arrivals. To this end, we devise methods for comparing different-sized solutions, normalizing costs, and accounting for future epochs that do not require any prior training. Despite this limitation, computational results on data from the EURO meets NeurIPS Vehicle Routing Competition 2022 demonstrate significantly improved solution quality over the best-performing baseline algorithm.