A novel ant colony system for vehicle routing problem with time windows: leveraging multiple heuristic strategies for improved solutions
Neural Computing and Applications, vol.38, no.17, 2026 (Scopus)
- Publication Type: Article / Article
- Volume: 38 Issue: 17
- Publication Date: 2026
- Doi Number: 10.1007/s00521-026-12418-z
- Journal Name: Neural Computing and Applications
- Journal Indexes: Scopus, Aerospace Database, Applied Science & Technology Source, Compendex, Index Islamicus, INSPEC, zbMATH, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Technology Collection (ProQuest)
- Keywords: Ant colony system, Heuristics, Multiple search strategies, Vehicle routing problem with time windows
- Bursa Uludag University Affiliated: Yes
Abstract
The vehicle routing problem with time windows presents a complex challenge, involving the efficient scheduling of deliveries within predefined time windows while optimizing vehicle usage. In response, this paper introduces an advanced ant colony system approach that seamlessly integrates four heuristic techniques. The devised framework employs a ranking-based selection mechanism, utilizing the roulette procedure, to synergistically harness these heuristics, resulting in the generation of promising solutions. To counter stagnation and enhance solution diversity, we incorporate a no-improvement counter into the algorithm for solution selection. Additionally, we introduce a pioneering heuristic called 2-opt-special, inspired by the renowned 2-opt algorithm, to bolster the efficiency and effectiveness of the proposed approach. Computational results, derived from extensive comparisons with other ant colony-based algorithms and established solutions on well-known benchmarks comprising 100 and 400 customers, unequivocally affirm the effectiveness and competitiveness of our proposed algorithm in effectively addressing VRPTW challenges.