Multi-Objective Heterogeneous Fleet Vehicle Routing Problem: Formulation and Algorithm

The Heterogeneous Fleet Vehicle Routing Problem (HFVRP) aims to find optimal routes for vehicles with different capacities and costs, and is common in real-world applications. Total cost and fairness among drivers are two important yet conflicting objectives, while existing studies address either on...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on intelligent transportation systems S. 1 - 15
Hauptverfasser: Ba, Yunpeng, Zheng, Ruihao, Wang, Zhenkun, Li, Genghui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2025
Schlagworte:
Aerospace electronics
artificial intelligence
Barium
Costs
freight transportation and logistics
meta-heuristics
Metaheuristics
Multi-objective combinatorial optimization
Object recognition
Perturbation methods
Resource management
Vectors
Vehicle routing
Vehicles
ISSN:1524-9050, 1558-0016
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The Heterogeneous Fleet Vehicle Routing Problem (HFVRP) aims to find optimal routes for vehicles with different capacities and costs, and is common in real-world applications. Total cost and fairness among drivers are two important yet conflicting objectives, while existing studies address either one objective alone or a specific weighted sum of them. To trade off the two objectives simultaneously, this paper formulates the Multi-Objective HFVRP (MO-HFVRP). Our analysis reveals that the MO-HFVRP is challenging, as the decision space has sparse feasible solutions and the objective space exhibits an uneven distribution of objective vectors. Subsequently, a corresponding algorithm called AMOILS/D is proposed. It decomposes the MO-HFVRP into a few single-objective subproblems, and then applies Iterated Local Search (ILS) and multi-objective optimization techniques to collaboratively solve them. AMOILS/D has three key components. The first is the resource allocation strategy that periodically selects subproblems to focus the search on promising regions. The other two are the adaptive perturbation degree control and the acceptance mechanism in ILS. They enable effective navigation of the decision space and balance convergence and diversity. Experimental results show that AMOILS/D significantly outperforms other representative algorithms across most instances. Ablation studies also confirm the effectiveness of each proposed component.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2025.3624271