Article Dans Une Revue Applied Soft Computing Année : 2025

Energy minimizing capacitated covering vehicle routing problem

Résumé

This paper introduces the Energy Minimizing Covering Capacitated Vehicle Routing Problem, which aims to minimize total energy consumed during delivery routes while satisfying customers’ demands. The problem considers a homogeneous fleet of identical vehicles stationed at a central depot and utilizes a concept of flexible delivery. Customers can receive parcels either directly during a vehicle visit or indirectly through designated neighboring customers. The flexible delivery can be particularly relevant for densely populated urban areas where parking is limited or situations where customers have restricted mobility or limited home presence. We formulate the studied problem as a mixed-integer programming problem. For large-scale instances, skewed and standard general variable neighborhood search heuristics are developed to tackle the problem efficiently. Extensive testing validates the model and assesses the effectiveness and efficiency of the proposed heuristics. The study also compares the skewed GVNS with metaheuristics of similar design, including GRASP and Iterated Local Search, to benchmark its performance. The results reveal that a skewed general variable neighborhood search heuristic is a viable approach for solving the problem, particularly for large-scale instances. Additionally, the study explores the trade-off between energy consumption and total travel distance. Results suggest that slight increases in travel distance can lead to significant energy consumption and CO2 emissions savings.

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Dates et versions

hal-05215104 , version 1 (19-08-2025)

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Milena Vukićević, Mustapha Ratli, Atika Rivenq, Raca Todosijević, Bassem Jarboui. Energy minimizing capacitated covering vehicle routing problem. Applied Soft Computing, 2025, 183, pp.113620. ⟨10.1016/j.asoc.2025.113620⟩. ⟨hal-05215104⟩
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