Multi-objective optimization of a two-echelon vehicle routing problem with vehicle synchronization and ‘grey zone’ customers arising in urban logistics

• Published in: European journal of operational research Vol. 289; no. 3; pp. 940 - 958
• Main Authors: Anderluh, Alexandra, Nolz, Pamela C., Hemmelmayr, Vera C., Crainic, Teodor Gabriel
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 16.03.2021
• Subjects: City layout; Grey zone; Multiple objective programming; Two-echelon VRP; Vehicle synchronization; Grey zone; City layout; Two-echelon VRP; Vehicle synchronization; Multiple objective programming
• ISSN: 0377-2217; 1872-6860; 1872-6860
• DOI: 10.1016/j.ejor.2019.07.049

•The ’grey zone’ improves the solution above all for clustered instances.•City layouts with the city center in the middle of the city profit most.•The population-based disturbance measure performs well.•The heuristic cuboid splitting can estimate the Pareto surface for 3 objectives.•The metaheuristic embeds large neighborhood search into heuristic cuboid splitting. We present a multi-ob’jective two-echelon vehicle routing problem with vehicle synchronization and ‘grey zone’ customers arising in the context of urban freight deliveries. Inner-city center deliveries are performed by small vehicles due to access restrictions, while deliveries outside this area are carried out by conventional vehicles for economic reasons. Goods are transferred from the first to the second echelon by synchronized meetings between vehicles of the respective echelons. We investigate the assignment of customers to vehicles, i.e., to the first or second echelon, within a so-called ‘grey zone’ on the border of the inner city and the area around it. While doing this, the economic objective as well as negative external effects of transport, such as emissions and disturbance (negative impact on citizens due to noise and congestion), are taken into account to include objectives of companies as well as of citizens and municipal authorities. Our metaheuristic – a large neighborhood search embedded in a heuristic rectangle/cuboid splitting – addresses this problem efficiently. We investigate the impact of the free assignment of part of the customers (‘grey zone’) to echelons and of three different city layouts on the solution. Computational results show that the impact of a ‘grey zone’ and thus the assignment of these customers to echelons depend significantly on the layout of a city. Potentially pareto-optimal solutions for two and three objectives are illustrated to efficiently support decision makers in sustainable city logistics planning processes.