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

•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 metaheuristi...

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Vydáno v:European journal of operational research Ročník 289; číslo 3; s. 940 - 958
Hlavní autoři: Anderluh, Alexandra, Nolz, Pamela C., Hemmelmayr, Vera C., Crainic, Teodor Gabriel
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 16.03.2021
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ISSN:0377-2217, 1872-6860
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Abstract •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.
AbstractList •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.
Author Crainic, Teodor Gabriel
Hemmelmayr, Vera C.
Anderluh, Alexandra
Nolz, Pamela C.
Author_xml – sequence: 1
  givenname: Alexandra
  orcidid: 0000-0001-7337-0799
  surname: Anderluh
  fullname: Anderluh, Alexandra
  email: alexandra.anderluh@wu.ac.at
  organization: WU Institute for Transport and Logistics Management, 1020 Vienna, Austria
– sequence: 2
  givenname: Pamela C.
  surname: Nolz
  fullname: Nolz, Pamela C.
  organization: AIT Center for Mobility Systems, Dynamic Transportation Systems, 1210 Vienna, Austria
– sequence: 3
  givenname: Vera C.
  surname: Hemmelmayr
  fullname: Hemmelmayr, Vera C.
  organization: WU Institute for Transport and Logistics Management, 1020 Vienna, Austria
– sequence: 4
  givenname: Teodor Gabriel
  orcidid: 0000-0002-4424-0984
  surname: Crainic
  fullname: Crainic, Teodor Gabriel
  organization: UQAM School of Management, Université du Québec à Montrèal, CP 8888 succ. Centre-ville, Montréal (QC), H3C 3P8 Canada
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Keywords Grey zone
City layout
Two-echelon VRP
Vehicle synchronization
Multiple objective programming
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Snippet •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...
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SubjectTerms City layout
Grey zone
Multiple objective programming
Two-echelon VRP
Vehicle synchronization
Title Multi-objective optimization of a two-echelon vehicle routing problem with vehicle synchronization and ‘grey zone’ customers arising in urban logistics
URI https://dx.doi.org/10.1016/j.ejor.2019.07.049
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