Environmental aspects in supplier networks-a bi-objective just-in-time truck routing problem

AbstractFreight transportation, including just-in-time (JIT) supplier networks, accounts for a substantial part of the global carbon dioxide (CO2) emissions. The JIT truck routing problem (TRP-JIT) presented in the recent literature consists of several suppliers serving a single original equipment manufacturer (OEM). A logistics provider organises the milk-run routes. The shipments are available after their release dates at the suppliers and should be delivered on their due dates at the OEM with minimal total earliness-tardiness penalties (first objective). Unlike previous research on the TRP-JIT, we focus on its environmental impact: (1) We include the weight-distance (second objective), depending on the truck's curb weight, the load, and the transportation distance. (2) We adapt a state-of-the-art large neighbourhood search (LNS) from the literature considering both objectives. (3) The LNS is embedded in bi-criterial frameworks, i.e. ε-constraint and weighted sum methods. Thereby, we estimate Pareto frontiers with at least 60 solutions in less than 25 min for instances with 99 shipments. From a managerial perspective, increasing the difference between the release and due dates for a better JIT performance may worsen the environmental impact. Lighter trucks can reduce the environmental costs without affecting the JIT performance, whereas a smaller fleet negatively affects both objectives.Keywords: Logisticsjust-in-timeenvironmentvehicle routinglarge neighbourhood search Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study (instances used in the computational study) are available on GitHub at https://github.com/jbaals/envtrpjit. These data were derived from the following resources available in the public domain:Instances of Demir, Bektaş, and Laporte (Citation2012) at http://www.apollo.management.soton.ac.uk/prplib.htm.Additional informationNotes on contributorsJulian BaalsJulian Baals received a B.Sc. and M.Sc. degree in Engineering Management/Industrial Engineering from the University of Technology in Darmstadt, Germany in 2016 and 2019 respectively. Between 2020 and 2022, he was enrolled as a Ph.D. fellow at Aarhus University, Denmark. Since 2022, he is continuing his Ph.D. studies at Friedrich Schiller University Jena, Germany. His focus is on just-in-time logistics especially the optimisation in transportation networks by developing metaheuristic solution procedures.