4.7 Article

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

Journal

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2023.2258237

Keywords

Logistics; just-in-time; environment; vehicle routing; large neighbourhood search

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Freight transportation, including JIT supplier networks, contributes significantly to global CO2 emissions. The recent literature discusses the JIT truck routing problem (TRP-JIT), which involves multiple suppliers serving a single OEM with the logistics provider organizing the milk-run routes. This study focuses on the environmental impact of TRP-JIT by considering weight-distance and using a bi-criterial LNS method to estimate Pareto frontiers.
Freight transportation, including just-in-time (JIT) supplier networks, accounts for a substantial part of the global carbon dioxide (CO $ _2 $ 2) 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. & epsilon;-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.

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