Planning the transportation of car parts from suppliers to assembly plants is a critical issue for automotive industries, which rely on complex supply chains. Over a planning horizon of several weeks, two decisions have to be made. First, how to assign parts to trucks, taking into account early and late delivery dates for parts and the planned truck schedule. Next, how to load the parts onto the trucks, a complex packing problem that must take into account multiple practical constraints, such as weight distribution, stability, orientation, and fragility. We follow a decomposition strategy, solving the f irst assignment problem approximately using an integer linear model, and developing a metaheuristic algorithm for the packing phase. We then study several strategies for combining these two elements in a matheuristic framework. Computational results on a set of instances provided by a major automotive manufacturer show that the proposed algorithm improves on the best published results, producing many new best solutions.
Parreño et al. (Mon,) studied this question.
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