Type of Publication: Article in Journal

How to load your auto carrier. A hybrid packing approach for the auto-carrier loading problem

Author(s):

Jäck, C.; Gönsch, J.

Title of Journal:

European Journal of Operational Research

Volume (Publication Date):

315 (2024)

Number of Issue:

3

pages:

1167-1181

Digital Object Identifier (DOI):

doi:10.1016/j.ejor.2024.01.001

Link to complete version:

https://www.sciencedirect.com/science/article/pii/S0377221724000018

Citation:

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Abstract

The distribution of new vehicles is a critical cost factor for automotive original equipment manufacturers (OEMs). While trains are the ideal mode for overland transport, OEMs often opt for road transport via auto carriers due to flexibility reasons or unavailability of rail networks. Auto carriers are specialized trucks equipped with flexible loading platforms. Finding the optimal configuration of platforms can be a difficult task due to passenger vehicles varying in size, shape, and weight. This problem is known as the auto-carrier loading problem (ACLP). The current literature deals with the ACLP on a strategic level with the goal to maximize transport capacities. However, it neglects the resulting increase in operational complexity.

In this paper, we present a hybrid packing approach to solve the ACLP on an operational level. Our approach considers the shapes of the vehicles as polygons and incorporates an approximation of continuous rotation by combining a geometric algorithm with a mixed integer model formulation. We aim not only to maximize loading capacities but also to provide instructions for truck drivers on how to arrange the vehicles on the truck. We conduct a proof of concept (POC) with actual auto carriers to verify the feasibility of our approach and to quantify potential benefits for our industry partner. The POC confirms that our approach reliably generates feasible and comprehensive loading instructions. Not only could the implementation of our approach reduce lead times and transportation damages, but in addition, could completely automate the manual load creation process in distribution centers.