Zur Person:

2022 bis 2024:

Doktorand im Bereich Qualitätsmanagement bei der BMW Group und am Lehrstuhl für Betriebswirtschaftslehre der Universität Duisburg-Essen

Optimierung der Verladung von Neufahrzeugen auf Autotransportern und Zügen

2019 bis 2021

Master-Studium Wirtschaftsingenieur Logistik an der Otto-von-Guericke Universität Magdeburg:

Master of Science mit den Vertiefungen Logistiksysteme, VR und Nachhaltigkeit

2015 bis 2019

Bachelor-Studium Wirtschaftsingenieur Maschinenbau an der Otto-von-Guericke Universität Magdeburg:

Bachelor of Science mit der Vertiefung Logistik und Materialflusstechnik

Publikationen:

• Jäck, C.: Optimization, Automation and Decision Support for Auto Carrier and Auto Train Loading (1). 2024. BIB DownloadDetails
• Jäck, C.; Gönsch, J.: How to Load Your Auto Carrier. A Hybrid Packing Approach for the Auto-Carrier Loading Problem. In: European Journal of Operational Research, Jg. 315 (2024) Nr. 3, S. 1167-1181. doi:10.1016/j.ejor.2024.01.001VolltextBIB DownloadKurzfassungDetails

  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.

• Jäck, C.: Comparing Loading Strategies for Auto Trains: Balancing Efficiency with Information Requirements, 2024. BIB DownloadDetails
• Jäck, C.; Gönsch, J.; Dörmann, H.: Load Factor Optimization for the Auto-Carrier Loading Problem (ACLP). In: Transportation Science, Jg. 57 (2023) Nr. 6, S. 1696-1719. doi:10.1287/trsc.2022.0373VolltextBIB DownloadKurzfassungDetails

  The distribution of passenger vehicles is a complex task and a high cost factor for automotive original equipment manufacturers (OEMs). On the way from the production plant to the customer, vehicles travel long distances on different carriers, such as ships, trains, and trucks. To save costs, OEMs and logistics service providers aim to maximize their loading capacities. Modern auto carriers are extremely flexible. Individual platforms can be rotated, extended, or combined to accommodate vehicles of different shapes and weights and to nest them in a way that makes the best use of the available space. In practice, finding feasible combinations is done with the help of simple heuristics or based on personal experience. In research, most papers that deal with auto carrier loading focus on route or cost optimization. Only a rough approximation of the loading subproblem is considered. In this paper, we present two different methodologies to approximate realistic load factors considering the flexibility of modern auto carriers and their height, length, and weight constraints. Based on our industry partner’s process, the vehicle distribution follows a first in, first out principle. For the first approach, we formulate the problem as a mixed integer, quadratically constrained assignment problem. The second approach considers the problem as a two-dimensional nesting problem with irregular shapes. We perform computational experiments using real-world data from a large German automaker to validate and compare both models with each other and with an approximate model adapted from the literature. The simulation results for the first approach show that, on average, for 9.37% of all auto carriers, it is possible to load an additional vehicle compared with the current industry solution. This translates to 1.36% less total costs. The performance of the nesting approach is slightly worse, but as it turns out, it is well-suited to check load combinations for feasibility.