Conference Agenda

The challenges faced in the design of heuristics for standard supply chain software are highlighted. Using the capacitated vehicle routing problem with time windows (CVRPTW) as an example, it is shown which additional business constraints need to be incorporated into the problem formulation, and how to address the impact of software product standards on algorithm design and architecture.

Several metaheuristics use decomposition and pruning strategies to solve large-scale instances of the vehicle routing problem (VRP). Those complexity reduction techniques often rely on simple, problem-specific rules. However, the growth in available data and advances in computer hardware enable data-based approaches that use machine learning (ML) to improve scalability of solution algorithms. We propose a decompose-route-improve (DRI) framework that groups customers using clustering. Its similarity metric incorporates customers' spatial, temporal, and demand data and is formulated to reflect the problem's objective function and constraints. The resulting sub-routing problems can independently be solved using any suitable algorithm. We apply pruned local search (LS) between solved subproblems to improve the overall solution. Pruning is based on customers' similarity information obtained in the decomposition phase. We parameterize and compare the clustering algorithms in a computational study and benchmark the DRI against state-of-the-art metaheuristics. Results show that our data-based approach outperforms classic cluster-first, route-second approaches solely based on customers' spatial information. The introduced similarity metric forms separate sub-VRPs and improves the selection of LS moves in the improvement phase. Thus, the DRI scales existing metaheuristics to achieve high-quality solutions faster for large-scale VRPs by efficiently reducing complexity. Further, the DRI can be easily adapted to various solution methods and VRP characteristics, making it a generalizable approach to solving routing problems.

Academic research plays a critical role in innovating and shaping the future of standard logistics software. As exemplified by the SAP@TUM collaboration, we highlight the pivotal role of collaborative, applied research and how partnering with academia drives product innovation. We provide insights into the SAP@TUM collaboration model, its underlying operational structure, and project workflow. Based on an exemplary project, we showcase how industry-university collaborations impact standard logistics software and lead to enhanced results regarding performance, solution quality, and innovative modeling approaches. These advancements ultimately deliver significant value to customers, setting the stage for future innovations.