Development of the Supply Chain Management 2040 – Opportunities and challenges

Logistics and supply chain management have undergone significant change due to technological changes in the recent years. The classic transport, handling and storage processes with a strict functional orientation have been transformed into a global, network-oriented task-field. The future challenges are individual customer requirements, shorter delivery times and increasing cost pressure. Due to these challenges and the increasing globalization, companies are confronted with ever more complex supply chain networks. The digital transformation is intended to remedy this situation. New technologies, comprehensive real-time information availability and agile value creation networks are just examples to meet these challenges. This paper provides an overview of the expected developments in supply chain management over the next 20 years. Based on ten future megatrends, four main topics (technology, control tower, value adding and green logistics) were derived. The focus of this paper is on OEMs and Tier 1/n suppliers. Both are undergoing a major change on the customer and supplier side due to their central position within the supply chain

Born-Again Tree Ensembles

The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives. As a consequence, research into interpretable machine learning has rapidly grown in an attempt to better control and fix possible sources of mistakes and biases. Tree ensembles offer a good prediction quality in various domains, but the concurrent use of multiple trees reduces the interpretability of the ensemble. Against this background, we study born-again tree ensembles, i.e., the process of constructing a single decision tree of minimum size that reproduces the exact same behavior as a given tree ensemble in its entire feature space. To find such a tree, we develop a dynamic-programming based algorithm that exploits sophisticated pruning and bounding rules to reduce the number of recursive calls. This algorithm generates optimal born-again trees for many datasets of practical interest, leading to classifiers which are typically simpler and more interpretable without any other form of compromise.Comment: "Born-Again Tree Ensembles", proceedings of ICML 2020. The associated source code is available at: https://github.com/vidalt/BA-Tree

Driver Routing and Scheduling with Synchronization Constraints

This paper investigates a novel type of driver routing and scheduling problem motivated by a practical application in long-distance bus networks. A key difference from other crew scheduling problems is that drivers can be exchanged between buses en route. These exchanges may occur at arbitrary intermediate stops such that our problem requires additional synchronization constraints. We present a mathematical model for this problem that leverages a time-expanded multi-digraph and derive bounds for the total number of required drivers. Moreover, we develop a destructive-bound-enhanced matheuristic that converges to provably optimal solutions and apply it to a real-world case study for Flixbus, one of Europe's leading coach companies. We demonstrate that our matheuristic outperforms a standalone MIP implementation in terms of solution quality and computational time and improves current approaches used in practice by up to 56%. Our solution approach provides feasible solutions for all instances within seconds and solves instances with up to 390 locations and 70 requests optimally with an average computational time under 210 seconds. We further study the impact of driver exchanges on personnel costs and show that allowing for such exchanges leads to savings of up to 75%

Support Vector Machines with the Hard-Margin Loss: Optimal Training via Combinatorial Benders' Cuts

The classical hinge-loss support vector machines (SVMs) model is sensitive to outlier observations due to the unboundedness of its loss function. To circumvent this issue, recent studies have focused on non-convex loss functions, such as the hard-margin loss, which associates a constant penalty to any misclassified or within-margin sample. Applying this loss function yields much-needed robustness for critical applications but it also leads to an NP-hard model that makes training difficult, since current exact optimization algorithms show limited scalability, whereas heuristics are not able to find high-quality solutions consistently. Against this background, we propose new integer programming strategies that significantly improve our ability to train the hard-margin SVM model to global optimality. We introduce an iterative sampling and decomposition approach, in which smaller subproblems are used to separate combinatorial Benders' cuts. Those cuts, used within a branch-and-cut algorithm, permit to converge much more quickly towards a global optimum. Through extensive numerical analyses on classical benchmark data sets, our solution algorithm solves, for the first time, 117 new data sets to optimality and achieves a reduction of 50% in the average optimality gap for the hardest datasets of the benchmark

Driver-aware charging infrastructure design

Public charging infrastructure plays a crucial role in the context of electrifying the private mobility sector in particular for urban regions. Against this background, we develop a new mathematical model for the optimal placement of public charging stations for electric vehicles in cities. While existing approaches strongly aggregate traffic information or are only applicable to small instances, we formulate the problem as a specific combinatorial optimization problem that incorporates individual demand and temporal interactions of drivers, exact positioning of charging stations, as well as various charging speeds, and realistic charging curves. We show that the problem can be naturally cast as an integer program that, together with different reformulation techniques, can be efficiently solved for large instances. More specifically, we show that our approach can compute optimal placements of charging stations for instances based on traffic data for cities with up to $600\,000$ inhabitants and future electrification rates of up to $15\%$