Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design

Operations Management of Logistics and Supply Chain: Issues and Directions

There has been consensus that logistics as well as supply chain management is a vital research field, yet with few literature reviews on this topic. This paper sets out to propose some hot issues in the current research, through a review of related literature from the perspective of operations management. In addition, we generate some insights and future research directions in this field

Robust load planning of trains in intermodal transportation

In this paper, the problem of robust load planning for trains in intermodal container terminals is studied. The goal of load planning is to choose wagon settings and assign load units to wagons of a train such that the utilization of the train is maximized, and setup and transportation costs in the terminal are minimized. However, in real-world applications, many of the parameters needed for the model are not known exactly. Since feasibility of the resulting load distribution has always to be guaranteed, we decided to use a robust approach. In particular, we apply the concepts of strict and adjustable robustness to enhance the load planning problem. Based on a formulation developed in Bruns and Knust (OR Spectrum 34:511-533, 2012) for the deterministic load planning problem, we propose mixed-integer linear programming formulations for most of the respective robust counterparts, dependent on the type of uncertainty. An experimental study shows that most of the robust problems can be solved within runtimes of a few minutes, which is good enough for real-world applications. Furthermore, our results indicate that robust solutions may improve the planning considerably, and that it is promising to add robustness even to large mixed-integer programs with many and diverse technical constraints