The research group Logistics (LOG) aims to achieve theoretical advances in modelling and solving real-life decision making problems in the field of logistics and to valorise results in cooperation with the industry. Planning problems are studied using techniques from operations research to increase the efficiency of transportation and logistics companies. Currently, four research streams exist within the group: vehicle routing, warehouse management, intermodal transportation and healthcare logistics.
Vehicle routing is a complex operational planning problem in which customers/locations have to be assigned to vehicles, and a route for each vehicle has to be created, so as to optimize a predefined objective (e.g., minimizing the distance to be travelled). In our research, we are focusing on a variety of vehicle routing problems in the context of both freight transportation (e.g., routing trucks to deliver goods to customers) and passenger transportation (e.g., routing vans to transport elderly and disabled people). Our aim is to develop new mathematical models for problems which account for real-life issues faced by companies (e.g., axle weight limitations), providing efficient solution methods for these models, and analysing the effect of problem characteristics and algorithm parameters on solutions.
A warehouse can be defined as a facility where activities of receiving, storage, order picking and shipping are performed, often by human operators. Trends such as shortened product life cycles, e-commerce, greater product variety and point-of-use delivery expose order picking activities to new challenges. In our research, we aim to optimize warehouse operations in an integrated way in order to avoid a suboptimal solution for the total warehouse. Furthermore, we try to incorporate human factors in order picking planning problems to ensure that order picking policies are conform the skills and overall requirements of the individual order picker. Another research topic within this research stream is the integrated order picking-vehicle routing problem, which optimizes internal warehouse operations and distribution operations simultaneously. This leads to cost savings or a higher service level by allowing customers to place their orders as late as possible.
Our research group has a strong experience in modelling intermodal transport operations. On the one hand, we develop models for supporting operational planning problems such as intermodal train loading planning. On the other hand, we analyze bundling opportunities for freight and investigate the complexity of sharing cost savings fairly among shippers who bundle freight flows in order to reach economies of scale in intermodal transport. Synchromodal transportation goes a step further with its ability to dynamically select the most appropriate transport mode, route and terminals depending on the network conditions.
In this research stream, we focus on three different topics: dial-a-ride problems, simulation and optimization of emergency departments, and inventory management in healthcare supply chains. A dial-a-ride problem tackles the routing arising in collective on-demand transportation systems. The aim is to develop efficient vehicle routes and time schedules, respecting service level requirements from customers. By simulating and optimizing emergency departments we aim to make these ED’s work more efficiently and evaluate the improvement options for solving the problem of ED crowding. Inventory management in healthcare supply chains minimizes the total multi-echelon inventory costs. Multiple supply chain configurations are compared in order to detect the optimal configuration.