Modules - Research topics

This project will combine the long-term experience of the laboratory head, Karl F. Doerner, in developing and applying optimization techniques to logistical decision problems with the practical expertise of our industrial partners Industrie-Logistik-Linz GmbH & Co KG and Wiener Linien GmbH & Co KG. The term intermodal transportation refers to the transportation of passengers or freight from an origin to a destination by at least two transportation modes, such that the transfer from one mode to another takes place in intermodal terminals. The primary aim of the CD laboratory for efficient intermodal transport operations is the development of new solution methods based on operations research techniques to optimize resource planning and management in intermodal and public transportation networks. New models and methods are designed for solving complex large-scale transportation problems.

The CD laboratory is structured in the following way: (i) Module 1: Efficient resource management in intermodal transportation started in February 2013 with the industrial partner Industrie-Logistik- Linz GmbH, and (ii) Module 2: Efficient resource management in public transportation started in February 2014 with the industrial partner Wiener Linien GmbH & Co KG. Industrie-Logistik-Linz focuses on intermodal transportation systems, which arise in the transportation of goods as well as in the transportation of people. Wiener Linien are particularly interested in disruption management, maintenance scheduling, and headway optimization.

New and innovative solution techniques

We will develop new and innovative matheuristics. Matheuristics are a combination of exact methods and metaheuristic search techniques. The research challenge lies in the connection of the metaheuristic and exact search components. New solution pool management and information exchange concepts will be introduced. These methods will be developed for single-objective and multi-objective cases.

Multi-objective optimization problems in intermodal transportation

Multiple, conflicting goals are typical in real-world scenarios but also difficult for practitioners to solve. Yet most research has focused mainly on single-objective problems. We will introduce intermodal optimization problems that address multiple objectives, including economical and client-centered goals. Our aim is to present a set of efficient solutions to decision makers, who then may select the best compromise solution according to their preferences.

Green logistics in intermodal passenger/freight transport

Furthermore, the optimization of ecological goals becomes more and more important in practice. On the one hand, this is another contribution to conflicting objectives. On the other hand we will work on specific mobility concepts, which are especially designed for optimizing these objectives (e.g., planning specific delivery routes with respect to CO2 reductions by considering the altitude of movements or concepts based on car pooling).

Value of information in intermodal passenger/freight transport

In reality, data uncertainties have significant impacts on the solution quality. We will analyze the gap between the best possible solution, considering complete information, and the solution quality achieved through the consideration of only random/uncertain input data. Furthermore, we will establish the impact of early availability of input data on solution quality. Stochastic information will be integrated into the optimization process helping to reduce this gap.

Benefit of flexibility in intermodal passenger/freight transport

The models and solution approaches will be extended to illustrate the impact of flexibility (of passengers or the receivers of freight) in relation to time-dependent constraints (e.g. fixed vs. flexible delivery dates) on the solution quality.

Disruption management in intermodal passenger/freight transport

We will further develop methods for managing uncontrollable disruptions (e.g., breakdowns, mechanical failures), which may have significant impacts on the solution quality. Here, we focus on intelligent buffer integration and finding robust solutions.