In spite of massive investment and increased adoption of digital services, citizens continue to use traditional channels to interact with public organizations. The channel choice (CC) field of research tries to understand citizens’ interactions with public authorities to make the interaction more efficient and increase citizen satisfaction. However, most studies have been conducted either as surveys of hypothetical services or in experimental settings, leading to a lack of empirical data from actual use contexts. Therefore, in the lunchtime seminar, I will present the results of a sequential mixed methods study which combines observations of citizen-caseworker interaction in a call center, contextual interviews with callers, and a survey classifying topics from 10,000 telephone calls. Specifically, the study explores the multiplex nature of real-life CC and the problems which cause people to call.

Large-scale spatial systems, such as river catchments, crowds, or land use systems, are difficult to fully analyze empirically. Geosimulation models are built to enable analyses of these systems. On the one hand, geosimulation models help us to better understand the system, because we can 'experiment' with its parameters. On the other hand, geosimulation models are used to make projections of the potential future development of the system under a set of scenarios. Scenario studies are often used to simulate the effects of a range of policy options, to inform decision makers about the potential impacts of the policies. This requires models that are valid now and in the projected future, as policy decisions based on erroneous model projections can be costly and/or irreversible. The overarching goal of my research lab is to better quantify the predictive value of geosimulation models. In this presentation, I show some examples of geosimulation modelling studies, with potential links to the research in the department of Information Systems in terms of the modelled systems (e.g. energy supply) as well the used methods (e.g. error propagation and optimization).

Nowadays, the provision of spare parts and related maintenance, repair and overhaul (MRO) services is a differentiating key factor and important growth sector for manufacturers. Especially in the capital-intensive mechanical engineering industry, companies rely on high-value technical systems in their processes. Hence, failures of machinery and related downtimes lead to losses in production or operations, decreasing customer satisfaction and high costs. Corresponding spare parts supply chains provide the required spare parts and MRO services to the customers. In many cases, multiple specialized actors form complex spare parts supply chains and collaborate in the provision of spare parts and MRO services. Since these actors are at least partially legally independent of each other and only share limited information along the spare parts supply chain, coordination and collaboration of the actors is both required and difficult. This talk will focus on the development of a maturity model for coordination and collaboration in spare parts supply chains. It considers especially aspects from supply chain coordination and collaboration as well as practical insights from the mechanical engineering industry.

Supply chains and production environments, especially in high wage countries like Germany, are constantly becoming more dynamic and complex for several decades already. This development finds its most recent peak in the advent of Industrie 4.0, in which it is intended to enable production systems to be reconfigurable and flexible, manufacturing products in vastly varying amounts and constantly changing specifications. Such changes of course influence the planning model that creates the base for decision-making. For example, the introduction of a new laser cutting machine could require new planning constraints for representing that the machine can operate with different energy levels, influencing its cutting speed and energy consumption.
However, commonly used heuristic-based methods are formulated in a problem-specific manner to exploit domain knowledge about the solution space. The changes in the underlying problem structure and model might, therefore, require changes in the heuristic methods as well, e.g. operators composition and parameters. Additionally, the heuristics perform with significant variance concerning the solution quality and the required execution time depending on the models’ parametrization. Adapting and fine-tuning heuristic-based methods to these changing conditions is a time-consuming process, which are usually performed in a trial-and-error manner. Thus, the objective is to take advantage of recent developments of Automated Algorithm Design to devise an approach (which could be used in the context of Production Planning Systems) that generates heuristic-based methods for different configurations of planning problems.
This talk presents preliminary results of an algorithm-generation approach meant to automate this process, exemplified on the Multi-Level Capacitated Lot-Sizing Problem (MLCLSP). The MLCLSP is an NP-complete problem from the production-planning domain used to determine optimal lot-sizes in a make-to-stock production setting. Several experiments were carried out to evaluate the ability of the proposed method to generate competitive algorithms for benchmark instances, under consideration of different algorithm components and cutoff times. Results indicate that the method is able to generate heuristic algorithms that find high-quality solutions significantly faster than the compared human-designed algorithm.