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.
The talk describes how commercial companies create operating systems with highly integrated services, which scientists use in every phase of their daily work and which by the way produce data about this work. These data, in turn, are processed by the commercial providers and converted into further products, which are now offered to the science bureaucracy as a tool for recruitment and research planning. The structure and marketing of both the tools for scientists and the controlling tools for the administration have features that are widely known from electronic environments (compliance through convenience, vendor-lock-in), but also features that show at the same time elements of the centrally planned economy and (although at first sight incompatible with it) a strong competitive connotation. The presentation also discusses the possible consequences of such a data-driven science control for individual researchers as well as for science as a social enterprise.
In the context of approaches for mobile software development, we present our concerns on the level of accessibility and usability for their interface components. We understand that model-driven development (MDD) and cross-platform mobile development, together, may offer benefits to solve the gap and benefit the development of more accessible mobile applications. This presentation discusses the investigations and trends to include requirements for accessibility in interface components during the MDD process for mobile development.
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).
Whereas digitization offers countless opportunities to individuals as well as to organizations, public administration lacks behind in making use of advanced technologies and applying information management tools. However, as public authorities have recognized the potential of digitization for service delivery and administrative organization their efforts in establishing common digitization strategies, institutions, and electronic service delivery are faced with several challenges. Recent and ongoing research found that problems and hurdles with the implementation of all these approaches are manifold including given administrative structures, civil servants competencies or financial restrictions. In Germany, for instance, one major structural hurdle is the constitutional given federalism, which internally slows down decision processes on eGovernment actions and complicates the implementation of common eGovernment solutions like the planned interconnected portals. Externally, citizens and companies in federations are faced with confusing responsibilities not knowing where to apply for a public service. Therefore, it must be the aim of highest priority for the public sector to advance eGovernment solutions to gain a higher degree of internal efficiency, to provide citizens with convenient services and responsibilities and, lastly, to be prepared for future tasks.
How to master the "E" - Tools for Competence Identification, Provision and Preservation in a Digitalized Public Sector
The digitalization of every aspect of life is in full swing and becoming an all-embracing societal phenomenon. Public administrations worldwide, eager to increase their efficiency and effectiveness, are in a change process, induced by the pervasiveness of technological advancements. This development does not only mean the substitution of analogue processes by the integration of information technologies, but first and foremost leads to changing demands on the tasks, roles and competences of the ones, who need to implement those changes, i.e. the public servants. The study we conducted therefore explores relevant roles and respective competences with regard to IT in public administrations and offers tools for their successful preservation to master this e-induced change: 19 reference roles in public administrations are identified that are important for the implementation of eGovernment. In addition role fact sheets and competence matrices as possible means for the identification and documentation will be presented, which in turn can be supportive of a possible future competence preservation.
Assessing and improving the maturity of spare parts supply chains in the mechanical engineering industry
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.
"Model-Driven Software Development is widely adopted throughout research and industry" is a sentence that often opens scientific papers regarding model-driven software engineering. While it is true that models are widely adopted they are often not utilized to their full extent. Therefore, efficiency enhancement potential and inherent insights remain unused. The talk presents the current status of research on increasing model utilization in software engineering. The audience will understand how model enrichment, integration, and collaboration enable leveraging the model inherent knowledge. Additionally, research findings from investigating the three approaches will be presented.
This talk addresses a new perspective on multimodality of multi-objective (MO) optimization landscapes. In Operations Research, we learn that local optima are traps for local opimizers. Interestingly, this fact is simply transfered to multi-objective optimization as certainty as well, although we do not have enough insights into multi-objective landscape characteristics. In our work, we use sophisticated visualization techniques, which rely on gradient fi eld heatmaps and enable a perception of multi-objective landscapes for the first time. We show that local efficient sets in a multi-objective setting are not necessarily traps but can assist optimizers in fi nding global ecient sets. Finally, the simple MO local optimizer MOGSA is introduced, which exploits those observations by sliding down the multi-objective gradient hill and moving along the local efficient sets.
Lunchtime Seminar - Towards the automated Design of Heuristic-Based algorithms for lot-sizing problems
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.