This seminar will take place on January 28 at 15:30, online via Zoom (link below)
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Most of the production scheduling problems are known to be NP-hard combinatorial optimization problems. One method that has recently received increasing attention in the research community is the so-called hyper-heuristic. Searching in the space of simple heuristics or heuristics’ components using evolutionary techniques makes it possible to combine existing heuristics or to create completely new heuristics. In this seminar, we introduce a novel interactive procedure that supports a decision-maker in finding a personalized heuristic achieving a desired compromise of multiple competing objectives. Based on an initial set of solutions, the decision-maker has the opportunity to explore new solutions on his own. Iteratively, the search can either be limited by adding aspiration/acceptance levels or intensified in the direct surroundings of a promising solution. Pairwise comparisons are finally carried out to facilitate the identification of the most suitable solution among all the candidates discovered. The main goal is not only to find an appropriate heuristic with respect to the decision-maker's preferences but also to gain a deeper insight into the problem itself and a better understanding of possible trade-offs between competing objectives. The developed decision support system is implemented in a web-based application to efficiently guide the user and coordinate the exchange of information. During the whole procedure, the decision-maker is provided with visualizations and interfaces to easily understand information and to express her/his preferences.
Yannik Zeiträg is a PhD candidate at the Instituto Superior Técnico of the University of Lisbon (IST-UL). He received his master’s degree in mechanical engineering and management from the Technical University of Munich (TUM) in 2018. After completing his studies, he worked for a global manufacturing company in the automotive supplier industry. Responsible for production scheduling, the main challenge was to ensure efficient resource allocation throughout the entire production process. His research focuses on multi-objective optimization, as well as decision analysis and its applications to production scheduling of manufacturing companies.