Tutorial 01 - AI Techniques for Solving Scheduling Problems
Abstract
Scheduling problems arise in various areas, including business, engineering, healthcare, and others. In this tutorial, we will first present several scheduling problems and case studies from various application domains, such as project scheduling, production planning and scheduling, employee scheduling, and timetabling. We will then provide an overview of different AI methods for solving such problems. The topics covered will include solver-independent modeling, constraint programming, metaheuristic methods, and hybrid techniques. In the second part of the tutorial, we will discuss methods that use machine learning techniques for automatic algorithm selection and heuristic algorithm design. We will demonstrate the application of these techniques in several real-world domains.
Official Website and Auxiliary Materials
Presenters
Nysret Musliu is an Associate Professor and the Head of the Christian Doppler Laboratory for AI and Optimization for Planning and Scheduling at TU Wien. His research focuses on problem solving and search in artificial intelligence, scheduling and timetabling, application of machine learning in optimization, and engineering of intelligent systems. He was Conference Chair of CPAIOR 2021, Conference/PC Co-Chair of CPAIOR 2020 and Conference/PC Co-Chair of PATAT 2018, and he is a steering committee member of PATAT conference series. He has lead several research projects funded by FWF, Christian Doppler Research Association, FFG, and several companies.
Lucas Kletzander is a postdoctoral researcher in the research unit Databases and AI, and Christian Doppler Laboratory for AI and Optimization for Planning and Scheduling, TU Wien. He did his PhD on automated solution methods for personnel scheduling problems. Currently, he works on complex real-life scheduling problems using exact, heuristic and hybrid methods and automated algorithm selection and configuration.
Florian Mischek is a postdoctoral researcher in the Christian Doppler Laboratory for AI and Optimization for Planning and Scheduling, research unit Databases and AI, TU Wien. He did his PhD on advanced automated project scheduling approaches for industrial test laboratories. Currently, he is working on multi-objective and explainable scheduling, as well as hyper-heuristics.