TBA
David Pisinger is professor in operations research at DTU Management. David graduated in mathematics and computer science in 1990, and finished his PhD at University of Copenhagen in 1995. His main research topics include; metaheuristics, transportation problems, machine learning in optimization, and offshore wind farm design. He has written more than 100 papers in leading journals, and a monograph on Knapsack Problems. David Pisinger received the PhD prize of the Danish Academy of Natural Sciences in 1995 for his thesis on Knapsack Problems. Moreover he received the Hedorfs Fonds prize for Transport Research 2013; award of teaching excellence Faculty of Natural Sciences, University of Copenhagen 2000; and the Teaching Prize at DTU Management in 2016, 2024. He received the Glover-Klingman prize in 2018, and in 2019 he was one of the finalists for the Franz Edelman Award. In 2022 he received an ERC advanced grant for the project "DECIDE". David has supervised more than 30 PhD students, many of which have received awards and distinctions. He is Area editor of Transportation Science, and Associate editor of TR-C and FLEX. Having a background in Knapsack Problems, David can be recognized on always wearing a knapsack.
Abstract: We present a general local search framework for scenario-based two-stage combinatorial stochastic programming problems. The framework is based on Adaptive Large Neighborhood Search (ALNS), where an upper-level local search method operates on the first-stage decision variables, while a number of lower-level local search methods optimize the individual scenarios. As a demonstration of the Stochastic ALNS algorithm, the framework is tested on the two-stage stochastic Prize-collecting Vehicle Routing Problem, and stochastic Team Orienteering Problem. In all problems, the customers, demands and travel times can be stochastic as long as they can be represented by a number of scenarios. Computational experiments show that the algorithm scales well with the number of scenarios, making it possible to solve instances with up to 1000 customers in short time.
Hana Rudová is an associate professor at Masaryk University in Brno, Czech Republic, where she works on scheduling and routing problems and serves as the head of the Department of Machine Learning and Data Processing. Her work is inspired by real-life problems arising from practice, and she concentrates on approaches that enable the solution of practical problems, such as course timetabling in the UniTime system, vehicle routing with Wereldo company, warehouse planning with Notino company, or computer job scheduling in the CERIT national infrastructure. She is the associate editor of the Journal of Scheduling, a member of the SoCS Council, and a member of the PATAT Steering Committee. She co-chaired the Symposium on Combinatorial Search 2025 and the PATAT 2006 conference in her hometown, as well as several Application tracks at the ICAPS conference. She co-organized the International Timetabling Competition (ITC 2019) with almost 700 registered users from 80 countries, preparing benchmarks from ten universities on five continents.
Kevin Tierney is currently Professor of Production and Logistics at the University of Vienna in Austria. He was previously Professor of Decision and Operation Technologies at Bielefeld University in Germany and Assistant Professor of Decision Support Systems and Operations Research at Paderborn University in Germany. His PhD is from the IT University of Copenhagen in Denmark where he wrote his thesis on optimizing liner shipping fleet repositioning. His work focuses on the intersection of machine learning and optimization, specifically how to make optimization solvers faster and data-driven through the integration of modern machine learning techniques. His work has won best paper awards at GECCO, ECAI and from the EURO for best review paper at EJOR. He has won multiple competitions with his work, including both tracks at the IJCAI AI4TSP competition and multiple awards at the MaxSAT Evaluation 2016. He is currently the coordinator of the MSCA Doctoral Network “Confident Data-Driven Decision Support (CoRDS).”