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Schedule
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Thursday 11:00 - 12:20 Routing Problems Room 138 - Chair: Pieter Vansteenwegen
Thursday 11:00 - 12:20 Emergency operations scheduling Room 130 - Chair: El-Houssaine Aghezzaf
Thursday 11:00 - 12:20 Algorithm design Room 126 - Chair: Gerrit Janssens
Thursday 11:00 - 12:20 Multiple Objectives Room 120 - Chair: Filip Van Utterbeeck
Thursday 13:30 - 14:50 Integrated logistics Room 138 - Chair: Kris Braekers
Thursday 13:30 - 14:50 Person transportation Room 130 - Chair: Célia Paquay
Thursday 13:30 - 14:50 Continuous models Room 126 - Chair: Nicolas Gillis
Thursday 13:30 - 14:50 Integer programming Room 120 - Chair: Bernard Fortz
Thursday 15:20 - 16:20 Material handling and warehousing 1 Room 138 - Chair: Greet Vanden Berghe
Thursday 15:20 - 16:20 Operations management Room 130 - Chair: Roel Leus
Thursday 15:20 - 16:20 Matrix factorization Room 126 - Chair: Pierre Kunsch
Thursday 16:30 - 17:10 Material handling and warehousing 2 Room 138 - Chair: Katrien Ramaekers
Thursday 16:30 - 17:10 Routing and local search Room 130 - Chair: An Caris
Thursday 16:30 - 17:10 Traffic management Room 126 - Chair: Joris Walraevens
Thursday 16:30 - 17:10 Pharmaceutical supply chains Room 120 - Chair: Bart Smeulders
Friday 10:50 - 12:10 Optimization in health care Room 138 - Chair: Jeroen Beliën
Friday 10:50 - 12:10 Network design Room 130 - Chair: Jean-Sébastien Tancrez
Friday 10:50 - 12:10 Local search methodology Room 126 - Chair: Patrick De Causmaecker
Friday 10:50 - 12:10 ORBEL Award Room 120 - Chair: Frits Spieksma
Friday 13:00 - 14:00 Production and inventory management Room 138 - Chair: Tony Wauters
Friday 13:00 - 14:00 Logistics 4.0 Room 130 - Chair: Thierry Pironet
Friday 13:00 - 14:00 Data clustering Room 126 - Chair: Yves De Smet
Friday 13:00 - 14:00 Collective decision making Room 120 - Chair: Bernard De Baets
Friday 14:10 - 15:10 Sport scheduling Room 138 - Chair: Dries Goossens
- Scheduling time-relaxed double round-robin tournaments with availability constraints
David Van Bulck (Ghent University) Co-authors: Dries Goossens
- Combined proactive and reactive strategies for round robin football scheduling
Xia-jie Yi (Ghent University) Co-authors: Dries Goossens
- A constructive matheuristic strategy for the Traveling Umpire Problem
Reshma Chirayil Chandrasekharan (KU Leuven) Co-authors: Tulio A. M. Toffolo, Tony Wauters Abstract: Traveling Umpire problem (TUP) is a sports scheduling problem concerning the assignment of umpires to the games of a fixed round robin tournament. Introduced by Michael Trick and Hakan Yildiz in 2007, the problem abstracts the umpire scheduling problem in the American Major League Baseball (MLB). A typical season of MLB requires umpires to travel extensively between team venues and therefore, the primary aim of the problem is to assign umpires such that the total travel distance is minimized. Constraints that prevent assigning umpires to consecutive teams or team venues make the problem challenging. Instances comprising of 8 to 30 teams have been proposed and since the introduction, various exact and heuristic techniques have been employed to obtain near optimal solutions to small and medium-sized instances. However, exact techniques prove to be inefficient in producing high quality solutions for large instances of 26 to 32 teams which resembles the real world problem. The present work focuses on improving the solutions for the large instance and presents a decomposition based method implementing constructive matheuristics (CMH). A given problem is decomposed into subproblems of which IP formulations are solved sequentially to optimality. These optimal solutions of the subproblems are utilized to construct a solution for the full problem. Various algorithmic parameters are implemented and extensive experiments are conducted to study their effects in the final solution quality. The algorithm being constructive, parameters have to be tuned such that the solution for the current subproblem does not prevent the feasibility of the future subproblems. In addition, design parameters are utilized to ensure feasibility of constraints that cannot be locally evaluated with in each subproblem. Parameters such as size of the subproblems and amount of overlap between the subproblems are tuned such that the solution quality is maximized while the runtime lies within the benchmark time limit of 5 hours. Design parameters such as the objective function and future of subproblems ensure that the solution constructed from the optimal solutions of the subproblems continues to be feasible in terms of the full problem.
The proposed method has been able to improve the current best solutions of all the large instances within the benchmark time limits. In addition, CMH is also able to improve solutions of two medium sized instances of 18 teams. Furthermore, CMH generates solutions those are comparable or better than the solutions generated by other similar heuristics. Experiments conducted so far on the TUP suggest the possibility of CMH being applied on other similar problems. Apart from the innovations in terms of design parameters that may improve the CMH algorithm, the CMH has the inherent property of getting faster with the evolution of better IP solvers. Insights on the applicability of CMH to similar optimization problems and the expected advantages or shortcomings may also be discussed.
Friday 14:10 - 15:10 Discrete choice modeling Room 130 - Chair: Virginie Lurkin
Friday 14:10 - 15:10 Data classification Room 126 - Chair: Ashwin Ittoo
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ORBEL - Conference chair: Prof. A. Arda -
Platform: Prof. M. Schyns.
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