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Conference dinner



Thursday, 30 January
09:10-09:30Opening sessionAmphi Cuccaroni
09:30-10:30Plenary session - Adam Letchford (Chair: Martine Labbé)Amphi Cuccaroni
10:30-11:20Coffee breakB14-B15-B16
11:20-12:40Parallel sessions
  Multi-objective Optimization
Chair: Sara Tari
Room: E501
Chair: Véronique François
Room: E502
Public Transportation
Chair: Javier Duran Micco
Room: E503
Global Optimization
Chair: Olivier Rigal
Room: E601
Analytics 1
Chair: Rafael Van Belle
Room: E602
12:40-14:10Lunch/ORBEL Board meetingB14-B15-B16/E501
14:10-15:10Parallel sessions
  Air, Rail and Multimodal Transportation
Chair: Paola Paregrini
Room: E501
Game Theory
Chair: Lotte Verdnock
Room: E502
Transportation of People
Chair: Yves Molenbruch
Room: E503
Multi-level Optimization
Chair: Concepcion Dominguez
Room: E601
Analytics 2
Chair: Vedavyas Etikala
Room: E602
15:10-15:50Coffee breakB14-B15-B16
15:50-16:40Parallel sessions
  Sport Timetabling
Chair: Dries Goossens
Room: E501
Project Management and Scheduling
Chair: Fan Yang
Room: E502
Rich Routing and Graphs
Chair: Alexandre Bontems
Room: E503
Chair: Yuan Yuan
Room: E601
Analytics 3
Chair: Jari Peeperkorn
Room: E602
16:50-17:30ORBEL general assemblyAmphi Cuccaroni
19:00-20:00CocktailBar Zango
20:30-23:00DinnerLa Terrasse des Ramparts

Friday, 31 January
09:30-10:30Plenary session - Miguel F. Anjos (Chair: Luce Brotcorne)Amphi Cuccaroni
10:30-11:20Coffee breakB14-B15-B16
11:20-12:40Parallel sessions
  Automatic Configuration and Metaheuristics Analysis
Chair: Kenneth Sorensen
Room: E501
Real Life and Integrated Problems
Chair: Jeroen Belien
Room: E502
Warehouse Management
Chair: Harol Mauricio Gamez
Room: E601
Analytics 4
Chair: Noureddine Kouaissah
Room: E602
14:10-15:10Parallel sessions
  Orbel Award
Chair: Jeroen Belien
Room: E501
Chair: Julien Dewez
Room: E502
Trucking Optimization
Chair: Hatice Çalik
Room: E601
Analytics 5
Chair: Diego Olaya
Room: E602
15:10-15:50Coffee breakB14-B15-B16
15:50-16:50Parallel sessions
  Learning and Optimization
Chair: Johan Van Kerckhoven
Room: E501
Lot-sizing and Inventory
Chair: Philippe Chevalier
Room: E502
Rich Routing
Chair: Cristian Aguayo
Room: E601
17:00-17:15ORBEL award and closing sessionAmphi Cuccaroni
17:15-18:30Closing cocktailB14-B15-B16

Thursday 11:20 - 12:40 Multi-objective Optimization
Room E501 - Chair: Sara Tari

Thursday 11:20 - 12:40 Health-care
Room E502 - Chair: Véronique François

Thursday 11:20 - 12:40 Public Transportation
Room E503 - Chair: Javier Duran Micco

Thursday 11:20 - 12:40 Global Optimization
Room E601 - Chair: Olivier Rigal

Thursday 11:20 - 12:40 Analytics 1
Room E602 - Chair: Rafael Van Belle

Thursday 14:10 - 15:10 Air, Rail and Multimodal Transportation
Room E501 - Chair: Paola Pellegrini

Thursday 14:10 - 15:10 Game Theory
Room E502 - Chair: Lotte Verdnock

Thursday 14:10 - 15:10 Transportation of People
Room E503 - Chair: Yves Molenbruch

Thursday 14:10 - 15:10 Multi-level Optimization
Room E601 - Chair: Concepcion Dominguez

Thursday 14:10 - 15:10 Analytics 2
Room E602 - Chair: Vedavyas Etikala

Thursday 15:50 - 16:50 Sport Timetabling
Room E501 - Chair: Dries Goossens

Thursday 15:50 - 16:50 Project Management and Scheduling
Room E502 - Chair: Fan Yang

Thursday 15:50 - 16:50 Rich Routing and Graphs
Room E503 - Chair: Alexandre Bontems

Thursday 15:50 - 16:50 Logistics
Room E601 - Chair: Silia Mertens

Thursday 15:50 - 16:50 Analytics 3
Room E602 - Chair: Jari Peeperkorn

Friday 11:20 - 12:40 Automatic Configuration and Metaheuristics Analysis
Room E501 - Chair: Kenneth Sorensen

Friday 11:20 - 12:40 Real Life and Integrated Problems
Room E502 - Chair: Jeroen Belien

Friday 11:20 - 12:40 Warehouse Management
Room E601 - Chair: Harol Mauricio Gamez

Friday 11:20 - 12:40 Analytics 4
Room E602 - Chair: Noureddine Kouaissah

Friday 14:10 - 15:10 Orbel Award
Room E501 - Chair: Jeroen Belien

    Friday 14:10 - 15:10 Optimization
    Room E502 - Chair: Julien Dewez

    Friday 14:10 - 15:10 Trucking Optimization
    Room E601 - Chair: Hatiz Çalik

    Friday 14:10 - 15:10 Analytics 5
    Room E602 - Chair: Diego Olaya

    Friday 15:50 - 16:50 Learning and Optimization
    Room E501 - Chair: Jorik Jooken
    • A Deep Reinforcement Learning approach to the Stochastic Inventory Problem
      Henri Dehaybe (UCLouvain)
      Co-authors: Philippe Chevalier and Daniele Catanzaro
    • Learning to optimize: a case study on a crane scheduling problem
      Jorik Jooken (KU Leuven)
      Co-authors: Pieter Leyman, Patrick De Causmaecker, Tony Wauters
      In many combinatorial optimization problems, the decision variables are all discrete. Hence, the set of all feasible solutions for a given problem instance can be represented by the leaf nodes of a tree in which a value is assigned to a decision variable at every level. In many cases, this tree is too large to be completely explored to find the best solution. However, one can still obtain very good solutions by not completely exploring the tree, but rather focusing on exploring the most promising parts of the tree. An example of an algorithm that uses this idea is Monte Carlo tree search. This algorithm has already been very successful in the context of game theory, where the most notable example is AlphaGo: a computer program that was able to defeat the world champion at the board game Go. In this presentation we demonstrate that the core principles of Monte Carlo tree search for game theory can also successfully be applied in the context of a combinatorial optimization problem. The problem on which the developed methodology was tested, is a crane scheduling problem with a non-crossing constraint. The specific variant that we have concentrated on in this presentation is the variant 1D||C_Max according to the classification scheme proposed by Boysen et al. In this problem, the goal is to assign cranes to containers, stored along a straight line, in order to minimize the makespan (the latest completion time of a crane). The tree search algorithm that is discussed in this presentation can be seen as a sampling method that samples the set of all possible solutions in a biased way (it is more likely to explore promising parts of the tree). To determine how exactly a sample will be generated, the algorithm uses two different policies: a rollout policy and a solution completion policy. The rollout policy is a stochastic policy that has to be learned by the algorithm. The solution completion policy on the other hand is a deterministic heuristic. To learn the rollout policy, the algorithm makes use of policy learning and this work hereby also contributes to recent attempts that focus on integrating machine learning into a combinatorial optimization context.

    Friday 15:50 - 16:50 Lot-sizing and Inventory
    Room E502 - Chair: Philippe Chevalier

    Friday 15:50 - 16:50 Rich Routing
    Room E601 - Chair: Cristian Aguayo

      ORBEL - Conference chairs: Diego Cattaruzza and Maxime Ogier