ORBEL 32

About the conference
What is OR
Organisation
Deadlines
Call for abstracts
Registration
   Registration
   Participants
Conference program
   Key speakers
   Speakers
   Detailed program
Location
Conference dinner
Partners
Contact
Pictures

 

Schedule

Click on a link for more details
Show all the abstracts
Download the booklet of abtracts

Thursday, February 1
08:30-09:15Welcome
09:15-09:30Opening session
09:30-10:30Plenary session - Dominique Feillet (Chair: Yasemin Arda)
10:30-11:00Coffee break
11:00-12:20Parallel sessions
  Routing Problems
Chair: Pieter Vansteenwegen
Room: 138
Emergency operations scheduling
Chair: El-Houssaine Aghezzaf
Room: 130
Algorithm design
Chair: Gerrit Janssens
Room: 126
Multiple Objectives
Chair: Filip Van Utterbeeck
Room: 120
12:20-13:30Lunch
12:25-13:25ORBEL board meeting
13:30-14:50Parallel sessions
  Integrated logistics
Chair: Kris Braekers
Room: 138
Person transportation
Chair: Célia Paquay
Room: 130
Continuous models
Chair: Nicolas Gillis
Room: 126
Integer programming
Chair: Bernard Fortz
Room: 120
14:50-15:20Coffee break
15:20-16:20Parallel sessions
  Material handling and warehousing 1
Chair: Greet Vanden Berghe
Room: 138
Operations management
Chair: Roel Leus
Room: 130
Matrix factorization
Chair: Pierre Kunsch
Room: 126
 
16:30-17:10Parallel sessions
  Material handling and warehousing 2
Chair: Katrien Ramaekers
Room: 138
Routing and local search
Chair: An Caris
Room: 130
Traffic management
Chair: Joris Walraevens
Room: 126
Pharmaceutical supply chains
Chair: Bart Smeulders
Room: 120
17:15-18:15ORBEL general assembly
18:30-...Conference dinner

Friday, February 2
09:30-10:30Plenary session - Martin Savelsbergh (Chair: Yves Crama)
10:30-10:50Coffee break
10:50-12:10Parallel sessions
  Optimization in health care
Chair: Jeroen Beliën
Room: 138
Network design
Chair: Jean-Sébastien Tancrez
Room: 130
Local search methodology
Chair: Patrick De Causmaecker
Room: 126
ORBEL Award
Chair: Frits Spieksma
Room: 120
12:10-13:00Lunch
13:00-14:00Parallel sessions
  Production and inventory management
Chair: Tony Wauters
Room: 138
Logistics 4.0
Chair: Thierry Pironet
Room: 130
Data clustering
Chair: Yves De Smet
Room: 126
Collective decision making
Chair: Bernard De Baets
Room: 120
14:10-15:10Parallel sessions
  Sport scheduling
Chair: Dries Goossens
Room: 138
Discrete choice modeling
Chair: Virginie Lurkin
Room: 130
Data classification
Chair: Ashwin Ittoo
Room: 126
 
15:10-15:30Coffee break
15:30- 16:30Plenary session - Michel Bierlaire (Chair: Michaël Schyns)
16:30- 16:45ORBEL award and closing session
16:45-18:00Closing cocktail

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

    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
    • A density-based decision tree for one-class classification
      Sarah Itani (University of Mons)
      Co-authors: Fabian Lecron & Philippe Fortemps
    • Comparison of active learning classification strategies
      Xavier Siebert (Faculté Polytechnique de Mons)
      Co-authors: Nouara Bellahdid, Moncef Abbas
      Abstract:
      Modern technologies constantly produce huge quantities of data. Because these data are often plain and unlabeled, a particular class of machine learning algorithms is devoted to help in the data annotation process. In the setting considered in this paper, the algorithm interacts with an oracle (e.g., a domain expert) to label instances from an unlabeled data set. The goal of active learning is to reduce the labeling effort from the oracle while achieving a good classification. One way to achieve this is to carefully choose which unlabeled instance to provide to the oracle such that it most improves the classifier performance. Active learning therefore consists in finding the most informative and representative sample. Informativeness measures the impact in reducing the generalization error of the model, while representativeness considers how the sample represents the underlying distribution. In early active learning research the approaches were based on informativeness, with methods such as uncertainty sampling, or query by committee. These approaches thus ignore the distribution of the data. To overcome this issue, active learning algorithms that exploit the structure of the data have been proposed. Among them, approaches based on the representativeness criterion have proved quite successful, such as clustering methods and optimal experiment design. Various approaches combining the two criteria have been studied: methods based on the informativeness of uncertainty sampling or query by committee, and a measure of density to discover the representativeness criterion, others methods combine the informativeness with semi-supervised algorithms that provide the representativeness. In this work, we review several active learning classification strategies and illustrate them with simulations to provide a comparative study between these strategies.
    • Risk bounds on statistical learning
      Boris Ndjia Njike (Université de Mons)
      Co-authors: Xavier Siebert

     
     
      ORBEL - Conference chair: Prof. A. Arda - Platform: Prof. M. Schyns.