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Detailed schedule
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Show all the abstracts
Thursday 30 January:
Thursday 11:15-12:30 TA-1: COMEX - Optimization 1 Room Vesale 023 - Chair: M. Schyns
Thursday 11:15-12:30 TA-2: Software and Implementation Room Vesale 020 - Chair: M. Mezmaz
Thursday 11:15-12:30 TA-3: COMEX - Smart mobility Room Vesale 025 - Chair: A. Caris
Thursday 11:15-12:30 TA-4: Systems Room Pentagone 0A11 - Chair: P. Kunsch
Thursday 14:00-15:40 TB-1: Data Analysis 1 Room Vesale 023 - Chair: X.Siebert
Thursday 14:00-15:40 TB-2: Multiple Objectives Room Vesale 020 - Chair: Y. de Smet
Thursday 14:00-15:40 TB-3: Logistics Room Vesale 025 - Chair: D. De Wolf
Thursday 14:00-15:40 TB-4: COMEX - Applications to Economy Room Pentagone 0A11 - Chair: W. Brauers
Thursday 14:00-15:40 TB-5: Networks Room Pentagone 0A07 - Chair: B. Fortz
Thursday 16:10-17:25 TC-1: Mixed-integer nonlinear programming Room Vesale 023 - Chair: Y. Crama
Thursday 16:10-17:25 TC-2: Decision Analysis 1 Room Vesale 020 - Chair: S. Eppe
- Learning a majority rule model from large sets of assignment examples
Olivier Sobrie (University of Mons / Ecole Centrale Paris) Co-authors: Olivier Sobrie
- Understanding people’s preferences: an integrated algorithm for the optimal design of discrete choice experiments with partial profiles
Daniel Palhazi Cuervo (University of Antwerp) Co-authors: Peter Goos, Roselinde Kessels and Kenneth Sörensen Abstract: Discrete choice experiments are conducted to identify the attributes that drive people’s preferences when choosing between competing options of products or services. A large number of attributes might increase the complexity of the choice task, and might have a detrimental effect on the quality of the results obtained from the choice experiment. In order to reduce the cognitive effort required by the experiment, researchers resort to experimental designs where the levels of some attributes are held constant in a choice set. These designs are called partial profile designs. In this paper, we propose an integrated algorithm for the generation of optimal designs for discrete choice experiments with partial profiles. This algorithm optimizes the set of constant attributes and the levels of the varying attributes simultaneously. An extensive computational experiment shows that the designs produced by the integrated algorithm outperform those obtained by existing algorithms, and match the optimal designs for a number of benchmark instances. We also evaluate the performance of the algorithm under varying experimental conditions and analyse the differential impact on the structure of the designs generated.
- An empirical distribution-based approximation of PROMETHEE II's net flow scores
Stefan Eppe (Université libre de Bruxelles) Co-authors: Yves De Smet
Thursday 16:10-17:25 TC-3: Routing Room Vesale 025 - Chair: K. Sörensen
Thursday 16:10-17:25 TC-4: Graphs Room Pentagone 0A11 - Chair: H. Mélot
Thursday 16:10-17:25 TC-5: Scheduling Room Pentagone 0A07 - Chair: S. Hanafi
Friday 9:00-10:15 FA-1: Queuing Room Vesale 023 - Chair: S. Wittevrongel
Friday 9:00-10:15 FA-2: Decision Analysis 2 Room Vesale 020 - Chair: R. Bisdorff
Friday 9:00-10:15 FA-3: COMEX - Optimization 2 Room Vesale 025 - Chair: M. Labbé
Friday 9:00-10:15 FA-4: Production Room Pentagone 0A11 - Chair: D. Tuyttens
Friday 14:00-15:40 FB-1: Data Analysis 2 Room Vesale 023 - Chair: P. Fortemps
Friday 14:00-15:40 FB-2: Heuristics Room Vesale 020 - Chair: T. Stützle
Friday 14:00-15:40 FB-3: COMEX - Transportation Room Vesale 025 - Chair: F. Spieksma
Friday 14:00-15:40 FB-4: Health Room Pentagone 0A11 - Chair: G. Vanden Berghe
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