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Detailed schedule
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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
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
- CP Approach for the Multi-Item Discrete Lot-Sizing Problem with Sequence-Dependent Changeover Costs
Vinasetan Houndji (Université Catholique de Louvain) Co-authors: Pierre Schaus, Laurence Wolsey Abstract: The Discrete Lot Sizing and Scheduling Problem (DLSP) is a production planning problem which involves determining a minimal cost production schedule in which the machine capacity restrictions are not violated, and the demand for all products is satisfied. The planning horizon is discrete and finite. The variant considered here, called the Pigment Sequencing Problem [1], is a multi-item, single machine problem with production capacity limited to one item per period. There are storage costs and sequence-dependent changeover costs satisfying the triangle inequality. Without loss of generality, we can assume that demands are normalized and so are binary.
Pigment Sequencing Problem is an NP-Hard combinatorial optimization problem for which medium-sized instances can be solved effectively using an appropriate Mixed Integer Program formulation (such as those introduced in [1]). However, as far as we know, no CP model have been proposed for it.
We propose two CP models for the problem:
- the first mainly uses a global cardinality constraint [2] to enforce the production of all items required and some "atleast" constraints so as to respect each demand deadline ;
- the second is similar to the successor CP model for the Traveling Salesman Problem (TSP) where the cities to be visited represent the different demands and the distance between them are the corresponding transition (changeover) costs. The distinction with the classical TSP lies in the storage costs which must be taken into account.
We use Large Neighborhood Search (LNS) [3] with some variants of relaxation procedures and heuristic search in order to quickly discover close to optimal solutions.
The results show that Constraint Programming is potentially an effective approach for tackling Discrete Lot Sizing Problems.
References
[1] Pochet, Y, Wolsey, L. : Production Planning by Mixed Integer Programming.
Springer, New York (2005)
[2] Régin, J-C. : Generalized arc consistency for global cardinality constraint.
Proceedings of Association for the Advancement of Artificial Intelligence (AAAI) conference on Artificial Intelligence, Portland, USA. 209--215 (1996)
[3] Shaw, P. : Using constraint programming and local search methods to solve vehicle routing problems.
Proceedings of Principles and Practice of Constraint Programming conference, Pisa, Italy. 417--431 (1998)
- Local search approaches for 2D nesting problems
Tony Wauters (KU Leuven, Department of Computer Science, CODeS - iMinds-ITEC) Co-authors: Sam Van Malderen, Greet Vanden Berghe
- Exact resolution of the Cover Printing Problem
Arnaud Vandaele (UMONS, Faculté Polytechnique) Co-authors: Daniel 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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