5 research outputs found

    Solving dynamic resource constraint project scheduling problems using new constraint programming tools

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    Timetabling problems have been studied a lot over the last decade. Due to the complexity and the variety of such problems, most work concern static problems in which activities to schedule and resources are known in advance, and constraints are fixed. However, every timetabling problem is subject to unexpected events (consider for example, for university timetabling problems, a missing teacher, or a slide projector breakdoawn). In such a situation, one has to quickly build a new solution which takes these events into account and which is preferably not too different form the current one. We introduce in this paper constraint-programming based tools for solving dynamic timetabling problems modelled as RCPSP (Resource-Constrained Project Scheduling Problems). This approach uses explanation-based constraint programming and operational research techniques

    Scheduling of biological samples for DNA sequencing

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    Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 95-97).In a DNA sequencing workflow, a biological sample has to pass through multiple process steps. Two consecutive steps are hydroshearing and library construction. Samples arrive randomly into the inventory and are to complete both processes before their due dates. The research project is to decide the optimal sequence of samples to go through these two processes subject to operational constraints. Two approaches, namely, heuristic and integer programming have been pursued in this thesis. A heuristic algorithm is proposed to solve the scheduling problem. A variant of the problem involving deterministic arrivals of samples is also considered for comparison purposes. Comparison tests between the two approaches are carried out to investigate the performance of the proposed heuristic for the original problem and its variant. Sensitivity analysis of the schedule to parameters of the problem is also conducted when using both approaches.by Yuwei Hu and Chin Soon Lim.S.M

    Ordonnancement cumulatif avec dépassements de capacité (Contrainte globale et décompositions)

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    La programmation par contraintes est une approche intéressante pour traiter des problèmes d ordonnancement. En ordonnancement cumulatif, les activités sont définies par leur date de début, leur durée et la quantité de ressource nécessaire à leur exécution. La ressource totale disponible (la capacité) en chaque instant est fixe. La contrainte globale Cumulative modélise ce problème en programmation par contraintes. Dans de nombreux cas pratiques, la date limite de fin d un projet est fixée et ne peut être retardée. Dans ce cas, il n est pas toujours possible de trouver un ordonnancement des activités qui n engendre aucun dépassement de la capacité en ressource. On peut alors tolérer de relâcher la contrainte de capacité, dans une limite raisonnable, pour obtenir une solution. Nous proposons une nouvelle contrainte globale : la contrainte SoftCumulative qui étend la contrainte Cumulative pour prendre en compte ces dépassements de capacité. Nous illustrons son pouvoir de modélisation sur plusieurs problèmes pratiques, et présentons différents algorithmes de filtrage. Nous adaptons notamment les algorithmes de balayage et d Edge-Finding à la contrainte SoftCumulative. Nous montrons également que certains problèmes pratiques nécessitent d imposer des violations de capacité pour satisfaire des règles métiers, modélisées par des contraintes additionnelles. Nous présentons une procédure de filtrage originale pour traiter ces dépassements imposés. Nous complétons notre étude par une approche par décomposition. Enfin, nous testons et validons nos différentes techniques de résolution par une série d expériences.Constraint programming is an interesting approach to solve scheduling problems. In cumulative scheduling, activities are defined by their starting date, their duration and the amount of resource necessary for their execution. The total available resource at each point in time (the capacity) is fixed. In constraint programming, the Cumulative global constraint models this problem. In several practical cases, the deadline of theproject is fixed and can not be delayed. In this case, it is not always possible to find a schedule that does not lead to an overload of the resource capacity. It can be tolerated to relax the capacity constraint, in a reasonable limit, to obtain a solution. We propose a new global constraint : the SoftCumulative constraint that extends the Cumulative constraint to handle these overloads. We illustrate its modeling power on several practical problems, and we present various filtering algorithms. In particular, we adapt the sweep and Edge-Finding algorithms to the SoftCumulative constraint. We also show that some practical problems require to impose overloads to satisfy business rules, modelled by additional constraints. We present an original filtering procedure to deal with these imposed overloads. We complete our study by an approach by decomposition. At last, we test and validate our different resolution techniques through a series of experiments.NANTES-ENS Mines (441092314) / SudocSudocFranceF

    Planung und Modellierung des Rückbaus kerntechnischer Anlagen unter der Berücksichtigung von Unsicherheiten – Ein Beispiel zur Planung von Großprojekten

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    Großprojekte wie der Rückbau kerntechnischer Anlagen besitzen u.a. aufgrund einer unzureichenden Planung häufig Kostenüberschreitungen und länger als geplante Ausführungsdauern. Im Rahmen dieser Arbeit wurde ein Planungswerkzeug für Großprojekte entwickelt, welches exemplarisch die Anforderungen kerntechnischer Rückbauprojekte berücksichtigt. Das Ziel der Planung ist die Identifikation eines Plans auf operativer Ebene, der minimale Kosten aufweist und der Unsicherheiten berücksichtigt
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