Constraint Solving in Uncertain and Dynamic Environments: A Survey

Verfaillie, Gérard; Jussien, Narendra

doi:10.1007/s10601-005-2239-9

Cited by 86 publications

(71 citation statements)

References 51 publications

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“…Cette approche regroupe plusieurs méthodes qui ne diffèrent l'une de l'autre que par le type des informations stockées avec chaque contrainte induite. Nous pouvons citer les méthodes basées sur les justifications et les méthodes basées sur les explications (Verfaillie et Jussien, 2005). La principale différence entre les deux méthodes est la nature de l'information enregistrée avec chaque contrainte induite.…”

Section: -L'approche Par Réutilisation Du Raisonnementunclassified

Planification de chaîne logistique sous incertitude : maintien de solution par satisfaction de contraintes dynamiques

Trojet¹,

H'Mida²,

López³

et al. 2016

Journal Européen des Systèmes Automatisés

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Section: -L'approche Par Réutilisation Du Raisonnementunclassified

Planification de chaîne logistique sous incertitude : maintien de solution par satisfaction de contraintes dynamiques

Trojet¹,

H'Mida²,

López³

et al. 2016

Journal Européen des Systèmes Automatisés

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“…For instance the possibility of deleting a constraint at runtime has been considered by some extensions like the ones described in [124] but this kind of operation are yet too costly to be performed.…”

Section: Limitationsmentioning

confidence: 99%

Constraints Meet Concurrency

Mauro

2014

Atlantis Studies in Computing

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We investigate the benefits that emerge when the fields of constraint programming and concurrency meet. On one hand, constraints can be use in concurrency theory to increase the conciseness and the expressive power of concurrent languages from a pragmatic point of view. On the other hand, problems modeled by using constraints can be solved faster and more efficiently using a concurrent system. We explore both directions providing two separate lines of contribution. Firstly we study the expressive power of a concurrent language, namely Constraint Handling Rules, that supports constraints as a primitive construct. We show what features of this language make it Turing powerful. Then we propose a framework to solve constraint problems that is intended to be deployed on a concurrent system. For the development of this framework we used the concurrent language Jolie following the Service Oriented paradigm. Based on this experience, we also propose an extension to Service Oriented Languages to overcome some of their limitations and to improve the development of concurrent applications. Needless to say, I am also in debt with my coauthors:

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“…Unfortunately, these solutions are usually tested only in static situation, i.e., when all the jobs are known to the scheduler before execution. One of our main interest is the dynamic aspect of scheduling [25,4,16], i.e., scheduling of jobs arriving during the system run [3,12], sometimes referred to as incremental scheduling [16] or on-line reasoning [4].…”

Section: Introductionmentioning

confidence: 99%

Alea – Grid Scheduling Simulation Environment

Klusáček

Matyska

Rudová

Parallel Processing and Applied Mathematics

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Abstract. This work concentrates on the design of a system intended for study of advanced scheduling techniques for planning various types of jobs in a Grid environment. The solution is able to deal with common problems of the job scheduling in Grids like heterogeneity of jobs and resources, and dynamic runtime changes such as arrivals of new jobs. Our new simulator called Alea is based on the GridSim simulation toolkit which we extended to provide a simulation environment that supports simulation of varying Grid scheduling problems. To demonstrate the features of the GridSim environment, we implemented an experimental centralised Grid scheduler which uses advanced scheduling techniques for schedule generation. By now local search based algorithms and some dispatching rules were tested. The scheduler is capable to handle both static and dynamic situation. In the static case, all jobs are known in advance while the dynamic situation means that jobs appear in the system during simulation. In this case generated schedule is changing through time as some jobs are already finished while the new ones are arriving. Comparison of FCFS, local search and dispatching rules is presented for both cases and we demonstrate that the new local search based algorithm provides the best schedule while keeping the running time acceptable.

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Constraint Solving in Uncertain and Dynamic Environments: A Survey

Abstract: This article follows a tutorial, given by the authors on dynamic constraint solving at CP 2003 [87]. It aims at offering an overview of the main approaches and techniques that have been proposed in the domain of constraint satisfaction to deal with uncertain and dynamic environments.

Cited by 86 publications

References 51 publications

Planification de chaîne logistique sous incertitude : maintien de solution par satisfaction de contraintes dynamiques

Planification de chaîne logistique sous incertitude : maintien de solution par satisfaction de contraintes dynamiques

Constraints Meet Concurrency

Alea – Grid Scheduling Simulation Environment

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