Essence: A constraint language for specifying combinatorial problems

Frisch, Alan M.; Harvey, Warwick; Jefferson, Christopher; Martínez‐Hernández, Bernadette; Miguel, Ian

doi:10.1007/s10601-008-9047-y

Cited by 131 publications

(142 citation statements)

References 15 publications

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“…Indeed, starting from a solver-independent MiniZinc model every solver can produce its own FlatZinc model by using solver-specific redefinitions. Apart from MiniZinc, other solver-independent modelling languages are ESRA [62], Essence [68], and OPL [180].…”

Section: Integer Variablesmentioning

confidence: 99%

Portfolio approaches in constraint programming

Amadini

2015

Constraints

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Recent research has shown that the performance of a single, arbitrarily efficient algorithm can be significantly outperformed by using a portfolio of -possibly onaverage slower-algorithms. Within the Constraint Programming (CP) context, a portfolio solver can be seen as a particular constraint solver that exploits the synergy between the constituent solvers of its portfolio for predicting which is (or which are) the best solver(s) to run for solving a new, unseen instance.In this thesis we examine the benefits of portfolio solvers in CP. Despite portfolio approaches have been extensively studied for Boolean Satisfiability (SAT) problems, in the more general CP field these techniques have been only marginally studied and used. We conducted this work through the investigation, the analysis and the construction of several portfolio approaches for solving both satisfaction and optimization problems. We focused in particular on sequential approaches, i.e., single-threaded portfolio solvers always running on the same core.We started from a first empirical evaluation on portfolio approaches for solving Constraint Satisfaction Problems (CSPs), and then we improved on it by introducing new data, solvers, features, algorithms, and tools. Afterwards, we addressed the more general Constraint Optimization Problems (COPs) by implementing and testing a number of models for dealing with COP portfolio solvers. Finally, we have come full circle by developing sunny-cp: a sequential CP portfolio solver that turned out to be competitive also in the MiniZinc Challenge, the reference competition for CP solvers.iii iv

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Section: Integer Variablesmentioning

confidence: 99%

Portfolio approaches in constraint programming

Amadini

2015

Constraints

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“…We use the Savile Row tool that converts constraint problem models formulated in the solver-independent modelling language Essence [20] to the input format of the Constraint Satisfaction Problem (CSP) solver Minion [22].…”

Section: Existing Approaches To Qmmentioning

confidence: 99%

Qualitative modelling via constraint programming

et al. 2014

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Abstract. Qualitative modelling is a technique integrating the fields of theoretical computer science, artificial intelligence and the physical and biological sciences. The aim is to be able to model the behaviour of systems without estimating parameter values and fixing the exact quantitative dynamics. Traditional applications are the study of the dynamics of physical and biological systems at a higher level of abstraction than that obtained by estimation of numerical parameter values for a fixed quantitative model. Qualitative modelling has been studied and implemented to varying degrees of sophistication in Petri nets, process calculi and constraint programming. In this paper we reflect on the strengths and weaknesses of existing frameworks, we demonstrate how recent advances in constraint programming can be leveraged to produce high quality qualitative models, and we describe the advances in theory and technology that would be needed to make constraint programming the best option for scientific investigation in the broadest sense.

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“…In the last three decades several languages have been developed for modeling combinatorial optimization problems including OPL [9], ASPEN [8], COMET [10], ESRA [6], ESSENCE [7] and Zinc [11].…”

Section: Related Modeling Languagesmentioning

confidence: 99%

TEMPLE - A domain specific language for modeling and solving staff scheduling problems

Gärtner¹,

Musliu

Schafhauser³

et al. 2011

2011 IEEE Symposium on Computational Intelligence in Scheduling (SCIS)

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Abstract-We present TEMPLE, a domain specific language for modeling and solving staff scheduling problems. TEMPLE provides a set of intuitive abstractions and notations allowing to formulate the constraints of a particular problem in a very compact and natural way. After modeling a staff scheduling problem in TEMPLE, three generic local search algorithms can immediately be applied to the corresponding optimization problem. We show how real-life staff scheduling problems can be both effectively modeled as well as efficiently solved using our approach. Finally, we report on a practical application of TEMPLE in a commercial staff scheduling software.

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Essence: A constraint language for specifying combinatorial problems

Cited by 131 publications

References 15 publications

Portfolio approaches in constraint programming

Portfolio approaches in constraint programming

Qualitative modelling via constraint programming

TEMPLE - A domain specific language for modeling and solving staff scheduling problems

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