ECL<sup>i</sup>PS<sup>e</sup> – From LP to CLP

Schimpf, Joachim; Shen, Kish

doi:10.1017/s1471068411000469

Cited by 55 publications

(41 citation statements)

References 25 publications

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“…Some important milestones include [6,46,44,51,26,52,45,4,47]. Some important milestones include [6,46,44,51,26,52,45,4,47].…”

Section: Separation Logic(s)mentioning

confidence: 99%

“…The all-path criterion requires covering all feasible program paths of p. Since the exhaustive exploration of all paths is usually impossible for real-life programs, the k-path criterion restricts exploration to paths with at most k consecutive iterations of each loop. This is done in the ECLiPSe Prolog environment [47] and uses Constraint Logic Programming. DART, CUTE, PEX, SAGE, KLEE).…”

Section: Fundamental Analysis: Concolic Testingmentioning

confidence: 99%

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Software Engineering and Formal Methods

2012

Lecture Notes in Computer Science

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“…Some important milestones include [6,46,44,51,26,52,45,4,47]. Some important milestones include [6,46,44,51,26,52,45,4,47].…”

Section: Separation Logic(s)mentioning

confidence: 99%

Section: Fundamental Analysis: Concolic Testingmentioning

confidence: 99%

Software Engineering and Formal Methods

2012

Lecture Notes in Computer Science

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“…• a new implementation in the language Comet, while the previous was in the logic language ECL i PS e [52]. Comet has more global constraints, that have powerful constraint propagation, and allowed us to improve the efficiency of the application; • new search algorithms, based on Large Neighbourhood Search, that let us get to better solutions in shorter time; • a wider experimentation;…”

Section: Introductionmentioning

confidence: 99%

An application of constraint solving for home health care

et al. 2015

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Although sometimes it is necessary, no one likes to stay in a hospital, and patients who need to stay in bed but do not require constant medical surveillance prefer their own bed at home. At the same time, a patient in a hospital has a high cost for the community, that is not acceptable if the patient needs service only a few minutes a day. For these reasons, the current trend in Europe and North-America is to send nurses to visit patients at their home: this choice reduces costs for the community and gives better quality of life to patients. The challenge is to deliver the service in a cost effective manner without a detriment of the service quality. These social and health management issues have interesting implications from the mathematical viewpoint, introducing a challenging combinatorial optimization problem. The problem consists in assigning patients' services to traveling nurses and defining the nurse itineraries so that the following optimization aspects are considered: the nurse workloads (including service as well as travel time) are balanced, patients are preferentially served by a single nurse or just a few ones, and the overall travel time is minimized. These objectives are somehow conflicting and a reasonable trade off must be found. The complexity of the problem calls for suitable optimization-based algorithmic support to decisions, in particular in the perspective of an increasing diffusion of the service.This problem is known in the literature as the Home Health Care (HHC) problem. In this paper, we address the HHC problem in the municipality of Ferrara, a mid-sized city in the North of Italy. The problem is currently solved by hand, starting from a partitioning of patients based on predefined zones. We describe a Constraint Programming model that solves the HHC problem, and show significant improvements with respect to the current manual solution. those resources can be spent for providing a better service to other patients. Moreover, patients perceive a higher quality of life when they stay at home, with their dear ones, and feel their illness more similar to a "normal" life situation. This reduces depression risk for the patient, and improves rehabilitation rate. Indeed, high quality home health care following hospital dismissal has proved essential in reducing hospital readmissions, if able to cope with preventable complications. Therefore, it is crucial to deliver the service in a cost effective manner while not deteriorating service quality. Arguments in favor of home health care and how to re-

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“…When sorting can be achieved in linear time, an O(n) bounds(Z) consistency propagator for Sort(L, S ) was introduced by Mehlhorn and Thiel [15], maintaining convexity [14] of the underlying bipartite graph. It was generalised [27] for the Sort(L, P −1 , S ) constraint of SICStus [11,10], ECLiPSe [26], and Gecode [13,19], at the expense of bounds consistency. The latter two provide both versions, Choco [21] and or-tools [18] only provide the two-argument version, whereas all other CP solvers we checked provide neither of them.…”

Section: The Sort Constraints and Their Limitationsmentioning

confidence: 99%

“…Unfortunately, Cumulative usually does not provide variables representing the resource profile, although the ECLiPSe [26] constraint Profile does, so in most CP solvers, we cannot use Smooth in a CuSP context. In order to address this issue, we introduce the CumulativeSmooth(T , c, N , τ ) constraint, which combines a Cumulative constraint with a Smooth constraint on variables representing the profile.…”

Section: The Cumulativesmooth Constraintmentioning

confidence: 99%

A Modelling Pearl with Sortedness Constraints

Beldiceanu

Carlsson²,

Flener³

et al.

EPiC Series in Computing

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Some constraint programming solvers and constraint modelling languages feature the Sort(L, P , S ) constraint, which holds if S is a nondecreasing rearrangement of the list L, the permutation being made explicit by the optional list P . However, such sortedness constraints do not seem to be used much in practice. We argue that reasons for this neglect are that it is impossible to require the underlying sort to be stable, so that Sort cannot be guaranteed to be a total-function constraint, and that L cannot contain tuples of variables, some of which form the key for the sort. To overcome these limitations, we introduce the StableKeysort constraint, decompose it using existing constraints, and propose a propagator. This new constraint enables a powerful modelling idiom, which we illustrate by elegant and scalable models of two problems that are otherwise hard to encode as constraint programs. MotivationIn order to motivate our work, we consider the following human resource planning problem, which will serve as running example throughout this paper. Consider n tasks that are to be assigned to a given set of m employees. For each task i ∈ 1..n, let attribute A i denote the initially unknown employee performing it, B i its fixed beginning time, D i its fixed duration, and E i its fixed end time, with E i = B i + D i for all i ∈ 1..n. A first constraint is that tasks assigned to the same employee should not overlap in time. A second constraint deals with the allocation of breaks to each employee. This is done by stating a Shift constraint, which we now describe. A shift is a maximum sequence of tasks assigned to a same employee such that the time gap between any two consecutive tasks is shorter than a given threshold minBreak : that is, consecutive shifts of an employee are separated by a break of minimum duration minBreak , and consecutive tasks of a shift are separated by a gap shorter than minBreak . The span of a shift is the difference between the end time of its last task and the beginning time of its first task: see Figure 1. The Shift constraint holds if and only if the span of each shift of each employee is at most a given threshold maxSpan.

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ECLⁱPS^e – From LP to CLP

Cited by 55 publications

References 25 publications

Software Engineering and Formal Methods

Software Engineering and Formal Methods

An application of constraint solving for home health care

A Modelling Pearl with Sortedness Constraints

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ECLiPSe – From LP to CLP

Cited by 55 publications

References 25 publications

Software Engineering and Formal Methods

Software Engineering and Formal Methods

An application of constraint solving for home health care

A Modelling Pearl with Sortedness Constraints

Contact Info

Product

Resources

About

ECLⁱPS^e – From LP to CLP