Carmen Gervet

Université de Montpellier | UM1 · Département Informatique

Local consistency techniques have been introduced in logic programming in order to extend the application domain of logic programming languages. The existing languages based on these techniques consider arithmetic constraints applied to variables ranging over finite integer domains. This makes difficult a natural and concise modelling as well as an...

Combinatorial problems involving sets and relations are currently tackled by integer programming and expressed with vectors or matrices of 0-1 variables. This is efficient but not flexible and unnatural in problem formulation. Toward a natural programming of combinatorial problems based on sets, graphs or relations, we define a new CLP language wit...

. The industrial and commercial worlds are increasingly competitive, requiring companies to be more productiveand more responsive to market changes (e.g. globalisation and privatisation). As a consequence, there is a strong need for solutions to large scale optimization problems, in domains such as production scheduling, transport, finance and netw...

In this paper we describe an iterative development process to deal with speculative constraint optimization projects. Speculative projects are ill-defined in nature, as they are often new to the customer organization who wishes to anticipate market changes, but also because their main complexity lies in completing the problem definition. We conside...

Constraint Programming (CP) has proved an effective paradigm to model and solve difficult combinatorial satisfaction and optimisation problems from disparate domains. Many such problems arising from the commercial world are permeated by data uncertainty. Existing CP approaches that accommodate uncertainty are less suited to uncertainty arising due...

The environmental and multi-sectoral challenges faced by small islands requires consideration of sustainability issues. The sustainability challenges in these regions involve in particular the achievement of a greater autonomy through the development of local resources. This is a complex system that encompasses interconnections between the resource...

In this paper we are interested in identifying insightful changes in climate observations series, through outlier detection techniques. Discords are outliers that cover a certain length instead of being a single point in the time series. The choice of the length can be critical, leading to works on computing variable length discords. This increases...

Human development in a context of climate change is one of the great challenges in West Africa. In particular, the low access-toelectricity (52% on average) means that future electrification of the region will follow a certain path, according to the decisions to be taken. If we want this path sustainable, energy transition and renewable power may r...

The definition and extraction of actionable anomalous discords, i.e. pattern outliers, is a challenging problem in data analysis. It raises the crucial issue of identifying criteria that would render a discord more insightful than another one. In this paper, we propose an approach to address this by introducing the concept of prominent discord. The...

Increasing the share of renewable energy sources in power systems is key to a successful energy transition.Optimal renewable site selection requires a holistic approach, involving land, resources, environmental and economical data and constraints. In this paper we consider the problem of solar PV penetration into the power network as a spatiotempor...

Energy transition requires a holistic approach, involving land, resources, environmental and economical data and constraints. The core purpose of energy transition, is to migrate power systems towards renewable energy usage (solar, hydro, biomass, wind), in a technical and economical viable manner. In this article we address this challenge as a spa...

The realm of big data has brought new venues for knowledge acquisition, but also major challenges including data interoperability and effective management. The great volume of miscellaneous data renders the generation of new knowledge a complex data analysis process. Presently, big data technologies provide multiple solutions and tools towards the...

La production d’énergie s’est considérablement accrue ces dernières années. À l’échelle mondiale, les besoins en énergie augmentent et entraînent un essor des usages énergétiques des terres agricoles. Car pour respecter les objectifs environnementaux –pour la France la neutralité carbone à l’horizon 2050– cette production d’énergie est appelée à se...

Today, the overall goal of energy transition planning is to seek an optimal strategy for increasing the share of renewable sources in existing power networks, such that the growing power demand is satisfied at manageable short/long term investment. In this paper we address the problem of PV penetration in electricity networks, by considering both 1...

Background Reducing Ambulatory Care Sensitive Admissions (ACSA) not only enhances patients’ quality of life but could also save substantial costs. ACSA are avoidable admissions for chronic conditions that are associated with socio-economic status, health status, utilization and readiness of primary care service as well as environmental factors. Und...

Data crossing seeks the extraction of novel knowledge through correlations and dependencies among heterogeneous data, and is considered a key process in sustainable science to push back the current frontiers of knowledge, especially to address challenges such as the socio-economic impacts of climate change. To tackle such complex challenges, interd...

Solar-based energy is an intermittent power resource whose potential pattern varies in space and time. Planning the penetration of such resource into a regional power network is a strategic problem that requires both to locate and bound candidate parcels subject to multiple geographical restrictions and to determine the subset of these and their si...

It has been identified that reducing potentially avoidable hospitalizations (PAHs) not only enhances patients’ quality of life but also could save substantial costs due to patient treatments. In addition, some recent studies have suggested that increasing the number of nurses in selected geographic areas could lead to the reduction of the rates of...

The paradigm of constraint reasoning aims at modeling and solving combinatorial search problems. The methodology and principle of such models are based on relationships among data and variables, specifically as constraints that must hold for a solution to answer a decision or optimization problem. The relationships can be dependencies of any kind:...

Background: Since prehistory to present times and despite a rough combat against it, malaria remains a concern for human beings. While evolutions of science and technology through times allowed for some infectious diseases eradication in the 20th century, malaria resists. Objectives: This review aims at assessing how Internet and web technologies a...

Uncertain data due to imprecise measurements is commonly specified as bounded intervals in a constraint decision or optimization problem. Dependencies do exist among such data, e.g. upper bound on the sum of uncertain production rates per machine, sum of traffic distribution ratios from a router over several links. For tractability reasons existing...

In this project we address the problem of modelling and solving constraint based problems permeated with data uncertainty, due to imprecise measurements or incomplete knowledge. It is commonly specified as bounded interval parameters in a constraint problem. For tractability reasons, existing approaches assume independence of the data, also called...

Uncertain data due to imprecise measurements is commonly specified as bounded interval parameters in a constraint problem. For tractability reasons, existing approaches assume independence of the parameters. This assumption is safe, but can lead to large solution spaces, and a loss of the problem structure. In this paper we propose to combine the s...

This paper introduces a new constraint domain for reasoning about data with uncertainty. It extends convex modeling with the notion of p-box to gain additional quantifiable information on the data whereabouts. Unlike existing approaches, the p-box envelops an unknown probability instead of approximating its representation. The p-box bounds are unif...

This paper introduces a new constraint domain for reasoning about data with uncertainty. It extends convex modeling with the notion of p-box to gain additional quantifiable information on the data whereabouts. Unlike existing approaches, the p-box envelops an unknown probability instead of approximating its representation. The p-box bounds are unif...

This paper reconsiders the deployment of synchronous op-tical networks (SONET), an optimization problem naturally expressed in terms of set variables. Earlier approaches, using either MIP or CP technologies, focused on symmetry breaking, including the use of SBDS, and the design of effective branching strategies. This paper advocates an orthogonal...

Applied research into renewable energies raises complex challenges of a technological, economical or political nature. In this paper, we address the technoeconomical optimization problem of selecting locations of wind and solar Parks to be built in Egypt, such that the electricity demand is satisfied at minimal costs. Ultimately, our goal is to bui...

University timetabling (UTT) is a complex problem due to its combinatorial nature but also the type of constraints involved. The holy grail of (constraint) programming: "the user states the problem the program solves it" remains a challenge since solution quality is tightly coupled with deriving "effective models", best handled by technology expert...

This paper reconsiders the deployment of synchronous op- tical networks (SONET), an optimization problem naturally expressed in terms of set variables. Earlier approaches, using either MIP or CP technologies, focused on symmetry breaking, including the use of SBDS, and the design of eective branching strategies. This paper advocates an orthogonal a...

Interval coefficients have been introduced in OR and CP to specify uncertain data in order to provide reliable solutions to convex models. The output is generally a solution set, guaranteed to contain all solutions possible under any realization of the data. This set can be too large to be meaningful. Furthermore, each solution has equal uncertaint...

Since their beginning in constraint programming, set solvers have been applied to a wide range of combinatorial search problems, such as bin-packing, set partitioning, circuit and combinatorial design. In this paper we present and evaluate a new means towards improving the practical reasoning power of Finite Set (FS) constraint solvers to better ad...

The length-lex representation has been recently proposed for representing sets in Constraint Satisfaction Problems. The length-lex representation directly captures cardinality infor- mation, provides a total ordering for sets, and allows bound consistency on unary constraints to be enforced in time ˜ O(c), where c is the cardinality of the set. How...

The chapter presents higher level modeling facilities utilizing constraints over structured domains. It addresses the bin-packing problem. The main constrained objects are the different bins, each describing a collection of unordered distinct elements, subject to disjointness constraints among them, weight constraints reflecting on each bin capacit...

Combinatorial design problems arise in many application ar- eas and are naturally modelled in terms of set variables and constraints. Traditionally, thedomain of asetvariable isspec- ified by two sets (R,E) and denotes all sets containing R and disjoint from E. This representation has inherent difficulties in handling cardinality and lexicographic...

In CP literature combinatorial design problems such as sport scheduling, Steiner systems, error-correcting codes and more, are typically solved using Finite Domain (FD) models despite often being more naturally expressed as Finite Set (FS) models. Existing FS solvers have difficulty with such problems as they do not make strong use of the ubiquitou...

Real-world constraint problems abound with uncertainty. Problems with incomplete or erroneous data are often simplified at present to tractable deterministic models, or modified using error correction methods, with the aim of seeking a solution. However, this can lead us to solve the wrong problem because of the approximations made, an outcome of l...

Abstract Constraint problems,with incomplete or erroneous data are often sim- plified to tractable deterministic models, or modified using error correction meth- ods, with the aim of seeking a solution. However, this can lead us to solve the wrong problem because of the approximations,made. Such an outcome,is of little help to a user who expects th...

Constraint Programming (CP) has proved an effective paradigm to model and solve difficult combinatorial satisfaction and optimization problems from disparate domains. Many such problems arising from the commercial world are permeated by data uncertainty. Existing CP approaches that accommodate uncertainty are less suited to uncertainty arising due...

Finite set constraint systems represent a natural choice to model combinatorial configuration problems involving set disjointness, covering or partitioning relations. However, for efficiency reasons, alternative formulations based on Finite Domain or 0-1 integer programming are often preferred even though they require much modelling effort. To offe...

The constraint programming paradigm has proved to have the flexibility and efficiency necessary to treat well-defined largescale optimisation (LSCO) problems. Many real world problems, however, are ill-defined, incomplete, or have uncertain data. Research on ill-defined LSCO problems has centred on modelling the uncertainties by approximating the s...

this document is the metaterm.

: Large Scale Combinatorial Optimization problems (LSCO) appear innumerous types of industrial applications (e.g. production scheduling, routing problems,financial applications). They are NP-complete problems characterized by largesets of data, constraints and variables, and often have an impure structure. Tacklingsuch problems successfully require...

this paper we propose to integrate sets and relations as constrained objects in a CLP framework with finite domains. The domain of discourse for sets is the powerset of the Herbrand universe P(HU). Set constraints and set operators can be simply and efficiently handled. Such an integration of symbolic constrained objects permits Operations Research...

this paper we propose to integrate sets and relations as constrained objects in a CLP framework with finite domains. The domain of discourse for sets is the powerset of the Herbrand universe P(HU). Set constraints and set operators can be simply and efficiently handled. Such an integration of symbolic constrained objects permits Operations Research...

A lot of work has been done up to now in designing Constraint Logic Programming Languages in order to solve combinatorial problems. Built-in computational domains in CLP support simple expression of problems and their efficient solution. Building a new computational domain comprising sets and graphs, this paper presents new symbolic constraints on...

CHIC-2 1 is an Esprit project on Creating Hybrid solutions for Industry and Commerce. CHIC-2 tackles four large scale combinatorial optimisation problems, each being an application supplied by an end-user partner in the project. In this paper we present the "risk management in energy trading" application. The problem comes from the recent privatisa...

this document is the metaterm.

Constraint satisfaction techniques have recently been introduced in logic programming to extend the application domain of logic programming languages. Existing languages, based on these techniques, consider arithmetic constraints applied to variables taking their value in domains of integers. This makes concise and natural modeling as well as an ef...

Constraint problems with incomplete or erroneous data are often sim- plified to tractable deterministic models, or modified using error correction meth- ods, with the aim of seeking a solution. However, this can lead us to solve the wrong problem because of the approximations made. Such an outcome is of little help to a user who expects the right p...

We present a generic framework to reason about constraint problems with incomplete or erroneous data. Such problems are often simplified at present to tractable deterministic models, or modified using error c orrection methods, with the aim of seeking a solution. However, this can lead us to solve the wrong problem because of the approximations mad...

Various extensions of the CSP framework exist to address ill-defined, real-world optimisation problems. One extension, the uncertain CSP (UCSP) tackles the aspect of data errors and incompleteness by ensuring that the problem is faithfully represented with what is known for sure about the data, and by seek- ing reliable solutions that do not approx...

CHIC-21is an Esprit project on Creating Hybrid solutions for Industry andCommerce. CHIC-2 tackles four large scale combinatorial optimisation problems,each being an application supplied by an end-user partner in the project. In thispaper we present the "risk management in energy trading" application. The problemcomes from the recent privatisation o...

Data uncertainties are inherent in the real world. The uncertain CSP (UCSP) is an extension of classical CSP that models incomplete and erroneous data by coefficients in the constraints whose values are unknown but bounded, for instance by an interval. It resolution is a closure, a set of potential solutions. This paper extends the UCSP model to ac...

Data uncertainties are inherent in the real world. The uncertain CSP (UCSP) is an extension of classical CSP that models incomplete and erroneous data by coefficients in the constraints whose values are unknown but bounded, for instance by an interval. Formally, the UCSP is a tractable restriction of the quantified CSP. The resolution of a UCSP, a...

The concept of cdf-intervals introduced in previous work is revisited with a new domain specification, inference mechanism and im-plementation. A cdf-interval extends traditional convex intervals over reals with a degree of knowledge attached to the data. Instead of ap-proximating the unknown data distribution with the nearest uniform distribution,...