Enriching Solutions to Combinatorial Problems via Solution Engineering

International audienceExisting approaches to identify multiple solutions to combinatorial problems in practice are at best limited in their ability to simultaneously incorporate both diversity among generated solutions, as well as problem-specific desires that may only be discovered or articulated by the user after further analysis of solver output. We propose a general framework for problems of a combinatorial nature that can generate a set of of multiple (near-)optimal, diverse solutions, that are further infused with desirable features. We call our approach solution engineering. A key novelty is that desirable solution properties need not be explicitly modeled in advance. We customize the framework to both the mathematical programming and constraint programming technologies, and subsequently demonstrate its prac-ticality by implementing and then conducting computational experiments on existing test instances from the literature. Our computational results confirm the very real possibility of generating sets of solutions infused with features that might otherwise remain undiscovered

Leprechauns on the chessboard

We introduce in this paper leprechauns, fairy chess pieces that can move either like the standard queen, or to any tile within k king moves. We then study the problem of placing n leprechauns on an n×n chessboard. When k=1, this is equivalent to the standard n-Queens Problem. We solve the problem for k=2, as well as for k>2 and n≤(k+1)2, giving in the process an upper bound on the lowest non-trivial value of n such that the (k,n)-Leprechauns Problem is satisfiable. Solving this problem for all k would be equivalent to solving the diverse n-Queens Problem, the variant of the n-Queens Problem where the distance between the two closest queens is maximized. While diversity has been a popular topic in other constraint problems, this is not the case for the n-Queens Problem, making our results the first major ones in the domain

LOGIC AND CONSTRAINT PROGRAMMING FOR COMPUTATIONAL SUSTAINABILITY

Computational Sustainability is an interdisciplinary field that aims to develop computational and mathematical models and methods for decision making concerning the management and allocation of resources in order to help solve environmental problems. This thesis deals with a broad spectrum of such problems (energy efficiency, water management, limiting greenhouse gas emissions and fuel consumption) giving a contribution towards their solution by means of Logic Programming (LP) and Constraint Programming (CP), declarative paradigms from Artificial Intelligence of proven solidity. The problems described in this thesis were proposed by experts of the respective domains and tested on the real data instances they provided. The results are encouraging and show the aptness of the chosen methodologies and approaches. The overall aim of this work is twofold: both to address real world problems in order to achieve practical results and to get, from the application of LP and CP technologies to complex scenarios, feedback and directions useful for their improvement

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

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

Soft constraints of difference and equality

In many combinatorial problems one may need to model the diversity or similarity of assignments in a solution. For example, one may wish to maximise or minimise the number of distinct values in a solution. To formulate problems of this type, we can use soft variants of the well known AllDifferent and AllEqual constraints. We present a taxonomy of six soft global constraints, generated by combining the two latter ones and the two standard cost functions, which are either maximised or minimised. We characterise the complexity of achieving arc and bounds consistency on these constraints, resolving those cases for which NP-hardness was neither proven nor disproven. In particular, we explore in depth the constraint ensuring that at least k pairs of variables have a common value. We show that achieving arc consistency is NP-hard, however achieving bounds consistency can be done in polynomial time through dynamic programming. Moreover, we show that the maximum number of pairs of equal variables can be approximated by a factor 1/2 with a linear time greedy algorithm. Finally, we provide a fixed parameter tractable algorithm with respect to the number of values appearing in more than two distinct domains. Interestingly, this taxonomy shows that enforcing equality is harder than enforcing difference