A Local Search Modeling for Constrained Optimum Paths Problems (Extended Abstract)

Constrained Optimum Path (COP) problems appear in many real-life applications, especially on communication networks. Some of these problems have been considered and solved by specific techniques which are usually difficult to extend. In this paper, we introduce a novel local search modeling for solving some COPs by local search. The modeling features the compositionality, modularity, reuse and strengthens the benefits of Constrained-Based Local Search. We also apply the modeling to the edge-disjoint paths problem (EDP). We show that side constraints can easily be added in the model. Computational results show the significance of the approach

A Constraint-directed Local Search Approach to Nurse Rostering Problems

In this paper, we investigate the hybridization of constraint programming and local search techniques within a large neighbourhood search scheme for solving highly constrained nurse rostering problems. As identified by the research, a crucial part of the large neighbourhood search is the selection of the fragment (neighbourhood, i.e. the set of variables), to be relaxed and re-optimized iteratively. The success of the large neighbourhood search depends on the adequacy of this identified neighbourhood with regard to the problematic part of the solution assignment and the choice of the neighbourhood size. We investigate three strategies to choose the fragment of different sizes within the large neighbourhood search scheme. The first two strategies are tailored concerning the problem properties. The third strategy is more general, using the information of the cost from the soft constraint violations and their propagation as the indicator to choose the variables added into the fragment. The three strategies are analyzed and compared upon a benchmark nurse rostering problem. Promising results demonstrate the possibility of future work in the hybrid approach

Packages (Re)Dice _ pour les computer experiments

http://r2014-mtp.sciencesconf.org/conference/r2014-mtp/pages/roustant.pdfNational audienceLa thématique des computer experiments ([1], [2]) concerne l'analyse ou la planification d'expériences dont la réponse est obtenue à l'aide d'un code de calcul coûteux. Typiquement, l'évaluation d'une réponse demande plusieurs heures, voire plusieurs jours de calcul. De telles situations se rencontrent dans des secteurs variés et intéressent de nombreux industriels, comme la simulation d'écoulement en ingénierie réservoir, ou la simulation de crash dans le secteur automobile. Les problèmes à résoudre concernent l'interpolation ou l'approximation de fonctions, l'optimisation. Ils sont liés à des problèmes plus classiques de statistique comme ceux de la planification d'expériences ou de la statistique spatiale, avec des spécificités dues à la nature des expériences (souvent déterministes) et à la dimension du problème (souvent supérieure à 3). On retrouve en particulier en computer experiments les techniques basées sur les processus gaussiens comme le krigeage. Le système R présente de nombreux atouts pour les computer experiments. Nous décrivons le rôle important joué par R dans les consortiums DICE [3] et ReDICE [4] rassemblant des industriels et des chercheurs académiques de cultures logicielles diverses. Plusieurs packages spécifiques ont été développés ou initialisés dans le cadre de ces consortiums et sont accessible sur le CRAN : DiceDesign, DiceEval, DiceKriging, DiceOptim, DiceView. D'autres packages sont en cours de développement. Nous présentons quelques fonctionnalités de ces packages, en lien avec leur contexte particulier de développement

On Improving Local Search for Unsatisfiability

Stochastic local search (SLS) has been an active field of research in the last few years, with new techniques and procedures being developed at an astonishing rate. SLS has been traditionally associated with satisfiability solving, that is, finding a solution for a given problem instance, as its intrinsic nature does not address unsatisfiable problems. Unsatisfiable instances were therefore commonly solved using backtrack search solvers. For this reason, in the late 90s Selman, Kautz and McAllester proposed a challenge to use local search instead to prove unsatisfiability. More recently, two SLS solvers - Ranger and Gunsat - have been developed, which are able to prove unsatisfiability albeit being SLS solvers. In this paper, we first compare Ranger with Gunsat and then propose to improve Ranger performance using some of Gunsat's techniques, namely unit propagation look-ahead and extended resolution

Efficient balanced sampling: The cube method

A balanced sampling design is defined by the property that the Horvitz-Thompson estimators of the population totals of a set of auxiliary variables equal the known totals of these variables. Therefore the variances of estimators of totals of all the variables of interest are reduced, depending on the correlations of these variables with the controlled variables. In this paper, we develop a general method, called the cube method, for selecting approximately balanced samples with equal or unequal inclusion probabilities and any number of auxiliary variable

Dynamic Demand-Capacity Balancing for Air Traffic Management Using Constraint-Based Local Search: First Results

Using constraint-based local search, we effectively model and efficiently solve the problem of balancing the traffic demands on portions of the European airspace while ensuring that their capacity constraints are satisfied. The traffic demand of a portion of airspace is the hourly number of flights planned to enter it, and its capacity is the upper bound on this number under which air-traffic controllers can work. Currently, the only form of demand-capacity balancing we allow is ground holding, that is the changing of the take-off times of not yet airborne flights. Experiments with projected European flight plans of the year 2030 show that already this first form of demand-capacity balancing is feasible without incurring too much total delay and that it can lead to a significantly better demand-capacity balance

A Hybrid Genetic Algorithm for the Traveling Salesman Problem with Drone

This paper addresses the Traveling Salesman Problem with Drone (TSP-D), in which a truck and drone are used to deliver parcels to customers. The objective of this problem is to either minimize the total operational cost (min-cost TSP-D) or minimize the completion time for the truck and drone (min-time TSP-D). This problem has gained a lot of attention in the last few years since it is matched with the recent trends in a new delivery method among logistics companies. To solve the TSP-D, we propose a hybrid genetic search with dynamic population management and adaptive diversity control based on a split algorithm, problem-tailored crossover and local search operators, a new restore method to advance the convergence and an adaptive penalization mechanism to dynamically balance the search between feasible/infeasible solutions. The computational results show that the proposed algorithm outperforms existing methods in terms of solution quality and improves best known solutions found in the literature. Moreover, various analyses on the impacts of crossover choice and heuristic components have been conducted to analysis further their sensitivity to the performance of our method.Comment: Technical Report. 34 pages, 5 figure