272 research outputs found
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
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