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Academic literature on the topic 'Problèmes de chemin le plus court en ligne'
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Journal articles on the topic "Problèmes de chemin le plus court en ligne"
McCormick, Peter. "Heidegger sur le chemin du langage." Articles 1, no. 2 (January 24, 2007): 15–36. http://dx.doi.org/10.7202/203011ar.
Full textHaouari, M., and P. Dejax. "Plus court chemin avec dépendance horaire : résolution et application aux problèmes de tournées." RAIRO - Operations Research 31, no. 2 (1997): 117–31. http://dx.doi.org/10.1051/ro/1997310201171.
Full textHenley, John S. "On the Lack of Trade Union Power in Kenya." Relations industrielles 31, no. 4 (April 12, 2005): 655–67. http://dx.doi.org/10.7202/028748ar.
Full textCockx, Bart, Koen Declercq, Muriel Dejemeppe, and Bruno Van der Linden. "Focus 24 - avril 2020." Regards économiques, July 16, 2020. http://dx.doi.org/10.14428/regardseco2020.04.02.01.
Full textDissertations / Theses on the topic "Problèmes de chemin le plus court en ligne"
Vu, Dong Quan. "Models and solutions of strategic resource allocation problems : approximate equilibrium and online learning in Blotto games." Electronic Thesis or Diss., Sorbonne université, 2020. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2020SORUS120.pdf.
Full textResource allocation problems are broadly defined as situations involving decisions on distributing a limited budget of resources in order to optimize an objective. In particular, many of them involve interactions between competitive decision-makers which can be well captured by game-theoretic models. In this thesis, we choose to investigate resource allocation games. We primarily focus on the Colonel Blotto game (CB game). In the CB game, two competitive players, each having a fixed budget of resources, simultaneously distribute their resources toward n battlefields. Each player evaluates each battlefield with a certain value. In each battlefield, the player who has the higher allocation wins and gains the corresponding value while the other loses and gains zero. Each player's payoff is her aggregate gains from all the battlefields. First, we model several prominent variants of the CB game and their extensions as one-shot complete-information games and analyze players' strategic behaviors. Our first main contribution is a class of approximate (Nash) equilibria in these games for which we prove that the approximation error can be well-controlled. Second, we model resource allocation games with combinatorial structures as online learning problems to study situations involving sequential plays and incomplete information. We make a connection between these games and online shortest path problems (OSP). Our second main contribution is a set of novel regret-minimization algorithms for generic instances of OSP under several restricted feedback settings that provide significant improvements in regret guarantees and running time in comparison with existing solutions
Parmentier, Axel. "Quelques Algorithmes pour des problèmes de plus court chemin et d'opérations aériennes." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1060/document.
Full textThis thesis develops algorithms for resource constrained shortest path problems, and uses them to solve the pricing subproblems of column generation approaches to some airline operations problems.Resource constrained shortest path problems are usually solved using a smart enumeration of the non-dominated paths. Recent improvements of these enumeration algorithms rely on the use of bounds on path resources to discard partial solutions. The quality of the bounds determines the performance of the algorithm. Our main contribution to the topic is to introduce a standard procedure to generate bounds on paths resources in a general setting which covers most resource constrained shortest path problems, among which stochastic versions. In that purpose, we introduce a generalization of the resource constrained shortest path problem where the resources are taken in a lattice ordered monoid. The resource of a path is the monoid sum of the resources of its arcs. The problem consists in finding a path whose resource minimizes a non-decreasing cost function of the path resource among the paths that satisfy a given constraint. Enumeration algorithms are generalized to this framework. We use lattice theory to provide polynomial procedures to find good quality bounds. The efficiency of the approach is proved through an extensive numerical study on deterministic and stochastic path problems. Interestingly, the bounding procedures can be seen as generalizations to lattice ordered monoids of some algebraic path problem algorithms which initially work with resources in a semiring.Given a set of flight legs operated by an airline, the aircraft routing and the crew pairing problem build respectively the sequences of flight legs operated by airplanes and crews at minimum cost. As some sequences of flight legs can be operated by crews only if they stay in the same aircraft, the two problems are linked. The current practice in the industry is to solve first the aircraft routing, and then the crew pairing problem, leading to a non-optimal solution. During the last decade, solution schemes for the integrated problem have been developed. We propose a solution scheme for the integrated problem based on two new ingredients: a compact integer program approach to the aircraft routing problem, and a new algorithm for the pricing subproblem of the usual column generation approach to the crew pairing problem, which is based on our resource constrained shortest path framework. We then generalize the algorithm to take into account delay propagation through probabilistic constraints. The algorithms enable to solve to near optimality Air France industrial instances
Chabrier, Alain. "Génération de colonnes et de coupes utilisant des sous-problèmes de plus court chemin." Angers, 2003. http://www.theses.fr/2003ANGE0029.
Full textIn recent years, column generation methods have been the subject of many publications about solving a greater number of combinatorial optimisation pro-blems. They correspond to a particular use of the revised Simplex method on a decomposed and restricted problem. An auxiliary problem generates new va-riables not present initially. In this thesis, we focus our interest on the cases where the auxiliary problcm is a constrained shortest path problem in a graph. Various improvcments have been proposed in thc literature, but they arc all limitcd to a particular problem. This thesis proposes to facilitate thc reuse of those improvcments in different problems. For this, we propose a generic formalism to model such problems and a description of thc scarch for solutions using goals. We then present new and different practical improvements applicable te varions problems. More precisely, the contributions are : an efficient algorithm for elcmentary shortcst path in the subproblem, a combination of expert heuristics and Constraint Programming in the subproblem, search strategies for the subproblem, a Constraint Programming global constraint for shortest path subproblem, cutting planes applied to the path-based master problem, heuristics and search strategies for the master problem. Those improvements are validated on three different real applications : ve-hicle routing, crew scheduling, and network design. For each of those applications, we produce several experimental results sho-wing how some combinations of the proposed contributions help to improve the ultimate solutions. Finally, a column and cut generation framework has been implemented that eases the development of such applications and that includes most of our pro-posal
Gueguen, Cyrille. "Méthodes de résolution exacte pour les problèmes de tournées de véhicules." Châtenay-Malabry, Ecole centrale de Paris, 1999. http://www.theses.fr/1999ECAP0664.
Full textBoukebab, Kaouthar. "Etude et résolution de problèmes d'ordonnancement d'opérations d'évacuation." Thesis, Tours, 2015. http://www.theses.fr/2015TOUR4023/document.
Full textThe work presented in this thesis, which is a part of the Franco-German project DSS_Evac_Logistic, aims at proposing methods to calculate macroscopic evacuation plans for mid-size towns after a tremendous disaster. Two evacuation problems have been tackled in this thesis : the bus evacuation problem and bus-and-vehicle evacuation problem. The bus evacuation problem aims at calculating an evacuation plan to relocate evacuees outside the endangered area. In this thesis, we consider three versions of the bus evacuation problem. The first one is a monocriterion problem, where the objective is to minimize the maximum evacuation time. In order to guarantee the safety of evacuees, we have considered a bicriteria problem, which is a generalization of the monocriterion version, in which we take into consideration the risk exposure of the evacuees. Consequently, the bicriteria problem is solved by minimizing the total evacuation time and the risk. The third version is a bicriteria robust version because most of the planning data is subject to uncertainty. The goal is to minimize both the evacuation time and the vulnerability of the schedule that is subject to different evacuation circumstances. To solve all the versions of the bus evacuation problem, we have developed exact solutions based on mathematical formulation to address small instances and heuristic solutions to deal with larger instances
Sérée, Bastien. "Problèmes d'optimisation des les graphes paramétrés." Electronic Thesis or Diss., Ecole centrale de Nantes, 2022. http://www.theses.fr/2022ECDN0066.
Full textWe are considering weighted oriented graphs with parametrized energy. Firstly we propose an algorithm that, given a graph and one of its vertices, returns trees, every tree representing shortest-paths from the source to every other vertex for a particular zone of the parameter space. Moreover, union of these zones is a covering of the parameter space. Then we consider reachability in graphs with multi-dimensional energy, with stricter constraints that enforce the energy to stay between bounds. We prove decidabilty and complexity of this problem regardless of the dimension and the number of parameters when parameters take integer values. We alsoprove the undecidability of this problem when there is at least one parameter and the dimension is at least two. Finally we study paritygames on parametrized graphs with one and two players whose objective is the conjunction of a qualitative condition on the parity andquantitative one : energy must stay positive. We show the decidability and prove bounds on the complexity of the problem of searchinga winning strategy in both cases with one and two players
Cheng, Jianqiang. "Stochastic Combinatorial Optimization." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112261.
Full textIn this thesis, we studied three types of stochastic problems: chance constrained problems, distributionally robust problems as well as the simple recourse problems. For the stochastic programming problems, there are two main difficulties. One is that feasible sets of stochastic problems is not convex in general. The other main challenge arises from the need to calculate conditional expectation or probability both of which are involving multi-dimensional integrations. Due to the two major difficulties, for all three studied problems, we solved them with approximation approaches.We first study two types of chance constrained problems: linear program with joint chance constraints problem (LPPC) as well as maximum probability problem (MPP). For both problems, we assume that the random matrix is normally distributed and its vector rows are independent. We first dealt with LPPC which is generally not convex. We approximate it with two second-order cone programming (SOCP) problems. Furthermore under mild conditions, the optimal values of the two SOCP problems are a lower and upper bounds of the original problem respectively. For the second problem, we studied a variant of stochastic resource constrained shortest path problem (called SRCSP for short), which is to maximize probability of resource constraints. To solve the problem, we proposed to use a branch-and-bound framework to come up with the optimal solution. As its corresponding linear relaxation is generally not convex, we give a convex approximation. Finally, numerical tests on the random instances were conducted for both problems. With respect to LPPC, the numerical results showed that the approach we proposed outperforms Bonferroni and Jagannathan approximations. While for the MPP, the numerical results on generated instances substantiated that the convex approximation outperforms the individual approximation method.Then we study a distributionally robust stochastic quadratic knapsack problems, where we only know part of information about the random variables, such as its first and second moments. We proved that the single knapsack problem (SKP) is a semedefinite problem (SDP) after applying the SDP relaxation scheme to the binary constraints. Despite the fact that it is not the case for the multidimensional knapsack problem (MKP), two good approximations of the relaxed version of the problem are provided which obtain upper and lower bounds that appear numerically close to each other for a range of problem instances. Our numerical experiments also indicated that our proposed lower bounding approximation outperforms the approximations that are based on Bonferroni's inequality and the work by Zymler et al.. Besides, an extensive set of experiments were conducted to illustrate how the conservativeness of the robust solutions does pay off in terms of ensuring the chance constraint is satisfied (or nearly satisfied) under a wide range of distribution fluctuations. Moreover, our approach can be applied to a large number of stochastic optimization problems with binary variables.Finally, a stochastic version of the shortest path problem is studied. We proved that in some cases the stochastic shortest path problem can be greatly simplified by reformulating it as the classic shortest path problem, which can be solved in polynomial time. To solve the general problem, we proposed to use a branch-and-bound framework to search the set of feasible paths. Lower bounds are obtained by solving the corresponding linear relaxation which in turn is done using a Stochastic Projected Gradient algorithm involving an active set method. Meanwhile, numerical examples were conducted to illustrate the effectiveness of the obtained algorithm. Concerning the resolution of the continuous relaxation, our Stochastic Projected Gradient algorithm clearly outperforms Matlab optimization toolbox on large graphs
Azi, Nabila. "Méthodes exactes et heuristiques pour le problème de tournées de véhicules avec fenêtres de temps et réutilisation de véhicules." Thèse, 2010. http://hdl.handle.net/1866/4876.
Full textThis thesis studies vehicle routing problems with time windows, where a gain is associated with each customer and where the objective is to maximize the total gain collected minus the routing costs. Furthermore. the same vehicle might be assigned to different routes during the planning horizon. This problem has received little attention in the literature in spite of its importance in practice. For example, in the home delivery of perishable goods (like food), routes of short duration must be combined to form complete workdays. We believe that this type of problem will become increasingly important in the future with the advent of electronic services, like e-groceries, where customers can order goods through the Internet and get these goods delivered at home. In the first chapter of this thesis, we present a review of vehicle routing problems with gains, as well as vehicle routing problems with multiple use of vehicles. We discuss the general classes of problem-solving approaches for these problems, namely, exact methods, heuristics and metaheuristics. We also introduce dynamic vehicle routing problems, where new information is revealed as the routes are executed. In the second chapter, we describe an exact algorithm for a vehicle routing problem with time windows and multiple use of vehicles, where the first objective is to maximize the number of served customers. To this end, the problem is modeled as a vehicle routing problem with gains. The exact algorithm is based on column generation, coupled with an elementary shortest path algorithm with resource constraints. To solve realistic instances in reasonable computation times, a heuristic approach is required. The third chapter proposes an adaptative large neighborhood search where the various hierarchical levels of the problem are exploited (i.e., complete vehicle workdays, routes within workdays and customers within routes). The fourth chapter deals with the dynamic case. In this chapter, a strategy for accepting or rejecting new customer requests is proposed. This strategy is based on the generation of multiple scenarios for different realizations of the requests in the future. An opportunity cost for serving a new request is then computed, based on an evaluation of the scenarios with and without the new request. Finally, the last chapter summarizes the contributions of this thesis and proposes future research avenues.