Many combinatorial optimization problems can be formulated asthe minimization of a 0–1 quadratic function subject to linear constraints. Inthis paper, we are interested in the exact solution of this problem through atwo-phase general scheme. The first phase consists in reformulating theinitial problem either into a compact mixed integer linear program or into a0–1 quadratic convex program. The second phase simply consists insubmitting the reformulated problem to a standard solver. The efficiency ofthis scheme strongly depends on the quality of the reformulation obtained inphase 1. We show that a good compact linear reformulation can be obtained bysolving a continuous linear relaxation of the initial problem. We also showthat a good quadratic convex reformulation can be obtained by solving asemidefinite relaxation. In both cases, the obtained reformulation profitsfrom the quality of the underlying relaxation. Hence, the proposed scheme getsaround, in a sense, the difficulty to incorporate these costly relaxations ina branch-and-bound algorithm.

In cutting stock problems, after an optimal (minimal stock usage)cutting plan has been devised, one might want to further reduce theoperational costs by minimizing the number of setups. A setupoperation occurs each time a different cutting pattern begins to beproduced. The related optimization problem is known as the PatternMinimization Problem, and it is particularly hard to solve exactly.In this paper, we present different techniques to strengthen aformulation proposed in the literature. Dual feasible functions areused for the first time to derive valid inequalities from differentconstraints of the model, and from linear combinations of constraints. A new arcflow formulation is also proposed. This formulation is used todefine the branching scheme of our branch-and-price-and-cutalgorithm, and it allows the generation of even stronger cuts bycombining the branching constraints with other constraints of themodel. The computational experiments conducted on instances from theliterature show that our algorithm finds optimalinteger solutions faster than other approaches. A set of computationalresults on random instances is also reported.

Linear programming techniques can be used in constructing schedules but theirapplication is not trivial. This in particular holds true if a trade-offhas to be made between computation time and solution quality. However,it turns out that – whenhandled with care – mixed integer linear programs may provide effectivetools. This is demonstrated in the successful approach to the benchmarkconstructed for the 2007 ROADEF computation challenge on scheduling problemsfurnished by France Telecom.

We present, in this article, a hybrid approach forsolvingthe 0–1 multidimensional knapsack problem (MKP). This approach combineslinearprogramming and Tabu search.The resulting algorithm improves on the best result on many well-knownhard benchmarks.

For solving linear complementarity problems LCP more attention has recently been paid on a class of iterative methods called the matrix-splitting. But up to now, no paper has discussed the effect of preconditioning technique for matrix-splitting methods in LCP. So, this paper is planning to fill in this gap and we use a class of preconditioners with generalized Accelerated Overrelaxation (GAOR) methods and analyze the convergence of these methods for LCP. Furthermore, Comparison between our methods and other non-preconditioned methods for the studied problem shows a remarkable agreement and reveals that our models are superior in point of view of convergence rate and computing efficiency. Besides, by choosing the appropriate parameters of these methods, we derive same results as the other iterative methods such as AOR, JOR, SOR etc. Finally the method is tested by some numerical experiments.

Soit F une famille de critères conçue pour asseoir un modèle de préférences global sur un ensemble A d'actions potentielles (ou alternatives). On se place ici dans une perspective d'aide à la décision et dans l'hypothèse où des dépendances (encore appelées interactions) sont susceptibles d'exister entre certains des critères de F. On commence (cf. Sect. 2.1) par préciser ce que signifie l'affirmation "il existe des dépendances entre certains des critères de F" (Déf. 1). On s'intéresse ensuite à deux formes classiques de dépendances (cf. Sect. 2.2). Dans la Section 3, on cherche à apporter des éléments de réponse à la question : comment mettre en évidence les dépendances s'il en existe et comment les rendre intelligibles pour les parties prenantes au processus de décision ? On définit trois types de situations particulièrement dignes d'intérêt car aisément intelligibles et faciles à cerner. Dans la section suivante, on examine les possibilités qu'offrent un certain nombre de modèles d'agrégation pour prendre en compte explicitement des dépendances. On met notamment en évidence (cf. Sect. 4.2) les possibilités et limites de l'intégrale de Choquet. Tout au long de l'article, des exemples sont introduits pour illustrer les propos.


Retrial queueing systems are characterized by the requirement that customersfinding the service area busy must join the retrial group and reapply forservice at random intervals. This paper deals with the M/G/1 retrial queuesubjected to breakdowns. We use its stochastic decomposition property toapproximate the model performance in the case of general retrial times.

In this study, we consider a scheduling environment with m(m ≥ 1) parallel machines.The set of jobs to schedule is divided into K disjoint subsets. Each subset of jobs isassociated with one agent. The K agents compete to perform their jobs on commonresources. The objective is to find a schedule that minimizes a global objective functionf 0, while maintaining the regularobjective function of each agent, f k, at a level nogreater than a fixed value, εk (fk ∈ {fkmax, ∑fk}, k = 0, ..., K). This problem is a multi-agent schedulingproblem with a global objective function. In this study, we consider the casewith preemption and the case without preemption. If preemption is allowed, we propose apolynomial time algorithm based on a network flow approach for the unrelated parallelmachine case. If preemption is not allowed, we propose some general complexity results anddevelop dynamic programming algorithms.

Single server queues with repeated attempts are useful inthe modeling of computer and telecommunication systems. In addition, weconsider in this paper the possibility of disasters. When a disasteroccurs, all the customers present in the system are destroyedimmediately. Using a regenerative approach, we derive a numericallystable recursion scheme for the state probabilities. This model can beemployed to analyze the behaviour of a buffer in computers with virusinfections.

The problem is to modify the capacities of the arcs from a network so that a given feasible flow becomes a maximum flow and the maximum change of the capacities on arcs is minimum. A very fast O(m⋅log(n)) time complexity algorithm for solving this problem is presented, where m is the number of arcs and n is the number of nodes of the network. The case when both, lower and upper bounds of the flow can be modified so that the given feasible flow becomes a maximum flow is also discussed. The algorithm proposed can be adapted to solve this problem, too. The inverse minimum flow problem considering l ∞ norm is also studied.

This paper studies the machine repairproblem consisting of M operating machines with S sparemachines, and R servers (repairmen) who leave for a vacation ofrandom length when there are no failed machines queuing up forrepair in the repair facility. At the end of the vacation theservers return to the repair facility and operate one of threevacation policies: single vacation, multiple vacation, and hybridsingle/multiple vacation. The Markov process and thematrix-geometric approach are used to develop the steady-stateprobabilities of the number of failed machines in the system aswell as the performance measures. A cost model is developed toobtain the optimal values of the number of spares and the numberof servers while maintaining a minimum specified level of systemavailability. Some numerical experiments are performed and someconclusions are drawn.

Simple games cover voting systems in which a single alternative, such as a bill or anamendment, is pitted against the status quo. A simple game or a yes-no voting system is aset of rules that specifies exactly which collections of “yea” votes yield passage of theissue at hand. Each of these collections of “yea” voters forms a winning coalition. We areinterested in performing a complexity analysis on problems defined on such families ofgames. This analysis as usual depends on the game representation used as input. Weconsider four natural explicit representations: winning, losing, minimal winning, andmaximal losing. We first analyze the complexity of testing whether a game is simple andtesting whether a game is weighted. We show that, for the four types of representations,both problems can be solved in polynomial time. Finally, we provide results on thecomplexity of testing whether a simple game or a weighted game is of a special type. Weanalyze strongness, properness, weightedness, homogeneousness, decisiveness andmajorityness, which are desirable properties to be fulfilled for a simple game. Finally,we consider the possibility of representing a game in a more succinct and natural way andshow that the corresponding recognition problem is hard.

In this paper, which is an extension of [4],we first show the existence of solutions to a class of Min Sup problems with linked constraints, which satisfy a certain property. Then, we apply our result to a class of weak nonlinear bilevelproblems. Furthermore, for such a class of bilevel problems, wegive a relationship with appropriate d.c. problems concerning theexistence of solutions.

This paper presents a logarithmic barrier method for solving a semi-definite linear program. The descent direction is the classical Newton direction. We propose alternative ways to determine the step-size along the direction which are more efficient than classical line-searches.

We present in this paper a new multiobjective memetic algorithm scheme called MEMOX. In current multiobjective memetic algorithms, the parents used for recombination are randomly selected. We improve this approach by using a dynamic hypergrid which allows to select a parent located in a region of minimal density. The second parent selected is a solution close, in the objective space, to the first parent. A local search is then applied to the offspring. We experiment this scheme with a new multiobjective tabu search called PRTS, which leads to the memetic algorithm MEMOTS. We show on the multidimensional multiobjective knapsack problem that if the number of objectives increase, it is preferable to have a diversified research rather using an advanced local search. We compare the memetic algorithm MEMOTS to other multiobjective memetic algorithms by using different quality indicators and show that the performances of the method are very interesting.

In telecommunications network design, one of the most frequentproblems is to adjust the capacity on the links of the network in order to satisfy a set of requirements. In the past, theserequirements were demands based on historical data and/or demographic predictions. Nowadays, because of new technologydevelopment and customer movement due to competitiveness, thedemands present considerable variability. Thus, network robustness w.r.t demand uncertainty is now regarded as a majorconsideration. In this work, we propose a min-max-min formulation and a methodology to cope with this uncertainty. We model the uncertainty as the convex hull of certain scenarios and show thatcutting plane methods can be applied to solve the underlying problems. We will compare Kelley, Elzinga-Moore and bundle methods.

This paper considers an M/M/R/N queue with heterogeneousservers in which customers balk (do not enter) with a constantprobability (1 - b). We develop the maximum likelihoodestimates of the parameters for the M/M/R/N queue with balking andheterogeneous servers. This is a generalization of the M/M/2queue with heterogeneous servers (without balking), and theM/M/2/N queue with balking and heterogeneous servers in theliterature. We also develop the confidence interval formula forthe parameter ρ, the probability of empty system P 0, andthe expected number of customers in the system E[N], of anM/M/R/N queue with balking and heterogeneous servers. The effectsof varying b, N, and R on the confidence intervals of P 0and E[N] are also investigated.

This survey presents major results and issues related to the study of NPO problems in dynamic environments, that is, in settings where instances are allowed to undergo some modifications over time. In particular, the survey focuses on two complementary frameworks. The first one is the reoptimization framework, where an instance I that is already solved undergoes some local perturbation. The goal is then to make use of the information provided by the initial solution to compute a new solution. The second framework is probabilistic optimization, where the instance to optimize is not fully known at the time when a solution is to be proposed, but results from a determined Bernoulli process. Then, the goal is to compute a solution with optimal expected value.

In the paper we generalize sufficient and necessary optimality conditions obtained by Ginchev, Guerraggio, Rocca, and by authors with the help of the notion of ℓ-stability for vector functions.