This paper is devoted to the exact resolution of a strongly NP-hard resource-constrained scheduling problem, the Process Move Programming problem, which arises in relation to the operability of certain high-availability real-time distributed systems. Based on the study of the polytope defined as the convex hull of the incidence vectors of the admissible process move programs, we present a branch-and-cut algorithm along with extensive computational results demonstrating its practical relevance, in terms of both exact and approximate resolution when the instance size increases.

Given a weighted undirected graph G = (V,E),a tree (respectively tour) cover of an edge-weighted graph is a set of edges which forms a tree (resp. closed walk) and covers every other edge in the graph. The tree (resp. tour) cover problem is of finding a minimum weight tree (resp. tour) cover of G. Arkin, Halldórsson and Hassin (1993) give approximation algorithms with factors respectively 3.5 and 5.5. Later Könemann, Konjevod, Parekh, and Sinha (2003) study the linear programming relaxations and improve both factors to 3. We describe in the first part of the paper a 2-approximation algorithm for the metric case of tree cover.In the second part, we will consider a generalized version of tree (resp. tour) covers problem which is to find a minimum tree (resp. tours) which covers a subset D ⊆ E of G.We show that the algorithms of Könemann et al.can be adapted for the generalized tree and tours covers problem with the same factors.

A co-biclique of a simple undirected graph G = (V,E) is the edge-set of two disjoint complete subgraphs of G.(A co-biclique is the complement of a biclique.)A subset F ⊆ E is an independent of G if there is a co-biclique B such that F ⊆ B, otherwise F is a dependent of G. This paper describes the minimal dependents of G. (A minimal dependent is a dependent C such that any proper subset of C is an independent.) It is showed that a minimum-cost dependent set of G can be determined in polynomial time for any nonnegative cost vector $x\in \mathbb Q_+^E$ . Based on this, we obtain a branch-and-cut algorithm for the maximum co-biclique problem which is, given a weight vector $w\in \mathbb Q_+^E$ , to find a co-biclique B of G maximizing w(B) = ∑e∈B we .

The minimum cost multiple-source unsplittable flow problem isstudied in this paper. A simple necessary condition to get asolution is proposed. It deals with capacities and demands and canbe seen as a generalization of the well-known semi-metriccondition for continuous multicommdity flows. A cutting planealgorithm is derived using a superadditive approach. Theinequalities considered here are valid for single knapsackconstraints. They are based on nondecreasing superadditivefunctions and can be used to strengthen the relaxation of anyinteger program with knapsack constraints. Some numericalexperiments confirm the efficiency of the inequalities introducedin the paper.

We discuss the use of operations research methods for computer-aided design of mechanical transmission systems. We consider how to choose simultaneously transmission ratios and basic design parameters of transmission elements (diameters, widths, modules and tooth number for gears, diameters of shafts). The objectives, by the order of importance, are: to minimize the deviation of the obtained speeds from desired; to maximize the transmission life; to minimize the total mass. To solve this problem, we propose a multi-level decomposition scheme in combination with methods of quadratic and dynamic programming. Some industrial cases are solved. For these cases, the developed software tool improves the design decisions by decreasing total metal consumption of the transmission as much as 7–10% and considerable simplifies the work of the designer.

This paper addresses a one-machine scheduling problem in which the efficiency of the machine is not constant, that is the duration of a task is longer in badly efficient time periods. Each task has an irregular completion cost. Under the assumption that the efficiency constraints are time-periodic, we show that the special case where the sequence is fixed can be solved in polynomial time. The general case is NP-complete so that we propose a two-phase heuristic to find good solutions. Our approach is tested on problems with earliness-tardiness costs.

L'opérateur de moyenne pondérée est très souvent utilisé pour définir une valeur v(a) à des entités a à partir de performances xj(a), j=1,...,n. Cet opérateur fait intervenir des poids spécifiquesw j comme multiplicateurs de la performance relative à la je composante. Ceci induit des possibilités de compensation des mauvaises performances par les meilleures qui peuvent être jugées inacceptables dans certains contextes concrets. En vue d'atténuer ces possibilités de compensation, on peut faire intervenir une seconde pondération à l'aide de poids de rangq r qui affectent le rôle que joue, dans la définition de v(a), la performance xj(a) en fonction du rang r qu'elle occupe dans un rangement des meilleures valeurs aux moins bonnes.Je commencerai par décrire trois exemples issus de contextes réels dans lesquels cette double pondération est nécessaire. Ensuite, je présenterai successivement un premier opérateur que j'ai introduit en 1996 sous le nom de moyenne ordonnée doublement pondérée (MO2P) et un second, proposé en 1997 par Torra [Int. J. Intell. Syst.12 (1997) 153–166.] “weighted ordered weighted average” (WOWA). Ces deux opérateurs n'étant signifiants que si les performances xj(a) se situent sur une même échelle d'intervalle E, je terminerai en proposant un autre type d'opérateur pouvant convenir lorsque E est une échelle purement ordinale.



The line balancing problem consits in assigning tasks to stations in orderto respect precedence constraints and cycle time constraints. In thispaper, the cycle time is fixed and the objective is to minimize the numberof stations. We propose to use metaheuristics based on simulated annealingby exploiting the link between the line balancing problem and the binpacking problem. The principle of the method lies in the combinationbetween a metaheuristic and a bin packing heuristic. Two representationsof a solution and two neighboring systems are proposed and the methods arecompared with results from the literature. They are better or similar totabu search based algorithm.


We address a multi-item capacitated lot-sizing problem with setup times that arises in real-world production planning contexts. Demand cannot be backlogged, but can be totally or partially lost. Safety stock is an objective to reach rather than an industrial constraint to respect. The problem is NP-hard. We propose mixed integer programming heuristics based on a planning horizon decomposition strategy to find a feasible solution. The planning horizon is partitioned into several sub-horizons over which a freezing or a relaxation strategy is applied. Some experimental results showing the effectiveness of the approach on real-world instances are presented. A sensitivity analysis on the parameters of the heuristics is reported.

This paper deals with a special case of Project Scheduling problem: there is a project to schedule, which is made up of activities linked by precedence relations. Each activity requires specific skills to be done. Moreover, resources are staff members who master fixed skill(s). Thus, each resource requirement of an activity corresponds to the number of persons doing the corresponding skill that must be assigned to the activity during its whole processing time. We search for an exact solution that minimizes the makespan, using a Branch-and-Bound method.


We develop a discrete-time approximation technique dealing with the time-cost trade-off problem in PERT networks. It is assumed that the activity durations are independent random variables with generalized Erlang distributions, in which the mean duration of each activity is a non-increasing function of the amount of resource allocated to it. It is also assumed that the amount of resource allocated to each activity is controllable. Then, we construct an optimal control problem with three conflicting objective functions. Solving this optimal control problem, optimally, is impossible. Therefore, a discrete-time approximation technique is applied to solve the original multi-objective optimal control problem, using goal attainment method. To show the advantages of the proposed technique, we also develop a Simulated Annealing (SA) algorithm to solve the problem, and compare the discrete-time approximation results against the SA and also the genetic algorithm results.

We describe the solution of a bound constrained convex quadratic problem with limited memory resources. The problem arises from physical simulations occurring within video games. The motivating problem is outlined, along with a simple interior point approach for its solution. Various linear algebra issues arising in the implementation are explored, including preconditioning, ordering and a number of ways of solving an equivalent augmented system. Alternative approaches are briefly surveyed, and some recommendations for solving these types of problems are given.

This paper presents a feasible primal algorithm for linear semidefinite programming. The algorithm starts with a strictly feasible solution, but in case where no such a solution is known, an application of the algorithm to an associate problem allows to obtain one. Finally, we present some numerical experiments which show that the algorithm works properly.

The soft-capacitated facility location problem, where each facility is composed of a variable number of fixed-capacity production units, has been recently studied in several papers, especially in the metric case. In this paper, we only consider the general problem where connection costs do not systematically satisfy the triangle inequality property. We show that an adaptation of the set covering greedy heuristic, where the subproblem is approximately solved by a fully polynomial-time approximation scheme based on cost scaling and dynamic programming, achieves a logaritmic approximation ratio of (1 + ε)H(n) for the problem, where n is the number of customers to be served and H is the harmonic series. This improves the previous bound of 2H(n) for this problem.

We study a deterministic problem of evaluating the worst case performance of flexible solutions in the single machine scheduling. A flexible solution is a set of schedules following a given structure determined by a partial order of jobs and a type of the schedules. In this paper, the schedules of active and non-delay type are considered. A flexible solution can be used on-line to absorb the impact of data disturbances related to, for example, job arrival, tool availability or machine breakdowns. The performance of a flexible solution includes the best case and the worst case performances. The best case performance is an ideal performance that can be achieved only if the on-line conditions allow to implement the best schedule of the set of schedules characterizing the flexible solution. In contrast, the worst case performance indicates how poorly the flexible solution may perform when following the given structure in the on-line circumstances. The best-case and the worst-case performances are usually evaluated by the minimum and maximum values of the considered objective function, respectively. We present algorithmic and computational complexity results for some maximization scheduling problems. In these problems, the jobs to be scheduled have different release dates and precedence constraints may be given on the set of jobs.

This paper investigates an inventory control problem where a firm orders and sells an inventory item through discount strategy in a price sensitive market. From the economic points of view, customers may expect a further price reduction when a firm uses pricing promotion to stimulate demand, the demand curve may vertically shift down when a firm reduces the selling price. Taking these phenomena into account, this paper developed a continuous inventory model for finding the ordering quantity, the number of pricing changing and times of price changes simultaneously so as to maximize the total profit. A solution procedure is developed for finding the optimal decision rules.

In this paper, we propose a new class of adaptive trust region methods for unconstrained optimization problems and develop some convergence properties. In the new algorithms, we use the current iterative information to define a suitable initial trust region radius at each iteration. The initial trust region radius is more reasonable in the sense that the trust region model and theobjective function are more consistent at the current iterate. The global convergence, super-linear and quadratic convergence rate are analyzed under some mild conditions. Numericalresults show that some special adaptive trust region methods are available and efficient in practical computation.

We prove that MAX-3SAT can be approximated in polynomial time within a factor 1.0957 on random instances.