This paper studies scheduling problems which include a combination of nonlinear job deterioration and a time-dependent learning effect. We use past sequence dependent (p-s-d) setup times, which is first introduced by Koulamas and Kyparisis [Eur. J. Oper. Res.187 (2008) 1045–1049]. They considered a new form of setup times which depend on all already scheduled jobs from the current batch. Job deterioration and learning co-exist in various real life scheduling settings. By the effects of learning and deterioration, we mean that the processing time of a job is defined by increasing function of its execution start time and a function of the total normal processing time of jobs scheduled prior to it. The following objectives are considered: single machine makespan and sum of completion times (square) and the maximum lateness. For the single-machine case, we derive polynomial-time optimal solutions.

In this paper, we present an Uzawa-based heuristic that is adapted to certain type of stochastic optimal control problems. More precisely, we consider dynamical systems that can be divided into small-scale subsystems linked through a static almost sure coupling constraint at each time step. This type of problem is common in production/portfolio management where subsystems are, for instance, power units, and one has to supply a stochastic power demand at each time step. We outline the framework of our approach and present promising numerical results on a simplified power management problem.

This paper addresses an ongoing experience in the design of an artificial agent taking decisions and combining them with the decisions taken by human agents. The context is a serious game research project, aimed at computer-based support for participatory management of protected areas (and more specifically national parks) in order to promote biodiversity conservation and social inclusion. Its objective is to help various stakeholders (e.g., environmentalist, tourism operator) to collectively understand conflict dynamics and explore negotiation strategies for the management of parks. In this paper, after introducing the design of our serious game, named SimParc, we will describe the architecture of the decision making agent playing the role of the park manager. In the game, the park manager makes final decisions based on its own analysis and also on the votes of the stakeholders. It includes two modules: 1) individual decision – based on a model of argumentation, which also provides a basis to justify and explain the decision; 2) participatory decision – to take into account the preferences/votes from the stakeholders.

In the last decade, several robustness approaches have been developed to deal with uncertainty. In decision problems, and particularly in location problems, the most used robustness approach rely either on maximal cost or on maximal regret criteria. However, it is well known that these criteria are too conservative. In this paper, we present a new robustness approach, called lexicographic α-robustness, which compensates for the drawbacks of criteria based on the worst case. We apply this approach to the 1-median location problem under uncertainty on node weights and we give a specific algorithm to determine robust solutions in the case of a tree. We also show that this algorithm can be extended to the case of a general network.

Recently, [Y.Q. Bai, M. El Ghami and C. Roos, SIAM J. Opt. 15 (2004) 101–128] investigated a new class of kernel functions which differs from the class of self-regular kernel functions. The class is defined by some simple conditions on the growth and the barrier behavior of the kernel function. In this paper we generalize the analysis presented in the above paper for P*(κ) Linear Complementarity Problems (LCPs). The analysis for LCPs deviates significantly from the analysis for linear optimization. Several new tools and techniques are derived in this paper.

We introduce augmented Lagrangian methods for solving finite dimensional variational inequality problems whose feasible sets are defined by convex inequalities, generalizing the proximal augmented Lagrangian method for constrained optimization. At each iteration, primal variables are updated by solving an unconstrained variational inequality problem, and then dual variables are updated through a closed formula. A full convergence analysis is provided, allowing for inexact solution of the subproblems.

We consider the maximum weight perfectly matchable subgraph problem on a bipartite graph G=(UV,E) with respect to given nonnegative weights of its edges. We show that G has a perfect matching if and only if some vector indexed by the nodes in UV is a base of an extended polymatroid associated with a submodular function defined on the subsets of UV. The dual problem of the separation problem for the extended polymatroid is transformed to the special maximum flow problem on G. In this paper, we give a linear programming formulation for the maximum weight perfectly matchable subgraph problem and propose an O(n3) algorithm to solve it.

Information quality is crucial to any information fusion system as combining unreliable or partially credible pieces of information may lead to erroneous results. In this paper, Dempster-Shafer theory of evidence is being used as a framework for representing and combining uncertain pieces of information. We propose a method of dynamic estimation of evidence discounting rates based on the credibility of pieces of information. The credibility of a piece of information Cre(In) is evaluated through a measure of consensus (corroboration degree) between a set of belief functions, and this measure serves as a basis for quantifying the credibility of the source (sensor or fusion node) itself, Cre(Sk), used then as a discounting factor for all further belief functions provided by Sk. The process is dynamic in the sense that the credibility of the source is revisited in the light of new incoming piece of information. The method proposed relies on a hybrid fusion topology in which the sensors are grouped according to the feature they measure (similar and dissimilar sensors), allowing to select different kinds of measure for estimating the corroboration degrees. Through simulations, we compare (a) the hybrid-combination using the source credibility and the robust combination rule (RCR-L) accounting automatically for sensors's credibility; (b) the hybrid-combination, with different membership degrees and corroboration degrees used to estimate the sources credibility. We show that the new hybrid topology together with the credibility-based evidence discounting estimation algorithm provide a faster identification of the observed object.

La finance de marché est devenue un des domaines d'ap- plication privilégiés de la recherche opérationnelle. D'un autre côté, rares sont les applications touchant la banque de détail, tournée vers le grand public. Dans ce papier, nous abordons un problème d'actualité dans le secteur bancaire français : l'optimisation de plans de financement immobiliers. Le travail que nous présentons a été effectué dans le cadre du développement par la société Experian-Prologia d'une nouvelle application d'instruction de prêts immobiliers pour une grande banque française. Notre module d'optimisation, au cœur de cette application, est aujourd'hui déployé dans les 2200 agences de cette banque et permet la simulation et le montage de plusieurs milliers de plans de financement immobiliers chaque mois.

The VIKOR method was introduced as a Multi-Attribute Decision Making (MADM) method to solve discrete decision-making problems with incommensurable and conflicting criteria. This method focuses on ranking and selecting from a set of alternatives based on the particular measure of “closeness” to the “ideal” solution. The multi-criteria measure for compromise ranking is developed from the l–p metric used as an aggregating function in a compromise programming method. In this paper, the VIKOR method is extended to solve Multi-Objective Large-Scale Non-Linear Programming (MOLSNLP) problems with block angular structure. In the proposed approach, the Y-dimensional objective space is reduced into a one-dimensional space by applying the Dantzig-Wolfe decomposition algorithm as well as extending the concepts of VIKOR method for decision-making in continues environment. Finally, a numerical example is given to illustrate and clarify the main results developed in this paper.

The partial inverse minimum cut problem is to minimally modify the capacities of a digraph such that there exists a minimum cut with respect to the new capacities that contains all arcs of a prespecified set. Orlin showed that the problem is strongly NP-hard if the amount of modification is measured by the weighted L1-norm. We prove that the problem remains hard for the unweighted case and show that the NP-hardness proof of Yang [RAIRO-Oper. Res.35 (2001) 117–126] for this problem with additional bound constraints is not correct.

Combat Management System training uses simulation of an overall tactical situation. This involves the real-time management of numerous and diverse entities to keep the simulation scenario consistent in a highly dynamic environment. To address this difficult problem, we propose an adaptive multi-agent system in which each entity is considered as a smart sensor/effector mobile. The autonomy and the dynamic behaviour offered to each entity leads the simulation to self-adapt to inevitable disturbances of the user. According to the cooperation paradigm, this approach also allows the mobiles to highlights a coherent global behaviour with mutual helping. Finally, the system shows the relevance of the Emergence Technologies in the elaboration of a new generation of sensors. This software is currently under development in GATES, a project of the DCNS company.

Determining amino acid sequences of protein molecules is one of the most important issues in molecular biology. These sequences determine protein structure and functionality. Unfortunately, direct biochemical methods for reading amino acid sequences can be used for reading short sequences only. This is the reason, which makes peptide assembly algorithms an important complement of these methods. In this paper, a genetic algorithm solving the problem of short amino acid sequence assembly is presented. The algorithm has been tested in computational experiment and compared with an existing tabu search method for the same problem. The results clearly show that the genetic algorithm outperformed the tabu search approach.

We discuss the problem of computing points of IRn whose convex hull contains the Euclidean ball, and is contained in a small multiple of it. Given a polytope containing the Euclidean ball, we introduce its successor obtained by intersection with all tangent spaces to the Euclidean ball, whose normals point towards the vertices of the polytope. Starting from the L∞ ball, we discuss the computation of the two first successors, and give a complete analysis in the case when n=6.

In this paper we propose a primal-dual interior-point algorithm for convex quadratic semidefinite optimization problem. The search direction of algorithm is defined in terms of a matrix function and the iteration is generated by full-Newton step. Furthermore, we derive the iteration bound for the algorithm with small-update method, namely, O($\sqrt{n}$ log $\frac{n}{\varepsilon}$), which is best-known bound so far.

In this paper, we propose a new method to generate a continuous belief functions from a multimodal probability distribution function defined over a continuous domain. We generalize Smets' approach in the sense that focal elements of the resulting continuous belief function can be disjoint sets of the extended real space of dimension n. We then derive the continuous belief function from multimodal probability density functions using the least commitment principle. We illustrate the approach on two examples of probability density functions (unimodal and multimodal). On a case study of Search And Rescue (SAR), we extend the traditional probabilistic framework of search theory to continuous belief functions theory. We propose a new optimization criterion to allocate the search effort as well as a new rule to update the information about the lost object location in this latter framework. We finally compare the allocation of the search effort using this alternative uncertainty representation to the traditional probabilistic representation.

This article focuses on data aggregation in vehicular ad hoc networks. In such networks, sensor data are usually produced and exchanged between vehicles in order to warn or inform the drivers when an event is detected (e.g., accident, emergency braking, parking space released, vehicle with non-functioning brake lights, etc.). In the following, we present a solution to aggregate and store these data in order to have a history of past events. We therefore use Flajolet-Martin sketches. Our goal is to generate additional knowledge to assist drivers by providing them useful information even if no event is transmitted by vehicles in the vicinity.