Global disparities in surgeons’ workloads, academic engagement and rest periods: the on-calL shIft fOr geNEral SurgeonS (LIONESS) study

: The workload of general surgeons is multifaceted, encompassing not only surgical procedures but also a myriad of other responsibilities. From April to May 2023, we conducted a CHERRIES-compliant internet-based survey analyzing clinical practice, academic engagement, and post-on-call rest. The questionnaire featured six sections with 35 questions. Statistical analysis used Chi-square tests, ANOVA, and logistic regression (SPSS® v. 28). The survey received a total of 1.046 responses (65.4%). Over 78.0% of responders came from Europe, 65.1% came from a general surgery unit; 92.8% of European and 87.5% of North American respondents were involved in research, compared to 71.7% in Africa. Europe led in publishing research studies (6.6 ± 8.6 yearly). Teaching involvement was high in North America (100%) and Africa (91.7%). Surgeons reported an average of 6.7 ± 4.9 on-call shifts per month, with European and North American surgeons experiencing 6.5 ± 4.9 and 7.8 ± 4.1 on-calls monthly, respectively. African surgeons had the highest on-call frequency (8.7 ± 6.1). Post-on-call, only 35.1% of respondents received a day off. Europeans were most likely (40%) to have a day off, while African surgeons were least likely (6.7%). On the adjusted multivariable analysis HDI (Human Development Index) (aOR 1.993) hospital capacity > 400 beds (aOR 2.423), working in a specialty surgery unit (aOR 2.087), and making the on-call in-house (aOR 5.446), significantly predicted the likelihood of having a day off after an on-call shift. Our study revealed critical insights into the disparities in workload, access to research, and professional opportunities for surgeons across different continents, underscored by the HDI

Résolution du problème du multi-sac-à-dos quadratique en variables entières

L objet de cette thèse est la résolution d un problème d optimisation combinatoire discrète, le problème du multi-sac-à-dos quadratique en variables entières, que nous notons (QMKP). Ce problème est NP-difficile. Nous étudions, dans un premier temps, un cas particulier du problème (QMKP) : le problème du multi-sac-à-dos quadratique en variables entières dont la fonction économique est concave et séparable. Dans ce contexte, nous élaborons une méthode exacte de recherche arborescente par séparation et évaluation pour le résoudre. Cette dernière repose sur le calcul d un majorant fin de la valeur optimale de (QMKP). Ce majorant provient d une méthode basée sur une linéarisation et une relaxation agrégée du problème initial. Notre branch-and-bound incorpore également des procédures de pré-traitement en amont. Nous comparons les performances, en terme de temps de calcul et de qualité, de notre approche avec trois autres méthodes existantes : un algorithme de branch-and-bound développé par Djerdjour et al. (1988), une formulation linéarisée en variables 0-1 (initialement utilisée pour résoudre le sac-à-dos quadratique mono-contraint en variables entières que nous avons étendu au cas multi-contraint), un branch-and-bound classique (optimisation quadratique Cplex9.0). Notre branch-and-bound est clairement le plus performant nous permettant de résoudre des problèmes de grandes tailles (jusqu à 2000 variables et contraintes) en moins de 5 minutes en moyenne. Dans un deuxième temps, nous étendons nos recherches au cas où la fonction économique du problème (QMKP) est concave et non séparable. Nous proposons alors une voie théorique de résolution de ce problème basée sur une transformation du problème non séparable en un problème séparable. Nous sommes alors en mesure de lui appliquer notre algorithme développé pour résoudre (QMKP) séparable et ainsi de fournir l optimum entier de ce problème.This thesis deals with the integer quadratic multi-knapsack problem (QMKP). This problem is NP-hard. We first study a special case of (QMKP): the integer quadratic multi-knapsack problem where the objective function is concave and separable. In this context, we develop a branch-and-bound algorithm to solve (QMKP) to optimality. This method is based on the computation of a tight upper bound for (QMKP) which is derived from a linearization and a surrogate relaxation. Our branch-and-bound also incorporates pre-processing procedures. The computational performance of our branch-and-bound is compared to that of three exact methods: a branch-and-bound algorithm developed by Djerdjour et al. (1988), a 0-1 linearization method originally applied to the separable quadratic knapsack problem with a single constraint that we extend to the case of m constraints, a standard branch-and-bound algorithm (Cplex9.0 quadratic optimization). Our branch-and-bound clearly outperforms other methods for large instances (up to 2000 variables and constraints in 5 minutes). Finally, we suggest a theoretical approach to solve the problem (QMKP) where the objective function is concave and non separable. We transform the non separable problem into a separable in order to be able to apply our branch-and-bound for the separable case in this context.PARIS-DAUPHINE-BU (751162101) / SudocSudocFranceF

hybridation de méthodes intérieures et de métaheuristiques pour la programmation linéaire en nombres entiers

PARIS-DAUPHINE-BU (751162101) / SudocSudocFranceF

Optimisation du routage dans les réseaux internet avec qualité de servicee

Dans les réseaux Internet, les protocoles de routage classiques acheminent les demandes de trafic sur des plus courts chemins selon des poids administratifs fixés sur les liens de ces réseaux. Il peut arriver que plusieurs plus-courts chemins existent entre l'origine et la destination d'une demande. Dans ce cas, un chemin parmi les plus courts est arbitrairement choisi, et l'administrateur du réseau n'a plus la maîtrise des chemins de routage, ou alors un partage de charge, difficile à gérer, est réalisé. De plus, une certaine qualité de service est devenue nécessaire à garantir avec l'explosion du trafic Internet. Nous nous sommes donc intéressés au problème de routage selon d'uniques plus-courts-chemins; nous déterminons un ensemble de poids qui assure l'unicité des plus-courts-chemins de routage tout en optimisant un critère de garantie de performance dans le choix de ces chemins. Nous modélisons ce problème par un programme linéaire à variables mixtes et nous le résolvons avec des méthodes de l'optimisation combinatoire.In most Internet routing protocols, traffic demands are routed on shortest paths according to a set of administrative weights. However, several shortest paths can co-exist between the origin and the destination of a demand. In this case, one of these shortest paths is arbitrarily chosen to route the demand or a load balancing is realized, and it becomes difficult for the administrator of the network to control the overall routing paths scheme or to manage the load balancing. Quality of service has also become necessary because of the explosion of Internet traffic these last years. This is the reason why we focused on the problem of unique shortest paths routing for which we determine a set of weights that ensures the unicity of the shortest paths while optimizing a quality of service criterion. We formulate this routing problem using linear programs with mixed integer variables and solve it with combinatorial optimization methods.PARIS-DAUPHINE-BU (751162101) / SudocSudocFranceF

An interactive multiobjective nonlinear programming procedure

This paper develops a method for interactive MultiObjective NonLinear Programming procedure (MONLP). It provides a detailed description of an efficient algorithm, and reports on promising computational results. It also discusses several alternative strategies for implementing GRG code (Generalized Reduced Gradient), which is known as one of the ‘best’ methods for solving NonLinear optimization Problems (Abadie, 1978). The method relies on three steps: 1) generation of a subset of feasible efficient solutions; 2) interactive definition by Decision Maker (DM) of his preference structure according to desired outcome; 3) determination of a compromise solution using nonlinear optimization; a global analysis based on “reference point search procedure” is performed (in the criteria space). Following this methodology, it is possible for the DM to find his final solution. A microcomputer version (for medium problems) of the method is available

A Two-Phase Path Relinking Algorithm for the Generalized Assignment Problem

We propose a two-phase Path-Relinking (PR) heuristic for the NP-hard Generalized Assignment Problem. The first phase of the algorithm combines LP-solving, cuts and local search for obtaining a population of solutions with guided diversity. The second phase applies the PR template to the population built at phase one. Both feasible and infeasible solutions are inserted in the reference set during combinations of PR. The trade-off between feasibility and infeasibility is controlled through a penalty coefficient for infeasibility which is dynamically updated throughout the search for maintaining a balance between feasible and infeasible solutions in the reference set. Numerical experiments on classical testbed instances of the OR-library show the practical efficiency of the method

Upper Bounds for Large Scale Integer Quadratic Multidimensional Knapsack Problems

Abstract⎯We consider the separable quadratic multi-knapsack problem (QMKP) which consists in maximizing a concave separable quadratic integer function subject to m linear capacity constraints. The aim of this paper is to develop an effective method to compute an upper bound for (QMKP) from a surrogate relaxation originally proposed in Djerdjour et al. (1988). The quality of three other upper bounds for (QMKP) is evaluated and they are compared theoretically and experimentally with the bound we suggest. An effective heuristic method is presented to obtain a good feasible solution for (QMKP). Finally, computational experiments are reported. They assess the efficiency of our upper bound for instances up to 2000 variables and constraints