Méthodes hybrides basées sur la génération de colonnes pour des problèmes de tournées de véhicules avec fenêtres de temps

RÉSUMÉ Un problème de tournées de véhicules avec fenêtres de temps consiste à faire la livraison de marchandise à un ensemble de clients avec une flotte de véhicules ayant un ou plusieurs points de départ appelés dépôts. Chaque client doit être desservi à l'intérieur d'une période prédéfinie, appelée fenêtre de temps. En pratique, on doit pouvoir respecter un grand nombre de contraintes et de caractéristiques complexes telles que des flottes hétérogènes de véhicules, des restrictions sur les routes, etc., en plus de devoir prendre en compte un grand nombre de clients. Il est donc primordial pour les distributeurs d'avoir accès à des outils performants d'optimisation capables de gérer un grand ensemble de contraintes de façon efficace. Dans cette thèse, nous présentons une méthode heuristique pour résoudre un ensemble de problèmes de tournées de véhicules de grande taille avec fenêtres de temps de façon efficace. Les problèmes abordés sont riches dans le sens où ils contiennent des caractéristiques non conventionnelles complexes s'apparentant à des problématiques réelles. La méthode proposée est un hybride entre une méthode métaheuristique de recherche à grands voisinages et une méthode exacte de génération de colonnes, la plus performante à ce jour pour résoudre de façon exacte des problèmes de tournées de véhicules assez contraints. La recherche à grands voisinages est une méthode où l'on vient itérativement détruire (phase de destruction) et reconstruire (reconstruction) des parties d'une solution courante afin d'obtenir de meilleures solutions. Les voisinages, définis dans la phase de destruction, sont explorés dans la phase de reconstruction. Dans notre méthode, les voisinages sont explorés par génération de colonnes gérée de façon heuristique. Une méthode de génération de colonnes sert à résoudre la relaxation linéaire d'un programme linéaire. Elle résout itérativement un problème maître, qui est le programme linéaire restreint à un sous-ensemble de variables, et un ou plusieurs sous-problèmes qui servent à rajouter des variables de coût réduit négatif au problème maître. La résolution se termine lorsque les sous-problèmes ne trouvent plus de variables de coût réduit négatif. Cette méthode est imbriquée dans un algorithme de séparation et évaluation pour obtenir des solutions entières. Plusieurs opérateurs sont définis pour sélectionner des éléments qui seront retirés de la solution courante dans la phase de destruction. À chaque itération, un opérateur est choisi aléatoirement en favorisant ceux qui ont permis d'améliorer la solution courante dans les itérations précédentes. La génération de colonnes sert ensuite à explorer le voisinage ainsi défini (reconstruction). Plusieurs aspects de la génération de colonnes sont gérés de façon heuristique afin d'obtenir de bonnes solutions en des temps raisonnables aux dépens de la certitude de trouver une solution optimale. Les sous-problèmes sont résolus par une méthode de recherche tabou et la génération de colonnes est stoppée après une trop faible amélioration de la valeur de la solution courante de la relaxation linéaire au cours des dernières itérations. Afin d'obtenir des solutions entières, un branchement agressif sur la variable ayant la valeur fractionnaire la plus grande est effectué. Sa valeur est fixée à 1 sans possibilité de retour en arrière.----------ABSTRACT Given a fleet of vehicles assigned to one or more depots, a vehicle routing problem with time windows consists of determining a set of feasible vehicle routes to deliver goods to a set of scattered customers. Every customer must be visited within a prescribed time interval, called a time window. In practice, vehicle routing problems can have many different types of constraints and complex characteristics such as a heterogeneous fleet, restrictions on the routes, etc., while having to serve a large number of customers. Therefore, it is essential for distributors to rely on competitive optimizing tools able to tackle a large number of constraints efficiently. In this thesis, we present an efficient heuristic method for solving a number of large-scale vehicle routing problems with time windows. The problems tackled are rich in the sense that they contain many non-conventional complex characteristics arising in real applications. We propose a hybrid between a large neighborhood search metaheuristic and a column generation exact method, hitherto the most efficient to solve constrained vehicle routing problems exactly. Large neighborhood search is an iterative method where we sequentially remove (destruction phase) and reinsert (reconstruction phase) parts of an incumbent solution in the hope of improving it. Neighborhoods defined in the destruction phase are explored in the reconstruction phase. We propose to explore the neighborhoods by column generation managed heuristically. A column generation method is used to solve the linear relaxation of a linear program. It solves iteratively a master problem, that is the linear program restricted to a subset of variables, and one or many subproblems that attempt to find new negative reduced cost variables to add to the master problem. The process ends when the subproblems cannot find any negative reduced cost variables. This method is embedded within a brand-and-bound algorithm to derive integer solutions. Several operators are defined to select elements that will be removed from the incumbent solution in the destruction phase. At every iteration, an operator is randomly selected favouring those who managed to improve the incumbent solution in the past iterations. Afterwards, column generation is used to explore the neighborhood defined by the operator (reconstruction phase). Many aspects of the column generation approach are managed heuristically in order to obtain good solutions in reasonable time at the expense of ensuring optimality. The subproblems are solved by means of a tabu search algorithm and the column generation is stopped if the value of the solution of the linear relaxation does not improve enough over the last iterations. An aggressive ranching scheme is used to derive integer solutions. Branching is done on the variable with the highest fractional value, which is fixed at 1 without the possibility to backtrack

Derivation and external validation of a simple risk score to predict in-hospital mortality in patients hospitalized for COVID-19: A multicenter retrospective cohort study

ABSTRACT: As severe acute respiratory syndrome coronavirus 2 continues to spread, easy-to-use risk models that predict hospital mortality can assist in clinical decision making and triage. We aimed to develop a risk score model for in-hospital mortality in patients hospitalized with 2019 novel coronavirus (COVID-19) that was robust across hospitals and used clinical factors that are readily available and measured standardly across hospitals. In this retrospective observational study, we developed a risk score model using data collected by trained abstractors for patients in 20 diverse hospitals across the state of Michigan (Mi-COVID19) who were discharged between March 5, 2020 and August 14, 2020. Patients who tested positive for severe acute respiratory syndrome coronavirus 2 during hospitalization or were discharged with an ICD-10 code for COVID-19 (U07.1) were included. We employed an iterative forward selection approach to consider the inclusion of 145 potential risk factors available at hospital presentation. Model performance was externally validated with patients from 19 hospitals in the Mi-COVID19 registry not used in model development. We shared the model in an easy-to-use online application that allows the user to predict in-hospital mortality risk for a patient if they have any subset of the variables in the final model. Two thousand one hundred and ninety-three patients in the Mi-COVID19 registry met our inclusion criteria. The derivation and validation sets ultimately included 1690 and 398 patients, respectively, with mortality rates of 19.6% and 18.6%, respectively. The average age of participants in the study after exclusions was 64 years old, and the participants were 48% female, 49% Black, and 87% non-Hispanic. Our final model includes the patient\u27s age, first recorded respiratory rate, first recorded pulse oximetry, highest creatinine level on day of presentation, and hospital\u27s COVID-19 mortality rate. No other factors showed sufficient incremental model improvement to warrant inclusion. The area under the receiver operating characteristics curve for the derivation and validation sets were .796 (95% confidence interval, .767-.826) and .829 (95% confidence interval, .782-.876) respectively. We conclude that the risk of in-hospital mortality in COVID-19 patients can be reliably estimated using a few factors, which are standardly measured and available to physicians very early in a hospital encounter

Structural and functional changes of peripheral muscles in chronic obstructive pulmonary disease patients

PURPOSE OF REVIEW: The purpose of this review is to identify new advances in our understanding of skeletal muscle dysfunction in patients with COPD. RECENT FINDINGS: Recent studies have confirmed the relevance of muscle dysfunction as an independent prognosis factor in COPD. Animal studies have shed light on the molecular mechanisms governing skeletal muscle hypertrophy/atrophy. Recent evidence in patients with COPD highlighted the contribution of protein breakdown and mitochondrial dysfunction as pathogenic mechanisms leading to muscle dysfunction in these patients. SUMMARY: Chronic Obstructive Pulmonary Disease (COPD) is a debilitating disease impacting negatively on health status and the functional capacity of patients. COPD goes beyond the lungs and incurs significant systemic effects among which muscle dysfunction/wasting in one of the most important. Muscle dysfunction is a prominent contributor to exercise limitation, healthcare utilization and an independent predictor of morbidity and mortality. Gaining more insight into the molecular mechanisms leading to muscle dysfunction/wasting is key for the development of new and tailored therapeutic strategies to tackle skeletal muscle dysfunction/wasting in COPD patients

European driver rules in vehicle routing with time windows

As of April 2007, the European Union has new regulations concerning driver working hours. These rules force the placement of breaks and rests into vehicle routes when consecutive driving or working time exceeds certain limits. This paper proposes a large neighborhood search method for the vehicle routing problem with time windows and driver regulations. In this method, neighborhoods are explored using a column generation heuristic that relies on a tabu search algorithm for generating new columns (routes). Checking route feasibility after inserting a customer into a route in the tabu search algorithm is not an easy task. To do so, we model all feasibility rules as resource constraints, develop a label-setting algorithm to perform this check, and show how it can be used efficiently to validate multiple customer insertions into a given existing route. We test the overall solution method on modified Solomon instances and report computational results that clearly show the efficiency of our method compared to two other existing heuristics