La métaheuristique CAT pour le design de réseaux logistiques déterministes et stochastiques

De nos jours, les entreprises d’ici et d’ailleurs sont confrontées à une concurrence mondiale sans cesse plus féroce. Afin de survivre et de développer des avantages concurrentiels, elles doivent s’approvisionner et vendre leurs produits sur les marchés mondiaux. Elles doivent aussi offrir simultanément à leurs clients des produits d’excellente qualité à prix concurrentiels et assortis d’un service impeccable. Ainsi, les activités d’approvisionnement, de production et de marketing ne peuvent plus être planifiées et gérées indépendamment. Dans ce contexte, les grandes entreprises manufacturières se doivent de réorganiser et reconfigurer sans cesse leur réseau logistique pour faire face aux pressions financières et environnementales ainsi qu’aux exigences de leurs clients. Tout doit être révisé et planifié de façon intégrée : sélection des fournisseurs, choix d’investissements, planification du transport et préparation d’une proposition de valeur incluant souvent produits et services au fournisseur. Au niveau stratégique, ce problème est fréquemment désigné par le vocable « design de réseau logistique ». Une approche intéressante pour résoudre ces problématiques décisionnelles complexes consiste à formuler et résoudre un modèle mathématique en nombres entiers représentant la problématique. Plusieurs modèles ont ainsi été récemment proposés pour traiter différentes catégories de décision en matière de design de réseau logistique. Cependant, ces modèles sont très complexes et difficiles à résoudre, et même les solveurs les plus performants échouent parfois à fournir une solution de qualité. Les travaux développés dans cette thèse proposent plusieurs contributions. Tout d’abord, un modèle de design de réseau logistique incorporant plusieurs innovations proposées récemment dans la littérature a été développé; celui-ci intègre les dimensions du choix des fournisseurs, la localisation, la configuration et l’assignation de mission aux installations (usines, entrepôts, etc.) de l’entreprise, la planification stratégique du transport et la sélection de politiques de marketing et d’offre de valeur au consommateur. Des innovations sont proposées au niveau de la modélisation des inventaires ainsi que de la sélection des options de transport. En deuxième lieu, une méthode de résolution distribuée inspirée du paradigme des systèmes multi-agents a été développée afin de résoudre des problèmes d’optimisation de grande taille incorporant plusieurs catégories de décisions. Cette approche, appelée CAT (pour collaborative agent teams), consiste à diviser le problème en un ensemble de sous-problèmes, et assigner chacun de ces sous-problèmes à un agent qui devra le résoudre. Par la suite, les solutions à chacun de ces sous-problèmes sont combinées par d’autres agents afin d’obtenir une solution de qualité au problème initial. Des mécanismes efficaces sont conçus pour la division du problème, pour la résolution des sous-problèmes et pour l’intégration des solutions. L’approche CAT ainsi développée est utilisée pour résoudre le problème de design de réseaux logistiques en univers certain (déterministe). Finalement, des adaptations sont proposées à CAT permettant de résoudre des problèmes de design de réseaux logistiques en univers incertain (stochastique)

Simulation et conception d'heuristiques efficaces pour un problème d'assemblage de planchers

Dans l'industrie de la fabrication de planchers de bois pour les remorques, la qualité d'un plancher est directement liée à la position relative des lattes de bois. Le procédé de fabrication actuellement en place chez notre partenaire industriel utilise une main-d'oeuvre abondante. Les planchers sont produits en continu et puisque les décisions de positionnement de lattes doivent se prendre très rapidement (chaque employé doit placer une latte à toutes les 1,5 seconde), même des employés d'expérience peuvent commettre des erreurs. Dans ce mémoire, nous proposons des méthodes rapides et efficaces pouvant être utilisées par un processus d'assemblage mécanique. Ces méthodes ont été utilisées pour assembler des planchers à l'aide de simulation par ordinateur. Par comparaison à l'assemblage manuel, qui produit des planchers ayant environ 5% de joints défectueux, nos méthodes d'assemblage produisent des planchers presque parfaits

Biomass and carbon dynamics in forest management at a strategic scale

This study explored these two questions: (1) How much carbon can be stored in the forest? and (2) Which forest management regimes best achieve the dual objectives of high sustained timber yield and high carbon sequestration? A model that can be used to predict carbon sequestration potential within a forest region assuming a given management strategy was developed. First, a carbon sequestration unit that accounts for both the amount of carbon stocked and the time during which it is stocked was introduced. This unit was used to integrate the carbon dimension in a Model-III formulation for forest management adapted from the description of models used by the Chief Forester who is responsible of determining the annual allowable cut in the different forest management units in Québec. The CBM-CFS3 model was used to simulate carbon dynamics of above- and belowground biomass and dead organic matter, including soils. Different management scenarios were developed using the data of an actual forest management unit in Quebec. Managing this forest for carbon maximization instead of letting grow naturally with no harvest or other treatment, would increase the carbon stocks by 1.89%, and only 25% of the carbon stock is estimated to occur in the aboveground live pool. Six scenarios aimed at achieving the dual objectives of high sustained timber yield and high carbon storage were also computed and compared

A strategic forest management model for optimizing timber yield and carbon sequestration

Strategic forest management planning models designed to maintain existing carbon stocks and maximize capacity for future sequestration can help identify underused opportunities to increase carbon stocks without diminishing other forest products. This study proposed a carbon stock unit that allows summing up the stocks in the different forest pools even if the decomposition far exceeds the planning horizon. This unit is used to integrate the methods and algorithms from the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) model into a wood supply model. The resulting model could be used to predict changes in carbon stocks, transfers between carbon pools, and greenhouse gas emissions that would result from every forest management activity. We tailored this model to meet different strategies: maximizing carbon storage in the forest, maximizing high-sustained timber yield, and achieving the dual objectives of yield and carbon storage. A range of management scenarios were simulated using the data of a 485,000 hectares mixed-wood forest in Quebec, Canada. Our results demonstrate that, with the reduction in the harvest rates, the increase in the ecosystem carbon storage is insufficient to offset the carbon losses associated with the increase in the harvest rates. Study Implications: In this article, we adopt the perspective of forest managers who contend that removing lumber from the forest can be achieved in a responsible way or in a way that does not affect the carbon stocks in the forest in the long term. We propose a model that integrates methods and algorithms from the CBM-CFS3 model to simulate carbon dynamics of aboveground and belowground biomass and dead organic matter, including soils. The model can be used to predict carbon storage potential within a forest region assuming a given management strategy. We used data of a large forest area to develop a number of sophisticated scenarios of strategic forest planning. Our results are consistent with the forest managers’ contentions. When carbon was maximized regardless of volume, the increase in the ecosystem carbon storage was insufficient to offset the carbon losses associated with the reduction in the harvest rates

Timber selling policies using bundle-based auction : the case of public forests in Québec

In the province of Québec, the government provides 25% of the volume of timber that is annually cut in crown forests through sealed-bid one-winner auctions. It was noted that many offers are made for some areas but few or none are made for many other areas. As such, a significant number of the timber volumes remains unsold. However, the combination of areas to form bundles can provide economy of scale that is not seen otherwise. We highlight some issues regarding the current allocation system and we analyse the effectiveness of different bundling systems in maximizing government revenues and enhancing bidders' competitiveness. We use actual forest data to evaluate different rules and strategies for the creation and allocation of partial and full bundles. Our results suggest that the use of the option of bundling forests areas makes the auction process more beneficial to the majority of stakeholders: Government revenues are increased; the bidding companies are more likely to obtain the desired volumes and pay less for harvesting and equipment relocation; and greenhouse gas emissions are reduced

Integrating revenue management and sales and operations planning in a Make-To-Stock environment : softwood lumber case study

Most research regarding revenue management in manufacturing has considered only a short-term planning horizon, assuming supply and production data exogenously given. Motivated by the case of the Canadian softwood lumber industry, this paper offers additionally a medium-term visibility for firms with limited capacity and faced with seasonal markets. We propose a demand management process for Make-To-Stock environments, integrating sales and operations planning (S&OP) and order promising based on revenue management concepts. Given heterogeneous customers, divergent product structure and multiple sourcing locations in a multi-period context, we first define a multi-level decision framework in order to support medium-term, short-term and real-time sales decisions in a way to maximize profits and to enhance the service level offered to high-priority customers. We further propose a mathematical formulation integrating an S&OP network model in the Canadian softwood lumber industry and an order promising model using nested booking limits. This new formulation allows reviewing previous order promising decisions while respecting sales commitments. A rolling horizon simulation is used to evaluate the performance of the proposed process in various demand scenarios and provides evidence that better performances can be achieved compared to common demand management practices by integrating S&OP and revenue management concepts

Kriging analysis of an integrated demand management process in softwood industry

Objective: This paper aims to develop a basic understanding of a demand management process integrating sales and operations planning (S&OP) and order promising in a Make-To-Stock environment and to compare different demand management policies. Contribution: Typical researches about demand management processes analyze few system specifications or vary few potential factors one at time. Yet, we can get additional insights by employing design of experiments (DOE). Methodology: For making promises, we compare a First-Come First-Served approach to an approach using nested booking limits and giving advantage to profitable customers and attractive periods. Considering various sequences of order arrival, we generate Kriging metamodels that best describe the nonlinear relationships between the simulation responses and system factors for Canadian softwood lumber firms. We employ a Latin hypercube design to take into account different environmental scenarios. Results: Our analysis reveals the potential to improve the performance of the demand management process if we know high-priority customers needs before fulfilling less-priority orders and if we use nested booking limits concept

Configuration and evaluation of an integrated demand management process using a space-filling design and Kriging metamodeling

Objective: This research aims to develop a basic understanding of a demand management process inte- grating sales and operations planning (S&OP) and order promising in a Make-To-Stock environment and to compare different demand management policies with limited capacity. Contribution: Typical researches about demand management processes analyze few system specifications or vary few potential factors one at a time. Yet, additional insights can be obtained by employing a space- filling design and Kriging metamodeling for analysis. Methodology: We compare two configurations of the integrated demand management process. While the First-Come First-Served concept is used at the order promising level for the first configuration, the sec- ond configuration uses nested booking limits and gives advantage to profitable customers and attractive periods. Considering various order arrival sequences, we generate Kriging metamodels that best describe the nonlinear relationships between four environmental factors (demand intensity, demand forecast er- ror, customer heterogeneity and coefficient of variation) and three performance measures (yearly profit margin, yearly sales and high-priority fill rate) for Canadian softwood lumber firms. Since our simulation experiments are time-consuming, we employ a Latin hypercube design to efficiently take into account different market situations. Results: Our analysis reveals the potential to improve the performance of the demand management pro- cess if we know high-priority customers needs before fulfilling low-priority orders and if we use nested booking limits concept

Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √s = 13 TeV

Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂ t) and chromomagnetic (μ̂ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ¯ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ¯ final states. The values found for the parameters are AFB(1)=0.048−0.087+0.095(stat)−0.029+0.020(syst),μ̂t=−0.024−0.009+0.013(stat)−0.011+0.016(syst), and a limit is placed on the magnitude of | d̂ t| < 0.03 at 95% confidence level. [Figure not available: see fulltext.