Le contexte d’application de cette recherche a été le projet CRIBA. CRIBA vise à industrialiser une solution intégrée de rénovation et d’isolation de grands bâtiments. De ce fait, une part importante de la valeur ajoutée est transférée des chantiers de rénovation vers des usines de fabrications devant être synchronisées avec les chantiers. La planification est l'une des étapes importantes de la gestion de projets. S’adaptant à une organisation, elle vise une réalisation optimale en considérant les facteurs de temps, coût, qualité ainsi que l’affectation efficace des ressources. Cette affectation est d’autant plus complexe lorsqu’un ensemble de projets se partagent les ressources, renouvelables ou non renouvelables. L'objectif global de notre étude est de développer un outil d’aide à la décision pour un décideur visant à planifier plusieurs projets en intégrant l'allocation des ressources renouvelables, et la planification des flux de ressources non-renouvelables vers ces projets. Dans ce cadre, les ressources non renouvelables telles que les machines et la main-d'œuvre ont une disponibilité initiale limitée sur les chantiers. Cependant, nous supposons que des quantités limitées supplémentaires peuvent être achetées. En outre, nous prenons en compte la volonté des coordinateurs des projets pour l’approvisionnement des chantiers en juste à temps (just in time), en particulier pour les ressources peu demandées, encombrantes et à forte valeur. Ceci oblige à étendre le cadre du modèle de la planification des projets en incluant la planification de la chaîne logistique qui approvisionne les ressources non renouvelables des chantiers. Enfin, pour répondre au besoin d’outils décisionnels responsables sur le plan environnemental, le modèle prévoit le transport et le recyclage des déchets des chantiers dans les centres appropriés. Un modèle linéaire mixte du problème est ainsi posé. Puisqu’il rentre dans la classe des modèles d'optimisation NP-durs, une double résolution est proposée. D’abord à l’aide d’un solveur puis une méta-heuristique basée sur un algorithme génétique. De plus, pour faciliter l'utilisation du modèle par des utilisateurs peu familiers avec la recherche opérationnelle, un système d'aide à la décision basé sur une application web a été développé. L’ensemble de ces contributions ont été évaluées sur des jeux de test issus du projet CRIBA. / The application context of the current study is on a CRIBA project. The CRIBA aims to industrialize an integrated solution for the insulation and thermal renovation of building complexes in France. As a result, a significant part of the added value is transferred from the renovation sites to the manufacturing centers, making both synchronized. Planning is one of the important steps in project management. Depending on the different viewpoints of organizations, successful planning for projects can be achieved by performing to optimality within the time, cost, quality factors as well as the efficient assignment of resources. Planning for the allocation of resources becomes more complex when a set of projects is sharing renewable and non-renewable resources. The global objective of the study is to develop a decision-making tool for decision-makers to plan multiple projects by integrating the allocation of the renewable resources and planning the flow of non-renewable resources to the project worksites. In this context, non-renewable resources such as equipment and labor have a limited initial availability at the construction sites. Nevertheless, we assume that additional limited amounts can be added to the projects. In addition, we take into account the interest of the project coordinators in supplying the non-renewable resources in a just-in-time manner to the projects, especially for low-demand resources with a high price. This requires extending the framework of the project planning by including the planning of the supply chain which is responsible. Finally, in order to meet the requirements for environmentally responsible decision-making, the model envisages the transportation and recycling of waste from project sites to appropriate centers. A mixed integer linear model of the problem is proposed. Since it falls within the class of NP-hard optimization models, a double resolution is targeted: first, using a solver and then a metaheuristic based on the genetic algorithm. In addition, in order to facilitate the use of the model by users unfamiliar with operational research, a web-based decision-making support system has been developed. All the contributions are evaluated in a set of case studies from the CRIBA project.

In this thesis, we study three supply chain planning problems. The first two problems fall in the tactical planning level, while the third one falls in the strategic/tactical level. We present a direct application for the first two planning problems in the wind turbines industry. For the third problem, we show how it can be applied to supply chains in the food industry. Many countries and localities have the explicitly stated goal of increasing the fraction of their electrical power that is generated by wind turbines. This has led to a rapid growth in the manufacturing and installation of wind turbines. The globally installed capacity for the manufacturing of different components of the wind turbine is nearly fully utilized. Because of the large penalties for missing delivery deadlines for wind turbines, the effective planning of its supply chain has a significant impact on the profitability of the turbine manufacturers. Motivated by the planning challenges faced by one of the world’s largest manufacturers of wind turbines, we present a comprehensive tactical supply chain planning model for manufacturing of wind turbines in the first part of this thesis. The model is multi-period, multi-echelon, and multi-commodity. Furthermore, the model explicitly incorporates backorder penalties with a general cost structure, i.e., the cost structure does not have to be linear in function of the backorder delay. To the best of our knowledge, modeling-based supply chain planning has not been applied to wind turbines, nor has a model with all the above mentioned features been described in the literature. Based on real-world data, we present numerical results that show the significant impact of the capability to model backorder penalties with general cost structures on the overall cost of supply chains for wind turbines. With today’s rapidly changing global market place, it is essential to model uncertainty in supply chain planning. In the second part of this thesis, we develop a two-stage stochastic programming model for the comprehensive tactical planning of supply chains under supply uncertainty. In the first stage, procurement decisions are made while in the second stage, production, inventory, and delivery decisions are made. The considered supply uncertainty combines supplier random yields and stochastic lead times, and is thus the most general form of such uncertainty to date. We apply our model to the same wind turbines supply chain. We illustrate theoretical and numerical results that show the impact of supplier uncertainty/unreliability on the optimal procurement decisions. We also quantify the value of modeling uncertainty versus deterministic planning. Supplier selection with quantity discounts has been an active research problem in the operations research community. In this the last part of this thesis, we focus on a new quantity discounts scheme offered by suppliers in some industries. Suppliers are selected for a strategic planning period (e.g., 5 years). Fixed costs associated with suppliers’ selection are paid. Orders are placed monthly from any of the chosen suppliers, but the quantity discounts are based on the aggregated annual order quantities. We incorporate all this in a multi-period multi-product multi-echelon supply chain planning problem and develop a mixed integer programming (MIP) model for it. Leading commercial MIP solvers take 40 minutes on average to get any feasible solution for realistic instances of our model. With the aim of getting high-quality feasible solutions quickly, we develop an algorithm that constructs a good initial solution and three other iterative algorithms that improve this initial solution and are capable of getting very fast high quality primal solutions. Two of the latter three algorithms are based on MIP-based local search and the third algorithm incorporates a variable neighborhood Descent (VND) combining the first two. We present numerical results for a set of instances based on a real-world supply chain in the food industry and show the efficiency of our customized algorithms. The leading commercial solver CPLEX finds only a very few feasible solutions that have lower total costs than our initial solution within a three hours run time limit. All our iterative algorithms well outperform CPLEX. The VND algorithm has the best average performance. Its average relative gap to the best known feasible solution is within 1% in less than 40 minutes of computing time.