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[pt] Este trabalho apresenta uma proposta de aceleração da decomposição de Benders aplicada a uma versão mais geral e compacta (menos restrições e variáveis) do modelo de gestão de estoques, otimizado via programação estocástica de dois estágios que considera uma camada, um item, demanda incerta e política de controle (R, S). De maneira a ser possível considerar problemas de grande porte, foram aplicados os métodos L-Shaped tradicional com corte único e a sua forma estendida com múltiplos cortes. Resultados computacionais preliminares mostraram um substancial melhor desempenho computacional do método L-Shaped tradicional em relação à sua forma multi-cut L-Shaped, mesmo o primeiro necessitando de mais iterações para convergir na solução ótima. Tal observação motivou o desenvolvimento de uma nova técnica de aceleração da decomposição de Benders e de um conjunto de desigualdades válidas. Experimentos numéricos mostram que a abordagem proposta de combinar a técnica de aceleração elaborada com as desigualdades válidas desenvolvidas provê significativa redução do tempo computacional necessário para a solução de instâncias de grande porte. / [en] This dissertation presents a speed up proposal for the Benders decomposition applied to a more general and compact version (less constraints and variables) of inventory management model, optimized via two-stage stochastic programming, which considers one layer, one item, uncertain demand and control policy (R, S). In order to be possible to consider large scale problems, the L-Shaped traditional method with single cuts and its extended form with multiple cuts were applied. Preliminary computational results showed a substantially better computational performance of the traditional L-Shaped method in comparison to the multi-cut L-Shaped method, even with the first requiring more iterations to converge on optimum solutions. This observation led to the development of a new technique to accelerate the decomposition of Benders and a set of valid inequalities. Numerical experiments show that the proposed approach of combining the elaborate acceleration technique with the developed valid inequalities, provide significant reduction in the computational time required to solve large scale instances.

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.