Nesta dissertação abordamos o problema de dimensionamento e sequenciamento de lotes com aceitação de pedidos. As demandas dos clientes são agregadas em pedidos, os quais podem ou não ser aceitos e devem ser entregues dentro de uma janela de tempo. Os itens são perecíveis e podem permanecer no estoque somente por um tempo determinado (shelf-life). O objetivo do problema é maximizar a receita gerada pelo atendimento dos pedidos, descontando os custos de estoque e das preparações da máquina. Para tratar o problema são propostas formulações matemáticas e abordagens heurísticas contendo uma etapa construtiva seguida por uma heurística de melhoramento. Testes computacionais foram realizados e os resultados obtidos foram analisados. As heurísticas obtiveram desempenho superior ao branch-and-cut do solver de otimização na obtenção de soluções de boa qualidade, no limite de tempo estabelecido. / In this dissertation, we approach the lot sizing and scheduling problem with order acceptance. Customers demands are aggregated into orders, which may or may not be accepted and must be delivered within a time window. The items are perishable and can remain in inventory only for a limited time (shelf-life). The aim of the problem is profit maximizing generated by orders acceptance, discounting inventory and machine setups costs. To deal with this problem math formulations, constructive and improvement heuristics were proposed. Computational tests were performed and the results obtained were analyzed. The heuristics obtained superior performance then branch-and-cut of the optimization solver obtaining good quality solutions within the established time limit.

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.