O problema de programaÃÃo de caminhÃes à um tema de grande relevÃncia na gestÃo de frota das empresas. Estas enfrentam dificuldades em gerenciar seus veÃculos devido Ãs diversas variÃveis inerentes ao processo, tais como o tamanho ideal da frota, os diversos tipos de caminhÃes disponÃveis, a capacidade de carga do caminhÃo, as informaÃÃes tÃcnicas do cliente e o agendamento das viagens. No que se refere ao transporte de derivados de petrÃleo, existem diversas caracterÃsticas operacionais que dificultam a programaÃÃo de caminhÃes. Embora a literatura sobre a programaÃÃo de veÃculos seja vasta, as abordagens para a programaÃÃo de caminhÃes para este tipo de transporte ainda à limitada. O presente trabalho tem como objetivo desenvolver um modelo de programaÃÃo inteira para a otimizaÃÃo da programaÃÃo de veÃculos de mÃltiplos tipos para o transporte de derivados de petrÃleo para obras de construÃÃo de rodovias. Dados reais sobre uma empresa de transporte desse setor foram coletados. Foi desenvolvido um modelo que buscasse a minimizaÃÃo da frota de caminhÃes disponÃveis. Como conclusÃes, pode-se ressaltar que a metodologia empregada serviu para minimizar a frota necessÃria no perÃodo analisado. O desenvolvimento de indicadores de desempenho permitiu avaliar a qualidade das soluÃÃes geradas. / The truck scheduling problem is an important topic in the companiesâ fleet management. Many companies face difficulties to manage their vehicles due to several variables inherent to the management process, such as the optimal fleet size, multiple types of trucks available, trucks capacity, the technical information from the clientâs construction site and trips scheduling. In terms of the transportation of oil products, there are many operational characteristics that make the truck scheduling difficult. Despite the extensive literature about vehicle scheduling, truck scheduling for this type of transport is limited. The purpose of this research study was to develop an Integer Programming model to optimize the multi-type truck scheduling for the transportation of oil products for road construction sites. Data from a real company were gathered. A model that aimed to minimize the truck fleet available was developed. The method used has minimized the available fleet in the period under review. The development of key performance indicators allows to evaluate the quality of the solutions created.

This dissertation consists of two self-contained studies. The first study, in the domain of stochastic inventory theory, addresses the structure of optimal ordering policies in a periodic review setting. We take multiple sources of a single product to imply an ordering cost function that is nondecreasing, piecewise linear, and convex. Our main contribution is a proof of the optimality of a finite generalized base stock policy under an average cost criterion. Our inventory model is formulated as a Markov decision process with complete observations. Orders are delivered immediately. Excess demand is fully backlogged, and the function describing holding and backlogging costs is convex. All parameters are stationary, and the random demands are independent and identically distributed across periods. The (known) distribution function is subject to mild assumptions along with the holding and backlogging cost function. Our proof uses a vanishing discount approach. We extend our results from a continuous environment to the case where demands and order quantities are integral. The second study is in the area of capacity planning. Our overarching contribution is a relatively simple and fast solution approach for the fleet composition problem faced by a retail distribution firm, focusing on the context of a major beverage distributor. Vehicles to be included in the fleet may be of multiple sizes; we assume that spot transportation capacity will be available to supplement the fleet as needed. We aim to balance the fixed costs of the fleet against exposure to high variable costs due to reliance on spot capacity. We propose a two-stage stochastic linear programming model with fixed recourse. The demand on a particular day in the planning horizon is described by the total quantity to be delivered and the total number of customers to visit. Thus, daily demand throughout the entire planning period is captured by a bivariate probability distribution. We present an algorithm that efficiently generates a "definitive" collection of bases of the recourse program, facilitating rapid computation of the expected cost of a prospective fleet and its gradient. The equivalent convex program may then be solved by a standard gradient projection algorithm.