A load plan specifies how freight is routed through a linehaul terminal network operated by a less-than-truckload (LTL) carrier. Determining the design of the load plan is critical to effective operations of such carriers. This dissertation makes contributions in modeling and algorithm design for three problems in LTL load plan design: (1) Refined execution cost estimation. Existing load plan design models use approximations that ignore important facts such as the nonlinearity of transportation costs with respect to the number of trailers, and empty travel beyond what is required for trailer balance that results from driver rules. We develop models that more accurately capture key operations of LTL carriers and produce accurate operational execution costs estimates; (2) Dynamic load planning. Load plans are traditionally revised infrequently by LTL carriers due to the difficulty of solving the associated optimization problem. Technological advances have now enabled carriers to consider daily load plan updates. We develop technologies that efficiently and effectively adjust a nominal load plan for a given day based on the actual freight to be served by the carrier. We present an integer programming based local search procedure, and a greedy randomized adaptive search heuristic; and (3) Stochastic load plan design. Load plan design models commonly represent origin-destination freight volumes using average demands, which do not describe freight volume fluctuations. We investigate load plan design models that explicitly utilize information on freight volume uncertainty and design load plans that most cost-effectively deal with varying freight volumes and lead to the lowest expected cost. We present a Sample Average Approximation approach and a variant of the method for solving the stochastic integer programming formulations.

In this thesis, we investigate the potential of improving demand management activities in the global supply chains. In the increasingly global world, commerce is becoming more complex with an incredible amount of internal and external information available for businesses to select, analyze, understand and react. We identify opportunities for companies to convert data and business information into actionable intelligence. We first study the logistics industry with real data. In the Less-than-Truckload (LTL) market, we analyze an extensive historical shipment database to identify important factors to estimate LTL market rates. Quantifying critical expert knowledge, we develop a price estimation model to help shippers reduce their logistics cost and carriers to better manage their demand. In our second study, we analyze a global supply chain in the high tech industry. Using the demand dependency structure of certain products, we identify collaboration opportunities in the ordering practices that results in increased forecast accuracy. In our third study, we focus on using historical product adoption patterns for developing good pre-launch forecasts for new product introductions. Through a normalization approach and algebraic estimation procedures that use intuitive parameters, our models provide opportunities to significantly improve pre-launch forecast accuracy. Finally, in our fourth study, we develop novel approaches for modeling and mitigating the impact of demand seasonality in new product diffusion context. Focusing mainly on practical applications, our research shows that companies can find innovative ways for turning raw data into valuable insights leading to better demand management activities.

O presente estudo trata da configuração de uma rede de distribuição capacitada com restrição de cobertura. O objetivo é determinar quais cidades, dentre um conjunto de candidatas, devem atuar como centrais de desconsolidação de carga, de forma a minimizar o custo total de transporte (transferência e distribuição) para uma determinada demanda, atendendo às restrições operacionais e de distância de cobertura. A partir da pesquisa na literatura sobre o assunto, foi preparado um modelo de programação linear inteira para encontrar a solução ótima para o problema. Esse modelo é baseado nos clássicos problemas de localização, com modificação na função objetivo para retratar melhor a estrutura de custos de transporte, além da inclusão de restrições de cobertura e restrições de atendimento mínimas e máximas em cada central. O modelo foi implementado utilizando o suplemento Solver da planilha eletrônica Excel. Um outro enfoque de solução baseado na metaheurística Busca Tabu (Tabu Search) foi elaborado, com dois objetivos: permitir a análise de problemas quando não se tem disponível uma ferramenta para solução de modelos de programação linear; e analisar o comportamento da metaheurística quando utilizada na solução desse tipo de problema. O procedimento foi implementado a partir da construção de macros em linguagem Visual Basic for Application (VBA), também em Excel. O modelo de programação linear e a metaheurística Busca Tabu foram aplicados a alguns cenários de um problema real. Resultados, comparações e conclusões dessas aplicações são apresentados neste trabalho. / The present study deals with configuring a capacitated distribution network with coverage constraint. The objective consists of determining which cities, among a set of candidates, should act as load deconsolidation centers, aiming to minimize transportation total costs to attend a given demand, and obeying all operational constraints and coverage distances. Based on a literature review, an integer linear programming model was formulated to find the problem optimal solution. The model is based on classical location problems, but includes changes in the objective function to incorporate the transportation costs structure, besides coverage constraints and minimum and maximum central capacity constraints. The model was implemented using Excel’s Solver add-in. Another solution approach based on the Tabu Search metaheuristic was proposed, with two objectives: to permit problem analysis when linear programming tools are not available; and to learn on metaheuristic behavior when used to solve this type of problem. The Tabu Search procedure was implemented using Excel macro language in Visual Basic for Applications (VBA). Both integer linear programming and metaheuristic models were applied to some scenarios of a real-world problem. Applications results, comparisons and conclusions are presented in this work.