This book focuses on real time management of distribution systems, integrating the latest results in system design, algorithm development and system implementation to capture the state-of-the art research and application trends. The book important topics such as goods dispatching, couriers, rescue and repair services, taxi cab services, and more. The book includes real-life case studies that describe the solution to actual distribution problems by combining systemic and algorithmic approaches.

Two new dynamic planning approaches, incorporating all important real-life restrictions, such as regulations on driving and working hours, are developed and evaluated. Extensive numerical tests are carried out with a five-week real-life data set from an international freight forwarding company.

Esta dissertação tem como objetivo contribuir para o gerenciamento de frotas de grande porte, buscando uma maior rapidez e eficiência na distribuição de veículos ao longo do tempo/espaço, visando maximizar o lucro total da empresa. Problemas de gerenciamento de frotas dinâmicas são normalmente formulados como uma rede dinâmica, mas há uma grande dificuldade ao se trabalhar com problemas desse tipo, especialmente quando se busca uma solução sobre umhorizonte de planejamento longo. Visando contornar essa dificuldade, Powell & Carvalho (1998) desenvolveram uma nova abordagem para problemas desse tipo: a Logistics Queuing Network (LQN). A utilização do algoritmo LQN na prática (através de um software) permitiria uma tomada de decisão mais rápida e eficiente, sendo bastante útil, em especial para empresas de transportes. Assim, implementou-se o algoritmo LQN, através do desenvolvimento de um software para o gerenciamento de frotas de grande porte, por meio do qual podeseconstatar o potencial de aplicação desse algoritmo.

A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.

This book combines wireless telematics systems with dynamic vehicle routing algorithms and vehicle-positioning systems to produce a telematics-enabled information system that can be employed by commercial fleet operators for real-time monitoring, control, and planning. The book further presents a Messaging And Fleet Monitoring System and a Dynamic Planning System (DPS) that provides real-time decision support considering the current state of the transportation system.

This book deals with transportation processes denoted as the Real-time Distribution of Perishable Goods (RDOPG). The book presents three contributions that are made to the field of transportation. First, a model considering the minimization of customer inconvenience is formulated. Second, a pro-active real-time control approach is proposed. Stochastic knowledge is generated from past request information by a new forecasting approach and is used in the pro-active approach to guide vehicles to request-likely areas before real requests arrive there. Various computational results are presented to show that in many cases the pro-active approach is able to achieve significantly improved results. Moreover, a measure for determining the structural quality of request data sets is also proposed. The third contribution of this book is a method that is presented for considering driver inconvenience aspects which arise from vehicle en-route diversion activities. Specifically, this method makes it possible to restrict the number of performed vehicle en-route diversion activities.​