Hybrid approaches to solve dynamic fleet management problems

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Kim, Yŏng-jin

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The growing demand for customer-responsive, made-to-order manufacturing is stimulating the need for improved dynamic decision-making processes in commercial fleet operations. Moreover, the rapid growth of electronic commerce through the Internet is also requiring advanced and precise real-time operation of vehicle fleets. Accompanying these demand side developments/pressures, the growing availability of technologies such as AVL (Automatic Vehicle Location) systems and continuous two-way communication devices is driving developments on the supply side. These technologies enable the dispatcher to identify the current location of trucks and to communicate with drivers in real time affording the carrier fleet dispatcher the opportunity to dynamically respond to changes in demand, driver and vehicle availability, as well as traffic network conditions. This research investigates key aspects of real-time dynamic routing and scheduling problems in fleet operation particularly in a truckload pickup-and- delivery problem under various settings, in which information of stochastic demands is revealed on a continuous basis, i.e., as the scheduled routes are executed. The most promising solution strategies for dealing with this real-time problem are analyzed and integrated. Furthermore, this research develops, analyzes, and implements hybrid algorithms for solving them, which combine fast local heuristic approach with an optimization-based approach. Simulation experiments are developed and conducted to evaluate the performance of these algorithms.