Dynamic resource allocation problems with uncertainties and complex work rules

The developments in information technology and telecommunication systems have created new opportunities for logistics service operators. With those ad-vanced technology, the real-time information of shipment and equipment status allows the planning and control of the resources to be more responsive to changes. On the other hand, due to the practical issues including the uncertainties of mar-ket demands and service times, complex work rules, and the interaction among different types of resources, the task of repositioning the resources over time and space is not easy. This thesis is motivated to model the resource allocation prob-lems under various situations, and provide adaptive, flexible and implementable decision support tools that leverage the capability of obtaining real-time informa-tion. Especially, we consider four dynamic resource allocation problems, which are differentiated by the type of resources and the focused practical issues. We start by studying homogenous dynamic resource allocation problem with a focus on the issues of uncertain market demands and service times. We formulate the problem as a multistage dynamic network with partially dependent random arc capacities, and propose a resource-directive decomposition method. This method decomposes the network in each stage into tree recourse problems with partially dependent random arc capacities, and those tree recourse problems can be solved by a pseudo-polynomial algorithm. The new decomposition approach demonstrates superiority to the alternatives. Second, we study a class of homogenous resource-task assignment problem with a focus on the issues of complex work rules. We formulate the problem as a dynamic stochastic decision model. We develop an adaptive labeling solution procedure that can incorporate various practical constraints and work rules. Ex-periments are conducted to evaluate the procedure’s performance and compare the stochastic and deterministic formulations. Third, we are motivated to study the drayage problem faced by Hong Kong trucking industry where not only individual resources (e.g., driver, tractor, and chassis) but also the combinations of them (e.g., the driver-tractor-chassis triplets) need to be managed simultaneously. The drayage services between a container terminal and the origin (or destination) of a shipment account for a significant portion of the total transportation cost. They are the key sources of shipment delays, road congestions, and disruptions in the international logistics network. Such a situation is even worse when the drayage services involve cross-border issues. Using Hong Kong, the busiest port in the world, as an example, we illus-trate the challenges and issues in managing drayage activities in hub cities. We show that managing cross-border drayage container transportation is a very chal-lenging problem because not only individual resources (e.g., driver, tractor, and chassis) but also the composites of them (e.g., the driver-tractor-chassis triplets) need to be managed simultaneously. The problem is further complicated by the regulatory policies which govern the cross-border activities. We use an attribute-decision model for this problem and implement an adaptive labeling algorithm to solve it. We conduct numerical experiments to evaluate the system performances under various regulatory policies. The results show that the benefit gained by relaxing the regulatory policies is significant. Forth, for a single particular resource, motivated by a project for one of the major air cargo terminals in the world, we study the problem of finding K con-strained shortest paths. We show that, no matter the exact forms of the con-straints, the problem can be decomposed into a polynomially increasing number of subproblems and each subproblem can be solved efficiently by well studied constrained shortest paths algorithms. Those solution approaches and techniques build up a tool-kit for modern lo-gistics resource management problems. If balancing our theoretical developments with real life applications, we apply our techniques to solve dynamic fleet man-agement problems, cross-border cargo handling problems, route generation for automated retrieval and storage systems, empty containers repositioning prob-lems, and etc. We compare the performance of our methods with other solution techniques. The results are very encouraging