Date of Award

8-2008

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Industrial Engineering

Advisor

Taaffe, Kevin M.

Committee Member

Chowdhury , Ronnie A.

Committee Member

Ferrell , William G.

Committee Member

Kurz , Mary Elizabeth

Abstract

This dissertation focuses on the resource requirements and scheduling problem for logistic systems. We investigate solutions to this problem in two different logistic systems: logistic system of the health care facilities during emergency evacuations and delivery and distribution system of production industries. All hospitals must have an evacuation plan to ensure the safety of patients and prevent the loss of life. However, hospital operators have not been able to quantify how resource availability, the cost of acquiring those resources, and evacuation completion time are related. This research addresses this problem and contributes two methodologies to solve this problem. In the first methodology, we propose a mixed integer programming for identifying resource requirements, as well as the scheduling of these requirements, within a pre-specified period while minimizing cost. Also, we suggest a tailored solution approach that relaxes certain complicating integer constraints in an effort to find feasible, quality solutions. This model assumes that there exists no probabilistic event in the evacuation process of the hospitals. The second proposed methodology accounts for uncertainties in the evacuation process. We present a stochastic model via simulation and employ a simulation-optimization approach to solve the same problem. The resource requirements and scheduling problem is also a critical issue for the companies in the production industry as most of them have limited resources and need to make their tactical and operational plans with the consideration of this issue. As the focus of this dissertation is logistic operations, we consider this problem only for the logistic system of these companies and contribute methodologies to solve this problem. With the use of same structure of the formulation, used in mixed integer programming proposed for the evacuation problem, we propose models to solve this problem for two different production environments: when there is no limitation on resources and when there are limited resources. The models proposed for the restricted production environment also enables the companies to select the most profitable set of customers. We also suggest tailored solution approaches for each model with the use of the same techniques used for evacuation planning problem.

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