Date of Graduation

2015

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Industrial and Managements Systems Engineering

Committee Chair

Rashpal S Ahluwalia

Committee Co-Chair

Rashpal Ahluwalia

Committee Member

Fei Dai

Committee Member

Vasudevan Jagannathan

Committee Member

Majid Jaridi

Committee Member

Feng Yang

Abstract

Almost all sectors of the economy, such as, government, healthcare, education, ship repair, construction, and manufacturing require project management. A key component of project management deals with scheduling of tasks such that limited resources are utilized in an effective manner. Current research on resource constrained project-scheduling has been classified as: a) Single project with single mode for various tasks, b) Single project with multiple task modes, c) Multiple projects with single task mode, and d) Multiple projects with multiple task modes.;This work extends the current multi-project, multi-mode scheduling techniques. The resources can be renewable, and non-renewable. In addition, it focuses on short term scheduling, that is, scheduling on an hourly, daily, or weekly basis. Long term scheduling assumes a stable system, that is, resources, priorities, and other constraints do no change during the scheduling period. In this research, short term scheduling assumes a dynamic system, that is, resources, priorities, and other constraints change over time.;A hybrid approach is proposed to address the dynamic nature of the problem. It is based on discrete event simulation and a set of empirical rules provided by the project manager. The project manager is assumed to be highly knowledgeable about the project. He/she is regarded as an integral part of the system. Such an approach is better suited to deal with "real world" scheduling. The proposed approach does not seek to provide a single "optimum" solution, instead, it generates a series of feasible solutions, along with the impact of each solution on schedule and cost.;Two project case studies dealing with finding an optimum solution were selected from the literature. The proposed technique was applied to the data set in these studies. In both cases the proposed approach found the "optimum" solution. The model was then applied to two additional problems to test the features that could not be tested on the dataset from the literature.;As for practical implications, the proposed approach enhances the decision making process, by providing more resource allocation flexibility, and results in improved solutions in terms of total project duration and cost. From an academic viewpoint, this research enriches the existing literature, as it provides an extension of the resource constrained project scheduling problems, a discrete event simulation and four cases studies which highlights relevant issues to model properly the complexity of real-life projects.

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