Models for project management
Name
45494057-MIT.pdf
Description
Full printable version
Size
9.12 MB
Format
Adobe PDF
Checksum (MD5)
fd6da34202ac4a962f877e697a2bbfce
Author(s)
Messmacher, Eduardo B. (Eduardo Bernhart), 1972-
Advisor(s)
Donald Rosenfield.
Date Issued
2000
Publisher
Massachusetts Institute of Technology
Abstract
Organizations perform work essentially through operations and projects. The characteristics of projects makes them extremely difficult to manage: their non repetitive nature discards the trial and error learning, while their short life span is particularly unforgiving to misjudgments. Some authors have found that effective scheduling is an important contributor to the success of research and development (R&D), as well as construction projects. The widely used critical path method for scheduling projects and identifying important activities fails to capture two important dimensions of the problem: the availability of different technologies (or options) to perform the activities, and the inherent problem of limited availability of resources that most managers face. Nevertheless, when one tries to account for such additional constraints, the problems become very hard to solve. In this thesis we propose an approach to the scheduling problem using a genetic algorithm, and try to compare its performance to more traditional approaches, such as an extension to a very innovative Lagrangian relaxation approach recently proposed. The purpose of using genetic algorithms is twofold: first to obtain good approximations to very hard problems, and second to realize the limitations and virtues of this search technique. The purpose of this thesis is not only to develop the algorithms, but also to obtain insight about the implications of the additional constraints in the perspective of a project manager.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2000.
Also available online at the DSpace at MIT website.
Includes bibliographical references (p. 119-122).
Subjects
Operations Research Center.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
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M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
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