Title page for ETD etd-11202012-040328

Type of Document Master's Thesis
Author Kan, Claudia Yim-fun
URN etd-11202012-040328
Title A methodology for parsimoniously structuring a set of activities
Degree Master of Science
Department Urban and Regional Planning
Advisory Committee
Advisor Name Title
Dickey, John W. Committee Chair
Anderson, Larz T. Committee Member
Levy, John M. Committee Member
  • System theory
Date of Defense 1988-06-05
Availability restricted

In project or program planning, a Gantt or PERT chart is usually employed as a graphical representation of schedule for activities. Planners utilize this chart in performing analyses such as the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT). Very little effort, however, has been devoted to the formulation of activity networks, which is the initial step before aforementioned analyses. This research addresses this problem by developing a systematic methodology to aid in the identification and rapid structuring of a system of activities.

The theoretical foundation of the methodology is based on Interpretive Structural Modeling (ISM). It consists of seven basic steps: (l) identifying the activities in the set; (2) identifying the set of relation statements; (3)identifying the initial input; (4) establishing a transitive inference mechanism based upon previous responses; (5) generating a logical combination of relationships based on previous responses; (6) storing the relationship for each pair of activities in a relation matrix; and (7) outputting the relationships in the form of a simplified Gantt chart.

The merits of applying this methodology include (1) efficiency in activity structuring and (2) avoidance of illogical and inconsistent sequential relationship specifications. A "Business Appreciation" example is used in illustrating the application of this methodology. It reveals that 85% of a total of 120 possible sequential activity relationships can be deduced without asking for information from the user. In general, over 57% of the sequential relationships can be inferred without input by the user.

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