Masters Theses
Abstract
"This thesis explains a productive collaboration framework where companies compete and collaborate simultaneously in an auction based virtual marketplace. The thesis describes the control center of each company in this environment and proposes algorithms for the Scheduler and Estimator agents in this framework. A job shop scheduling heuristic algorithm for varying reward structures is proposed, which is an indexing algorithm that schedules tasks for each time unit, using dynamic allocation indices and binary integer programming. It is shown that the heuristic performs well against known reward based scheduling methods. An estimation decision system based on this scheduling algorithm is also presented that not only utilizes the Scheduler agent in task selection, but also uses concepts such as company reputation and aggressiveness. The integration of these agents result in a decision system for estimating and scheduling tasks with varying reward structures in a job-shop-like environment, which combines sound management science principles in order to emphasize analytical solutions to complicated real-life problems"--Abstract, page iii.
Advisor(s)
Grasman, Scott E. (Scott Erwin)
Saygin, Can
Committee Member(s)
Leu, M. C. (Ming-Chuan)
Department(s)
Engineering Management and Systems Engineering
Degree Name
M.S. in Engineering Management
Publisher
University of Missouri--Rolla
Publication Date
Spring 2005
Pagination
viii, 68 pages
Note about bibliography
Includes bibliographical references (pages 66-67).
Rights
© 2005 Evren Akcora, All rights reserved.
Document Type
Thesis - Restricted Access
File Type
text
Language
English
Subject Headings
Job shopsProduction scheduling -- Mathematical modelsHeuristic
Thesis Number
T 8743
Print OCLC #
62510383
Recommended Citation
Akcora, Evren, "A decision system for estimating and scheduling tasks with varying reward structures in a job shop environment" (2005). Masters Theses. 4434.
https://scholarsmine.mst.edu/masters_theses/4434
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Comments
The research was funded by the National Science Foundation through the grant DMI-0323028.