A Complex Adaptive Systems Analysis of Productive Efficiency

dc.contributor.authorDougherty, Francis Laverneen
dc.contributor.committeechairTriantis, Konstantinos P.en
dc.contributor.committeememberTaylor, G. Donen
dc.contributor.committeememberKleiner, Brian M.en
dc.contributor.committeememberWernz, Christianen
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.date.accessioned2016-04-10T06:00:18Zen
dc.date.available2016-04-10T06:00:18Zen
dc.date.issued2014-10-17en
dc.description.abstractLinkages between Complex Adaptive Systems (CAS) thinking and efficiency analysis remain in their infancy. This research associates the basic building blocks of the CAS 'flocking' metaphor with the essential building block concepts of Data Envelopment Analysis (DEA). Within a proposed framework DEA "decision-making units" (DMUs) are represented as agents in the agent-based modeling (ABM) paradigm. Guided by simple rules, agent DMUs representing business units of a larger management system, 'align' with one another to achieve mutual protection/risk reduction and 'cohere' with the most efficient DMUs among them to achieve the greatest possible efficiency in the least possible time. Analysis of the resulting patterns of behavior can provide policy insights that are both evidence-based and intuitive. This research introduces a consistent methodology that will be called here the Complex Adaptive Productive Efficiency Method (CAPEM) and employs it to bridge these domains. This research formalizes CAPEM mathematically and graphically. It then conducts experimentation employing using the resulting CAPEM simulation using data of a sample of electric power plants obtained from Rungsuriyawiboon and Stefanou (2003). Guided by rules, individual agent DMUs (power plants) representing business units of a larger management system,'align' with one another to achieve mutual protection/risk reduction and 'cohere' with the most efficient DMUs among them to achieve the greatest possible efficiency in the least possible time. Using a CAS ABM simulation, it is found that the flocking rules (alignment, cohesion and separation), taken individually and in selected combinations, increased the mean technical efficiency of the power plant population and conversely decreased the time to reach the frontier. It is found however that these effects were limited to a smaller than expected sub-set of these combinations of the flocking factors. Having been successful in finding even a limited sub-set of flocking rules that increased efficiency was sufficient to support the hypotheses and conclude that employing the flocking metaphor offers useful options to decision-makers for increasing the efficiency of management systems.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:3825en
dc.identifier.urihttp://hdl.handle.net/10919/65146en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectAgent-Based Modelingen
dc.subjectAnalysisen
dc.subjectComplex Adaptive Systemsen
dc.subjectData Envelopment Analysisen
dc.subjectFlockingen
dc.subjectManagementen
dc.subjectNetLogoen
dc.subjectProductive Efficiencyen
dc.titleA Complex Adaptive Systems Analysis of Productive Efficiencyen
dc.typeDissertationen
thesis.degree.disciplineIndustrial and Systems Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en
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