{"id":{"repo_id":"nps","oai_identifier":"oai:calhoun.nps.edu:10945/4985"},"canonical_url":"https://search.dev.ndltd.org/etd/nps/oai:calhoun.nps.edu:10945/4985","repository":{"repo_id":"nps","name":"Naval Postgraduate School","base_url":"https://calhoun.nps.edu/server/oai/request"},"display":{"title":"The allocation of automated test equipment capacity with variability in demand and processing rates","abstract":"The purpose of this thesis is to develop a model for allocating the Consolidated Automated Support System (CASS) to the intermediate repair sites. The model uses integer, linear, and nonlinear programming (optimization) to determine the approximate number of CASS stations at a site based on demand, operational availability of the aircraft at the site, budget, and utilization of the CASS stations. The model can be used as a decision tool by NAVAIR PMA 260 to allocate CASS stations to that site. Monte Carlo simulation with Crystal Ball is used to examine the impact of variability on the current and the proposed solution. Determining the number of CASS at a site affects the number of spare parts and the operational availability, and in turn will affect the budget of PMA 260. In this thesis, we develop a decision support tool to assist PMA 260 in making these CASS allocation decisions. Moreover, the most significant contributions are the proof of concept that variable and peak demand can be incorporated into capacity planning (beyond planning for average demand) and linking predicted congestion to operational availability of aircraft (readiness).","abstract_html":"The purpose of this thesis is to develop a model for allocating the Consolidated Automated Support System (CASS) to the intermediate repair sites. The model uses integer, linear, and nonlinear programming (optimization) to determine the approximate number of CASS stations at a site based on demand, operational availability of the aircraft at the site, budget, and utilization of the CASS stations. The model can be used as a decision tool by NAVAIR PMA 260 to allocate CASS stations to that site. Monte Carlo simulation with Crystal Ball is used to examine the impact of variability on the current and the proposed solution. Determining the number of CASS at a site affects the number of spare parts and the operational availability, and in turn will affect the budget of PMA 260. In this thesis, we develop a decision support tool to assist PMA 260 in making these CASS allocation decisions. Moreover, the most significant contributions are the proof of concept that variable and peak demand can be incorporated into capacity planning (beyond planning for average demand) and linking predicted congestion to operational availability of aircraft (readiness).","abstract_has_math":false,"creators":["Beckham, Jeremy M.","Akturk, Mustafa"],"institution":"Monterey, CA; Naval Postgraduate School","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Doerr, Kenneth","Myung, Noah"],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-12","date_published":"2010-12","updated_at":"2026-07-27T20:26:16Z","subjects":[],"languages":[],"rights":["This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.","Copyright is reserved by the copyright owner."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10945/4985","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Doerr, Kenneth","Myung, Noah"]},{"key":"dc:creator","label":"Author","values":["Beckham, Jeremy M.","Akturk, Mustafa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2012-03-14T17:43:48Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2012-03-14T17:43:48Z"]},{"key":"dc:date.issued","label":"Date","values":["2010-12"]},{"key":"dc:publisher","label":"Institution","values":["Monterey, CA; Naval Postgraduate School"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. 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Monte Carlo simulation with Crystal Ball is used to examine the impact of variability on the current and the proposed solution. Determining the number of CASS at a site affects the number of spare parts and the operational availability, and in turn will affect the budget of PMA 260. In this thesis, we develop a decision support tool to assist PMA 260 in making these CASS allocation decisions. Moreover, the most significant contributions are the proof of concept that variable and peak demand can be incorporated into capacity planning (beyond planning for average demand) and linking predicted congestion to operational availability of aircraft (readiness)."]},{"key":"dc:title","label":"Title","values":["The allocation of automated test equipment capacity with variability in demand and processing rates"]}]}],"canonical_facts":{"dc:contributor.advisor":["Doerr, Kenneth","Myung, Noah"],"dc:creator":["Beckham, Jeremy M.","Akturk, Mustafa"],"dc:date.accessioned":["2012-03-14T17:43:48Z"],"dc:date.available":["2012-03-14T17:43:48Z"],"dc:date.issued":["2010-12"],"dc:description.abstract":["The purpose of this thesis is to develop a model for allocating the Consolidated Automated Support System (CASS) to the intermediate repair sites. The model uses integer, linear, and nonlinear programming (optimization) to determine the approximate number of CASS stations at a site based on demand, operational availability of the aircraft at the site, budget, and utilization of the CASS stations. The model can be used as a decision tool by NAVAIR PMA 260 to allocate CASS stations to that site. Monte Carlo simulation with Crystal Ball is used to examine the impact of variability on the current and the proposed solution. Determining the number of CASS at a site affects the number of spare parts and the operational availability, and in turn will affect the budget of PMA 260. In this thesis, we develop a decision support tool to assist PMA 260 in making these CASS allocation decisions. Moreover, the most significant contributions are the proof of concept that variable and peak demand can be incorporated into capacity planning (beyond planning for average demand) and linking predicted congestion to operational availability of aircraft (readiness)."],"dc:identifier.uri":["https://hdl.handle.net/10945/4985"],"dc:publisher":["Monterey, CA; Naval Postgraduate School"],"dc:rights":["This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.","Copyright is reserved by the copyright owner."],"dc:title":["The allocation of automated test equipment capacity with variability in demand and processing rates"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:26:16Z"}