{"id":{"repo_id":"nps","oai_identifier":"oai:calhoun.nps.edu:10945/75025"},"canonical_url":"https://search.dev.ndltd.org/etd/nps/oai:calhoun.nps.edu:10945/75025","repository":{"repo_id":"nps","name":"Naval Postgraduate School","base_url":"https://calhoun.nps.edu/server/oai/request"},"display":{"title":"PREDICTING METRICS FOR NAVAL SUPPLY SYSTEMS COMMAND WHOLESALE INVENTORY OPTIMIZATION MODEL","abstract":"Naval Supply Systems Command, Weapons Systems Support relies on discrete-event simulation to generate performance metrics for candidate inventory policies (CIPs), followed by mixed-integer optimization to select the policy mix that best meets enterprise-level objectives. This approach is computationally expensive. This thesis investigates whether key performance metrics, such as expected ready for issue cost (RFIC), fill rate (FR), documents required (DOCS), demand wait time (DWT), and demand wait time for backorder (DWTBO) can be accurately predicted using machine learning models instead of repeated simulation runs. We examine two machine learning approaches: k-nearest neighbors (KNN) and Extreme Gradient Boosting (XGBoost). Both methods train very fast for more than 21,000 items and multiple CIPs by item. These observations are divided into four categories, and the results demonstrate that XGBoost consistently outperforms KNN. Neither method can predict RFIC, DWT and DWTBO with sufficient accuracy to replace the simulation. On the other hand, performance is moderate for DOCS, and very good for FR, with mean absolute errors under 1.0 documents and 0.1 (rate), respectively, across all categories.","abstract_html":"Naval Supply Systems Command, Weapons Systems Support relies on discrete-event simulation to generate performance metrics for candidate inventory policies (CIPs), followed by mixed-integer optimization to select the policy mix that best meets enterprise-level objectives. This approach is computationally expensive. This thesis investigates whether key performance metrics, such as expected ready for issue cost (RFIC), fill rate (FR), documents required (DOCS), demand wait time (DWT), and demand wait time for backorder (DWTBO) can be accurately predicted using machine learning models instead of repeated simulation runs. We examine two machine learning approaches: k-nearest neighbors (KNN) and Extreme Gradient Boosting (XGBoost). Both methods train very fast for more than 21,000 items and multiple CIPs by item. These observations are divided into four categories, and the results demonstrate that XGBoost consistently outperforms KNN. Neither method can predict RFIC, DWT and DWTBO with sufficient accuracy to replace the simulation. On the other hand, performance is moderate for DOCS, and very good for FR, with mean absolute errors under 1.0 documents and 0.1 (rate), respectively, across all categories.","abstract_has_math":false,"creators":["Patterson, Thomas B."],"institution":"Monterey, CA; Naval Postgraduate School","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Operations Research (OR)","school":null,"contributors":[],"advisors":["Salmeron-Medrano, Javier"],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-03","date_published":"2026-03","updated_at":"2026-07-27T20:25:31Z","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."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10945/75025","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Salmeron-Medrano, Javier"]},{"key":"dc:contributor.department","label":"Department","values":["Operations Research (OR)"]},{"key":"dc:creator","label":"Author","values":["Patterson, Thomas B."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-05-12T16:55:15Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-05-12T16:55:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-03"]},{"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. Copyright protection is not available for this work in the United States."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10945/75025"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Naval Supply Systems Command, Weapons Systems Support relies on discrete-event simulation to generate performance metrics for candidate inventory policies (CIPs), followed by mixed-integer optimization to select the policy mix that best meets enterprise-level objectives. This approach is computationally expensive. This thesis investigates whether key performance metrics, such as expected ready for issue cost (RFIC), fill rate (FR), documents required (DOCS), demand wait time (DWT), and demand wait time for backorder (DWTBO) can be accurately predicted using machine learning models instead of repeated simulation runs. We examine two machine learning approaches: k-nearest neighbors (KNN) and Extreme Gradient Boosting (XGBoost). Both methods train very fast for more than 21,000 items and multiple CIPs by item. These observations are divided into four categories, and the results demonstrate that XGBoost consistently outperforms KNN. Neither method can predict RFIC, DWT and DWTBO with sufficient accuracy to replace the simulation. On the other hand, performance is moderate for DOCS, and very good for FR, with mean absolute errors under 1.0 documents and 0.1 (rate), respectively, across all categories."]},{"key":"dc:title","label":"Title","values":["PREDICTING METRICS FOR NAVAL SUPPLY SYSTEMS COMMAND WHOLESALE INVENTORY OPTIMIZATION MODEL"]}]}],"canonical_facts":{"dc:contributor.advisor":["Salmeron-Medrano, Javier"],"dc:contributor.department":["Operations Research (OR)"],"dc:creator":["Patterson, Thomas B."],"dc:date.accessioned":["2026-05-12T16:55:15Z"],"dc:date.available":["2026-05-12T16:55:15Z"],"dc:date.issued":["2026-03"],"dc:description.abstract":["Naval Supply Systems Command, Weapons Systems Support relies on discrete-event simulation to generate performance metrics for candidate inventory policies (CIPs), followed by mixed-integer optimization to select the policy mix that best meets enterprise-level objectives. This approach is computationally expensive. This thesis investigates whether key performance metrics, such as expected ready for issue cost (RFIC), fill rate (FR), documents required (DOCS), demand wait time (DWT), and demand wait time for backorder (DWTBO) can be accurately predicted using machine learning models instead of repeated simulation runs. We examine two machine learning approaches: k-nearest neighbors (KNN) and Extreme Gradient Boosting (XGBoost). Both methods train very fast for more than 21,000 items and multiple CIPs by item. These observations are divided into four categories, and the results demonstrate that XGBoost consistently outperforms KNN. Neither method can predict RFIC, DWT and DWTBO with sufficient accuracy to replace the simulation. On the other hand, performance is moderate for DOCS, and very good for FR, with mean absolute errors under 1.0 documents and 0.1 (rate), respectively, across all categories."],"dc:identifier.uri":["https://hdl.handle.net/10945/75025"],"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."],"dc:title":["PREDICTING METRICS FOR NAVAL SUPPLY SYSTEMS COMMAND WHOLESALE INVENTORY OPTIMIZATION MODEL"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:25:31Z"}