{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T18:13:34Z","timestamp":1789755214704,"version":"4.0.1"},"reference-count":46,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the General Program of Natural Science Research for Higher Education Institutions of Jiangsu Province","award":["25KJD110002"],"award-info":[{"award-number":["25KJD110002"]}]},{"name":"the Start-up Fund for New Talented Researchers of Nanjing University of Industry Technology","award":["YK22-12-03"],"award-info":[{"award-number":["YK22-12-03"]}],"id":[{"id":"https:\/\/ror.org\/00qf07q44","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Service counts are often converted into capacity or inventory decisions with independent Poisson models, although clustering and latent heterogeneity can make the counts substantially more variable. We present reinforced-count simulation (RCS) as a low-parameter, pre-deployment stress test: it asks whether a decision calibrated under independence remains adequate when type shares persist. RCS is compared with independent Poisson, negative-binomial, empirical-residual and Scarf moment-robust decisions. The empirical analysis uses two public datasets. RAND Health Insurance Experiment physician-visit counts provide a cross-sectional held-out test (20,190 observations), and five categories of New York City 311 requests provide an external temporal test (1,096 days and 26 rolling origins). In the RAND analysis at a lost-event-to-holding-cost ratio of 20, over-dispersion-aware decisions reduced held-out cost relative to Poisson by 15.0% for RCS, 15.6% for negative binomial and 17.3% for the empirical quantile; fill rate increased from 76.5% to 88.2%\u201391.1%. In the NYC analysis at a ratio of 10, the RCS mean paired cost improvement was 18.7% (95% confidence interval, 12.4%\u201325.0%) and fill rate increased from 93.3% to 96.8%. A multi-type transfer experiment showed that each calibrated model was best in its matching environment; using a mismatched model produced 6.0%\u201320.0% regret. Joint sensitivity analysis, concentration-parameter learning curves and cost-ratio perturbations identify when the diagnostic is useful and when parameter error can dominate model choice. RCS is a reproducible check on mean-based decisions when composition dependence is uncertain, not a general demand model.<\/jats:p>","DOI":"10.1371\/journal.pone.0358767","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T17:57:22Z","timestamp":1789754242000},"page":"e0358767","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reinforced-count simulation for decision calibration under over-dispersed multi-type service demand"],"prefix":"10.1371","volume":"21","author":[{"given":"Saisai","family":"Hou","sequence":"first","affiliation":[{"name":"Nanjing University of Industry Technology","place":["Nanjing, China"],"department":["Department of Public Basic Courses"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunzhi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Nanjing University of Industry Technology","place":["Nanjing, China"],"department":["Department of Public Basic Courses"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4331-3465","authenticated-orcid":true,"given":"Sen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nanjing University of Industry Technology","place":["Nanjing, China"],"department":["Department of Public Basic Courses"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Chen","sequence":"additional","affiliation":[{"name":"Nanjing Medical University","place":["Nanjing, China"],"department":["Office of Academic Affairs"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"issue":"2","key":"pone.0358767.ref001","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1214\/aoms\/1177706264","article-title":"Bayes solutions of the statistical inventory problem","volume":"30","author":"H Scarf","year":"1959","journal-title":"Ann Math Stat"},{"issue":"9","key":"pone.0358767.ref002","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1287\/mnsc.31.9.1150","article-title":"Bayes solution to dynamic inventory models under unknown demand distribution","volume":"31","author":"KS Azoury","year":"1985","journal-title":"Manag Sci"},{"issue":"5","key":"pone.0358767.ref003","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1287\/mnsc.25.5.498","article-title":"Effects of centralization on expected costs in a multi-location newsboy problem","volume":"25","author":"GD Eppen","year":"1979","journal-title":"Manag Sci"},{"issue":"4","key":"pone.0358767.ref004","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.orl.2004.08.003","article-title":"A practical inventory control policy using operational statistics","volume":"33","author":"LH Liyanage","year":"2005","journal-title":"Oper Res Lett"},{"issue":"6","key":"pone.0358767.ref005","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1287\/opre.2015.1422","article-title":"The data-driven newsvendor problem: new bounds and insights","volume":"63","author":"R Levi","year":"2015","journal-title":"Oper Res"},{"issue":"1","key":"pone.0358767.ref006","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1287\/opre.2018.1757","article-title":"The big data newsvendor: practical insights from machine learning","volume":"67","author":"GY Ban","year":"2019","journal-title":"Oper Res"},{"issue":"10","key":"pone.0358767.ref007","doi-asserted-by":"crossref","first-page":"5848","DOI":"10.1287\/mnsc.2023.4725","article-title":"How big should your data really be? 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