{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T05:34:12Z","timestamp":1738128852654,"version":"3.33.0"},"reference-count":24,"publisher":"SAGE Publications","issue":"11","license":[{"start":{"date-parts":[[2002,11,1]],"date-time":"2002-11-01T00:00:00Z","timestamp":1036108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIMULATION"],"published-print":{"date-parts":[[2002,11]]},"abstract":"<jats:p> A sequential stopping procedure should collect enough steady-state data to overwhelm the influence of initial transient bias without requiring initial data truncation. The initial transient negatively affects the efficiency of the sequential procedure, but from a practical point of view, eliminating the difficulty of determining the data truncation point can lead to a more easily implemented algorithm for determining the appropriate length of a simulation run. A sequential stopping rule is presented that uses a time-series forecasting procedure to determine appropriate trade-offs between the efficiency and simplicity of the estimate of cycle time for a relevant constant mean process. Results show that the proposed sequential stopping rule terminates a simulation output process at a point when a stable estimate is obtained. Furthermore, the rule performs as well as the crossings-of-means data truncation technique yet is easier to implement. <\/jats:p>","DOI":"10.1177\/0037549702078011001","type":"journal-article","created":{"date-parts":[[2003,6,13]],"date-time":"2003-06-13T00:17:59Z","timestamp":1055463479000},"page":"643-654","source":"Crossref","is-referenced-by-count":2,"title":["A Sequential Stopping Rule for a Steady-State Simulation Based on Time-Series Forecasting"],"prefix":"10.1177","volume":"78","author":[{"given":"Gerald T.","family":"Mackulak","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Arizona State University, Tempe, AZ 85287-5906,"}]},{"given":"Sungmin","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Arizona State University, Tempe, AZ 85287-5906"}]},{"given":"John W.","family":"Fowler","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Arizona State University, Tempe, AZ 85287-5906"}]},{"given":"Sonia E.","family":"Leach","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Arizona State University, Tempe, AZ 85287-5906"}]},{"given":"J. Bert","family":"Keats","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Arizona State University, Tempe, AZ 85287-5906"}]}],"member":"179","published-online":{"date-parts":[[2002,11,1]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.20.4.785"},{"key":"atypb2","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316757"},{"key":"atypb3","doi-asserted-by":"publisher","DOI":"10.1287\/opre.27.5.1011"},{"key":"atypb4","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.28.5.550"},{"key":"atypb5","doi-asserted-by":"publisher","DOI":"10.1287\/opre.31.6.983"},{"key":"atypb6","doi-asserted-by":"publisher","DOI":"10.1002\/nav.3800240414"},{"key":"atypb7","doi-asserted-by":"publisher","DOI":"10.1002\/nav.3800250312"},{"key":"atypb8","volume-title":"Simulation modeling and analysis","author":"Law, A. 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