{"id":625,"date":"2020-10-06T18:48:53","date_gmt":"2020-10-06T22:48:53","guid":{"rendered":"https:\/\/sites.bu.edu\/met-dslab\/?page_id=625"},"modified":"2021-12-12T13:51:29","modified_gmt":"2021-12-12T18:51:29","slug":"finding-the-knee-of-a-waiting-line-hockey-stick-curve","status":"publish","type":"page","link":"https:\/\/sites.bu.edu\/met-dslab\/projects\/past_projects\/finding-the-knee-of-a-waiting-line-hockey-stick-curve\/","title":{"rendered":"Finding the Knee of a Waiting Line \u201cHockey Stick\u201d Curve"},"content":{"rendered":"<p>Researchers: John Maleyeff, Canan Gunes Corlu, and Danqi Lu<\/p>\n<hr \/>\n<p>The project concerns determining the mathematical approach that most closely resembles a human\u2019s innate ability to make decisions associated with queuing system optimization.\u00a0 The relationship between planned server utilization and customer waiting times follows a \u201chockey stick\u201d function.\u00a0 Here, the waiting time increases linearly in proportion to the server utilization until a certain threshold is reached, where the waiting times begin to increase at a higher rate until the server utilization reaches 100%.\u00a0 This threshold (called the knee or elbow of the hockey stick curve) differs depending on many factors, most importantly the number of servers, the coefficient of variation in service times, and the arrival pattern (e.g., scheduled or random).\u00a0\u00a0 In almost all cases server costs are known.\u00a0 When customer\u2019s waiting time aversion costs are also known, the system can easily be optimized.\u00a0 However, in most cases, waiting time costs are impossible to quantify.<\/p>\n<p>The project will present a series of \u201chockey stick\u201d patterns that show the relationship between server utilization and customer waiting times without context (i.e., a generic scenario).\u00a0 Their decisions will be compared with a variety of mathematical approaches that have been published or will be created.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers: John Maleyeff, Canan Gunes Corlu, and Danqi Lu The project concerns determining the mathematical approach that most closely resembles a human\u2019s innate ability to make decisions associated with queuing system optimization.\u00a0 The relationship between planned server utilization and customer waiting times follows a \u201chockey stick\u201d function.\u00a0 Here, the waiting time increases linearly in proportion [&hellip;]<\/p>\n","protected":false},"author":18030,"featured_media":0,"parent":604,"menu_order":12,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/met-dslab\/wp-json\/wp\/v2\/pages\/625"}],"collection":[{"href":"https:\/\/sites.bu.edu\/met-dslab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.bu.edu\/met-dslab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/met-dslab\/wp-json\/wp\/v2\/users\/18030"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/met-dslab\/wp-json\/wp\/v2\/comments?post=625"}],"version-history":[{"count":3,"href":"https:\/\/sites.bu.edu\/met-dslab\/wp-json\/wp\/v2\/pages\/625\/revisions"}],"predecessor-version":[{"id":628,"href":"https:\/\/sites.bu.edu\/met-dslab\/wp-json\/wp\/v2\/pages\/625\/revisions\/628"}],"up":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/met-dslab\/wp-json\/wp\/v2\/pages\/604"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/met-dslab\/wp-json\/wp\/v2\/media?parent=625"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}