{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T16:44:12Z","timestamp":1769100252095,"version":"3.49.0"},"reference-count":18,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Asia Pac. J. Oper. Res."],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:p> Online-to-offline (O2O) on-demand services require one-hour delivery and the demands vary substantially within one day. The capacity plans in the O2O industry evolve into three main modes: (i) in-house drivers only; (ii) full-time and part-time crowd-sourcing drivers; (iii) a mix of in-house and crowd-sourcing drivers. For current capacity plans, two issues remain unclear for both academia and industry. First, what is the optimal staffing decision when considering the behaviors of crowd-sourcing drivers. Second, how to choose from different capacity plans to match different operation strategies and market environments. To address these questions, we build an M\/M\/n queueing model to optimize the staffing decision with the aim of minimizing the total operation costs. Incentive mechanisms for both customers and crowd-sourcing drivers are crafted to improve their loyalty towards the O2O platform, in order to better manage capacity. Moreover, we apply a real dataset from one of the largest O2O platforms in China to verify our model. Our analyses show that adding flexibility \u2014 capacity-type flexibility and agent flexibility \u2014 to the O2O on-demand logistics system can help control costs and maintain a high service level. Furthermore, conditions in which different capacity plans match with different operation strategies and market environments are proposed. <\/jats:p>","DOI":"10.1142\/s0217595920500372","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T08:48:00Z","timestamp":1594111680000},"page":"2050037","source":"Crossref","is-referenced-by-count":6,"title":["Optimal Staffing for Online-to-Offline On-Demand Delivery Systems: In-House or Crowd-Sourcing Drivers?"],"prefix":"10.1142","volume":"38","author":[{"given":"Hongyan","family":"Dai","sequence":"first","affiliation":[{"name":"Business School, Central University of Finance and Economics, No. 39, Xueyuan Nanlu, Haidian District, Beijing 100086, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yali","family":"Liu","sequence":"additional","affiliation":[{"name":"Business School, Central University of Finance and Economics, No. 39, Xueyuan Nanlu, Haidian District, Beijing 100086, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nina","family":"Yan","sequence":"additional","affiliation":[{"name":"Business School, Central University of Finance and Economics, No. 39, Xueyuan Nanlu, Haidian District, Beijing 100086, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihua","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Management, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, P. R. 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