Optimal Staffing for Online-to-Offline On-Demand Delivery Systems: In-House or Crowd-Sourcing Drivers?

Hongyan Dai (), Yali Liu (), Nina Yan () and Weihua Zhou
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Hongyan Dai: Business School, Central University of Finance and Economics, No. 39, Xueyuan Nanlu, Haidian District, Beijing 100086, P. R. China
Yali Liu: Business School, Central University of Finance and Economics, No. 39, Xueyuan Nanlu, Haidian District, Beijing 100086, P. R. China
Nina Yan: Business School, Central University of Finance and Economics, No. 39, Xueyuan Nanlu, Haidian District, Beijing 100086, P. R. China
Weihua Zhou: School of Management, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2021, vol. 38, issue 01, 1-25

Abstract: 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 — capacity-type flexibility and agent flexibility — 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.

Keywords: E-commerce; crowd-sourcing drivers; O2O; on-demand delivery; queueing models (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1142/S0217595920500372

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