In-house Driver Research Articles

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

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.

Dispatch optimisation in O2O on-demand service with crowd-sourced and in-house drivers

O2O (Online to Offline) services enable customers to place orders online and receive products/services offline. In addition to traditional in-house drivers, the emergence of crowd-sourced drivers provides an opportunity to re-organise offline delivery services. In practice, three types of workforce, namely, in-house, full-time, and part-time crowd-sourced drivers, coexist in the system while exhibiting different characteristics. This situation creates challenges for the management of order assignment and routing. In particular, we study three settings in response to different driver preferences: the guaranteed minimum daily number of orders for full-time drivers; the maximally allowed number of orders per trip; and the detour proportion for part-time drivers. This paper aims to provide a method for O2O platforms to optimise order assignment and routing, considering these designs about driver preferences. We further validate our model and study managerial insights using real datasets. Specifically, the results show that among all designed parameters for the O2O on-demand delivery system, two parameters – the maximally allowed number of orders per trip and the detour proportion – are critical for the design. Moreover, we find that incentive mechanisms for inexperienced and experienced drivers are different because of their service capacities. The managerial insights are expected to guide practitioners.

Workforce planning for O2O delivery systems with crowdsourced drivers

This paper proposes a workforce capacity planning model for online-to-offline (O2O) logistics systems. Three types of workforces with different compensation schemes are considered: in-house drivers, full-time crowdsourced drivers, and part-time crowdsourced drivers. We propose a cost minimization problem to determine the optimal workforce capacity and optimal order allocations, considering the dynamics of incoming demand. We apply a dataset from an O2O platform and our analysis reveals that (1) the capacity plan priority is part-time crowdsourced drivers, followed by full-time crowdsourced drivers and in-house drivers; the order assignment priority is reverse; (2) setting a proper guaranteed minimum order level and using the single service mode for full-time crowdsourced drivers can significantly reduce the rate of unfulfilled orders and total cost; and (3) leveraging the flexibility of the part-time crowdsourced drivers can significantly reduce the unfulfilled orders and total cost. Moreover, customizing the design of these schemes further enhances their potential. We expect these results to shed light on cost control and provide a model for crowd-sourcing which can improve the efficiency of O2O on-demand businesses.

O2O on-demand delivery optimization with mixed driver forces

O2O (Online to Offline) service enables customers to place orders online and receive products/services offline. In addition to traditional in-house drivers, the emergence of crowd-sourced drivers provides an opportunity to re-organize the offline delivery. In practice, three types of workforce, namely, in-house drivers, full-time and part-time crowd-sourced drivers, coexist in the system with different characteristics. This posts challenges to the management of order assignment and routing by the online platform. In particular, we study the impact of the detour flexibility for part-time crowd-sourced drivers, which affects the participation rate of this type of workforce. This paper aims to provide a systematic method for the O2O platforms to optimize order assignment and routing. We further validate our model and study the managerial insights using real datasets from one of the mainstream O2O platforms in China.

Capacity Planning for O2O On-Demand Delivery Systems with Crowd-Sourcing

The online-to-offline (O2O) e-commerce business model enables customers to place orders online and receive products/services offline. The key element for converting online traffic to offline products/services is a highly efficient logistics system. Compared with conventional e-commerce, the O2O logistics network is characterized by highly time-sensitive, fluctuating and large-volume fulfillments. It is therefore necessary to leverage crowd-sourcing to deliver on-demand fulfillments. Although its drivers are flexible, crowd-sourcing brings supply fluctuation and issues with incentives to capacity management. Therefore, managing the capacity plan for an O2O on-demand system with crowd-sourcing is challenging. Because this business model is emerging, there is lack of both academic and operational research on O2O capacity management. Our research builds analytical models to determine the optimal capacity and staffing plan to minimize total logistics costs. We apply a data set from one of the largest O2O platforms in China to demonstrate our model. Our analysis shows that (i) the capacity plan priority is first part-time crowd-sourced drivers, then full-time crowd-sourced drivers, and finally in-house drivers; while the order assignment priority reverses; (ii) setting a proper guaranteed minimum order level and using single service mode for full-time crowd-sourced drivers can significantly reduce the unfulfilled rate and total cost. Moreover, customizing the design of these schemes further enhances their potential. We expect these results to shed light on cost control and provide a model for crowd-sourcing which can improve the efficiency of O2O on-demand businesses.