Designing pricing and compensation schemes by integrating matching and routing models for crowd-shipping systems
Co-authored with Tho V.Le, Satish V.Ukkusuri and Jiawei Xue is now published in Transportation Research Part E. You can download the paper for free the coming 50 days via this link.
This paper’s objective is to identify pricing and compensation schemes under different demand and supply scenarios for crowd-shipping (CS) systems. As such, an integrated framework of matching and routing models have been developed. A routing strategy is established to estimate for distances that couriers need to travel for picking up and delivering packages. A matching model is developed to assign crowd-shipping customers (i.e. senders) to couriers and to maximize the CS platform providers’ benefits.
Four different schemes of pricing and compensation are developed and evaluated. In this study, we examine different pricing and compensation schemes, based on ‘flat’ versus ‘individual’ scheme settings. The ‘flat’ setting means that the price and compensation are the same for all request and delivery trips. The ‘individual’ setting means that the price and compensation are applied to each request and delivery trip, respectively. Consequently, four different schemes are generated from combinations of these settings.
The following figure presents an example of three sources of profits for FPFC (flat price and flat compensation) and IPIC (individual price and individual compensation) schemes, under SPL1.2DMD scenario. As can be observed, benefits of the IPIC scheme which are generated from same matches, different matches, and new matches are substantially larger than profits of the FPFC scheme. Indeed, profits of the IPIC scheme boost from the changes of FP to IP, FC to IC, and the new matches. The difference in profits of the two schemes is 186%.
CS firms are noticed to have the highest profits when apply the ‘individual’ pricing and compensation schemes. The platform provider’s profits are found more sensitive with the increase of willingness to pay (WTP) than the rise of expected to-be-paid (ETP). The insights are helpful for CS firms to attract and retain customers and couriers in the system, by setting up optimal prices and optimal compensations based on demand and supply levels as well as the firms’ expected profits and platform-users’ presuming surplus.
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