Coordinating Supply and Demand on an On-Demand Service Platform with Impatient Customers

Abstract

We consider an on-demand service platform using earning sensitive independent providers with heterogeneous reservation price (for work participation) to serve its time and price sensitive customers with heterogeneous valuation of the service. As such, both the supply and demand are "endogenously" dependent on the price the platform charges its customers and the wage the platform pays its independent providers. We present an analytical model with endogenous supply (number of participating agents) and endogenous demand (customer request rate) to study this on-demand service platform. To coordinate endogenous demand with endogenous supply, we include the steady-state waiting time performance based on a queueing model in the customer utility function to characterize the optimal price and wage rates that maximize the profit of the platform. We first analyze a base model that uses a fixed payout ratio (i.e., the ratio of wage over price), and then extend our model to allow the platform to adopt a time-based payout ratio. We find that it is optimal for the platform to charge a higher price when demand increases; however, the optimal price is not necessarily monotonic when the provider capacity or the waiting cost increases. Furthermore, the platform should offer a higher payout ratio as demand increases, capacity decreases or customers become more sensitive to waiting time.We also find that the platform should lower its payout ratio as it grows with the number of providers and customer demand increasing at about the same rate. We use a set of actual data from a large on-demand ride-hailing platform to calibrate our model parameters in numerical experiments to illustrate some of our main insights.

Regulating TNCs: Should Uber and Lyft set their own rules?

“…As with Proposition 2, we can show that the constraint set (20) is non-empty if F p αt w (N 0 ) + βp c <…”

“…This introduces an additional decision variable N . It can be solved via numerical computation as in [20]. Similarly to Proposition 2, we can show that (16) has at least one non-trivial solution if F p (αt w (N 0 )) < 1.…”

“…It also suggests that when some periods have predictably higher demand than others (e.g., a rainy evening), with static pricing it is hard to find service at peak demand times, so surge pricing is likely to benefit all stakeholders. In the same vein, [20] suggests dynamic pricing for the platform to maximize the profit across different time periods when the underlying operating characteristics change significantly. It is shown in [21] that platform pricing can be more complicated when there is uncertainty in passenger's valuation or driver's opportunity cost.…”

Pricing and capacity allocation strategies: Implications for manufacturers with product sharing

“…Furthermore, Taylor () examines the precommitted price and wage based on consumer demand and other operating factors in the context of on‐demand services. Complementing Taylor's work, Bai, So, Tang, Chen, and Wang () focus on the impact of the demand rate, sensitivity to waiting time, service rate, and the size of available providers on the optimal price, wage and payout ratio. This perspective is mainly studied in the context of on‐demand services based on the two‐sided market theory, which differs from our setting of product sharing.…”

Intermediated surge pricing

“…Recent work in operations management investigates static and dynamic contracts in intermediated markets. See Banerjee, Johari, and Riquelme (2015), Cachon, Daniels, and Lobel (2017), and Bai, So, Tang, Chen, and Wang (2018).…”