Asymptotically optimal appointment schedules with customer no-shows

TL;DR

This paper develops asymptotically optimal appointment scheduling strategies considering customer no-shows, analyzing fluid and diffusion scales, and quantifying the impact of uncertainty through the stochasticity gap.

Contribution

It introduces a novel asymptotic analysis of appointment schedules in heavy traffic, providing explicit solutions and quantifying the stochasticity gap under uncertainty.

Findings

Optimal schedules achieve critical load in fluid scale.

Explicit Brownian optimization solution characterizes tradeoffs.

Stochasticity gap converges to a positive constant in diffusion scale.

Abstract

We consider the problem of scheduling appointments for a finite customer population to a service facility with customer no-shows, to minimize the sum of customer waiting time and server overtime costs. Since appointments need to be scheduled ahead of time we refer to this problem as an optimization problem rather than a dynamic control one. We study this optimization problem in fluid and diffusion scales and identify asymptotically optimal schedules in both scales. In fluid scale, we show that it is optimal to schedule appointments so that the system is in critical load; thus heavy-traffic conditions are obtained as a result of optimization rather than as an assumption. In diffusion scale, we solve this optimization problem in the large horizon limit. Our explicit stationary solution of the corresponding Brownian Optimization Problem translates the customer-delay versus server-overtime tradeoff to a tradeoff between the state of a reflected Brownian motion in the half-line and its local time at zero. Motivated by work on competitive ratios, we also consider a reference model in which an oracle provides the decision maker with the complete randomness information. The difference between the values of the scheduling problem for the two models, to which we refer as the stochasticity gap (SG), quantifies the degree to which it is harder to design a schedule under uncertainty than when the stochastic primitives (i.e., the no-shows and service times) are known in advance. In the fluid scale, the SG converges to zero, but in the diffusion scale it converges to a positive constant that we compute.