The Contextual Appointment Scheduling Problem

TL;DR

This paper addresses the data-driven appointment scheduling problem with uncertain job durations, proposing an integrated estimation and optimization approach that incorporates contextual information to improve decision quality.

Contribution

It introduces a novel formulation of ASP as an integrated estimation and optimization problem that leverages covariate data for more consistent and optimal appointment decisions.

Findings

Context inclusion prevents inconsistent decisions.

The proposed method improves appointment scheduling accuracy.

Numerical experiments validate the effectiveness of the approach.

Abstract

This study is concerned with the determination of optimal appointment times for a sequence of jobs with uncertain duration. We investigate the data-driven Appointment Scheduling Problem (ASP) when one has $n$ observations of $p$ features (covariates) related to the jobs as well as historical data. We formulate ASP as an Integrated Estimation and Optimization problem using a task-based loss function. We justify the use of contexts by showing that not including the them yields to inconsistent decisions, which translates to sub-optimal appointments. We validate our approach through two numerical experiments.