The Contextual Appointment Scheduling Problem
This work addresses scheduling inefficiencies for operations managers, but it appears incremental as it builds on existing data-driven optimization methods.
The study tackled the appointment scheduling problem with uncertain job durations by using contextual data to optimize appointment times, showing that ignoring contexts leads to inconsistent and sub-optimal decisions.
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.