# The Contextual Appointment Scheduling Problem

arXiv: https://arxiv.org/abs/2108.05531 · HTML: https://mlsanity.com/paper/2108.05531/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [From Predictive to Prescriptive Analytics](https://mlsanity.com/paper/1402.5481/) (2014)
- [On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization](https://mlsanity.com/paper/1607.05447/) (2016)
- [Training Deep Neural Networks via Direct Loss Minimization](https://mlsanity.com/paper/1511.06411/) (2015)
- [Applying Deep Learning to the Newsvendor Problem](https://mlsanity.com/paper/1607.02177/) (2016)
- [Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem](https://mlsanity.com/paper/1706.02899/) (2017)
- [Staffing Call Centers with Uncertain Arrival Rates and Co-sourcing](https://mlsanity.com/paper/1404.2938/) (2014)

## Abstract (arXiv)

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.