The Contextual Appointment Scheduling Problem
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 35 references that are in the map. First posted on arXiv 12 Aug 2021.
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References · 8
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
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- 2016On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimizationcited by 108
- 2015Training Deep Neural Networks via Direct Loss Minimizationcited by 25
- 2016Applying Deep Learning to the Newsvendor Problemcited by 24
- 2017Assessing the Performance of Deep Learning Algorithms for Newsvendor Problemcited by 6
- 2014Staffing Call Centers with Uncertain Arrival Rates and Co-sourcingcited by 2
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