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2017 | OriginalPaper | Buchkapitel

8. Advance Patient Appointment Scheduling

verfasst von : Antoine Sauré, Martin L. Puterman

Erschienen in: Markov Decision Processes in Practice

Verlag: Springer International Publishing

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Abstract

This chapter describes the use of the linear programming approach to approximate dynamic programming as a means of solving advance patient appointment scheduling problems, which are problems typically intractable using standard solution techniques. Starting from the linear programming approach to discounted infinite-horizon Markov decision processes, and employing an affine value function approximation in the state variables, the method described in this chapter provides a systematic way of identifying effective booking guidelines for advance patient appointment scheduling problems. Two applications found in the literature allow us to show how these guidelines could be used in practice to significantly increase service levels for medical appointments, measured as the percentage of patients booked within medically acceptable wait times, and thus to decrease the potential impact of delays on patients’ health.

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Fußnoten
1
A basis function is a mapping S to \(\mathbb{R}\).
 
2
A proof of the form of the optimal affine value function approximation for a simpler version of the advance patient appointment scheduling problem studied in this chapter can be found in [21].
 
3
This is equivalent to assuming \(l_{i} = 1\ \forall i\), \(r_{i1} = 1\ \forall i\) and \(y_{m} = 0\ \forall m\neq 1\).
 
4
Poisson distributions are truncated at three times their mean values to maintain a finite state space.
 
5
This is equivalent to imposing \(\sum _{n=1}^{N}x_{ in} = w_{i}\ \forall i \) in the definition of the action sets.
 
6
A maximum number of requests, obtained from historical data, is set to maintain a finite state space.
 
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Metadaten
Titel
Advance Patient Appointment Scheduling
verfasst von
Antoine Sauré
Martin L. Puterman
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-47766-4_8

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