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A policy approximation of a Markov decision process for scheduling clients in an outpatient mental healthcare clinic.

Vreeken, M.S. (2021) A policy approximation of a Markov decision process for scheduling clients in an outpatient mental healthcare clinic.

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Abstract:The main issues within the department of mood and anxiety issues of Mediant are long waiting times for clients and a high experienced workload for practitioners. A method to schedule clients is developed. The goal is to reduce both waiting times and experienced workload. Since it is known how many appointments a client needs before a new client needs to be notified for an initial appointment, it is possible to delay the scheduling of a new client until a current client leaves. To make scheduling easier and reduce irregularity, a blueprint schedule is made. To decide which type of client to plan with which type of practitioner, a Markov Decision Process (MDP) is formulated. Due to the curses of dimensionality, this is too large to solve for real-life cases. Therefore an approximation is made, which uses simple scheduling rules: a combination of a trunk reservation policy and a threshold policy. This approximation gives an expected penalty of 27% higher and an expected queue length of 29% higher than the MDP for small cases, while the runtime is much shorter. The approximation outperforms the current policy of assigning clients to practitioners assuming back-to-back scheduling in a realistic sized simulation.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics
Programme:Applied Mathematics MSc (60348)
Link to this item:http://purl.utwente.nl/essays/88619
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