Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2013

Abstract

In this paper we investigate a stochastic appointment-scheduling problem in an outpatient clinic with a single doctor. The number of patients and their sequence of arrivals are fixed, and the scheduling problem is to determine an appointment time for each patient. The service durations of the patients are stochastic, and only the mean and covariance estimates are known. We do not assume any exact distributional form of the service durations, and we solve for distributionally robust schedules that minimize the expectation of the weighted sum of patients' waiting time and the doctor's overtime. We formulate this scheduling problem as a convex conic optimization problem with a tractable semidefinite relaxation. Our model can be extended to handle additional support constraints of the service durations. Using the primal–dual optimality conditions, we prove several interesting structural properties of the optimal schedules. We develop an efficient semidefinite relaxation of the conic program and show that we can still obtain near-optimal solutions on benchmark instances in the existing literature. We apply our approach to develop a practical appointment schedule at an eye clinic that can significantly improve the efficiency of the appointment system in the clinic, compared to an existing schedule.

Keywords

appointment scheduling, copositive programming, semidefinite programming, network flow

Discipline

Operations and Supply Chain Management

Research Areas

Operations Management

Publication

Operations Research

Volume

61

Issue

3

First Page

711

Last Page

726

ISSN

0030-364X

Identifier

10.1287/opre.2013.1158

Publisher

INFORMS

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1287/opre.2013.1158

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