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MYOPIC POLICIES FOR NON-PREEMPTIVE SCHEDULING OF JOBS WITH DECAYING VALUE

Published online by Cambridge University Press:  28 November 2016

Neal Master ,
Carri W. Chan  and
Nicholas Bambos
Neal Master
Affiliation:
Department of Electrical Engineering, Stanford University, Stanford, California, USA E-mail: [email protected]
Carri W. Chan
Affiliation:
Decision, Risk, and Operations, Columbia Business School, New York, New York, USA E-mail: [email protected]
Nicholas Bambos
Affiliation:
Department of Management Sciences & Engineering and Department of Electrical Engineering, Stanford University, Stanford, California, USA E-mail: [email protected]
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Abstract

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In many scheduling applications, minimizing delays is of high importance. One adverse effect of such delays is that the reward for completion of a job may decay over time. Indeed in healthcare settings, delays in access to care can result in worse outcomes, such as an increase in mortality risk. Motivated by managing hospital operations in disaster scenarios, as well as other applications in perishable inventory control and information services, we consider non-preemptive scheduling of jobs whose internal value decays over time. Because solving for the optimal scheduling policy is computationally intractable, we focus our attention on the performance of three intuitive heuristics: (1) a policy which maximizes the expected immediate reward, (2) a policy which maximizes the expected immediate reward rate, and (3) a policy which prioritizes jobs with imminent deadlines. We provide performance guarantees for all three policies and show that many of these performance bounds are tight. In addition, we provide numerical experiments and simulations to compare how the policies perform in a variety of scenarios. Our theoretical and numerical results allow us to establish rules-of-thumb for applying these heuristics in a variety of situations, including patient scheduling scenarios.

Keywords

operations researchstochastic dynamic programmingstochastic modelling
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 32 , Issue 1 , January 2018 , pp. 1 - 36
Copyright
Copyright © Cambridge University Press 2016 

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