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Optimal Maintenance Planning in Novel Settings

He, Kai (2017) Optimal Maintenance Planning in Novel Settings. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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In this dissertation work, we focus on optimal planning of maintenance activities in several novel settings.

First, we consider a maintenance optimization model for a system with periodic preventive maintenance (PM), and periodic imperfect inspections to detect hidden failures. Our stylized mathematical model is inspired by the increasingly popular remote monitoring practices. We describe, both analytically and numerically, important structural properties of the model, and propose a simple approach to find a globally optimal solution.

In the second chapter, we investigate a maintenance planning scenario in which the implementation of PM is unpunctual. Under the assumption that the degree of the unpunctuality follows a known probability distribution, we formulate cost-rate minimizing models to study the impact of such deviations. We establish both analytical and numerical results for two specific types of maintenance policies common in practice, namely age replacement with and without minimal repair.

Finally, we focus on "maintaining" the health status of a patient with a chronic disease by investigating an optimal medical treatment sequencing problem. We restrict our attention to the two treatment case, and simultaneously balance three tradeoffs inherent to these treatments, i.e., length of effectiveness delay, probability of effectiveness and cost/reward. We provide both theoretical conditions and numerical examples that indicate when, as a function of the model parameters, it is optimal to initiate treatment with one treatment versus the other.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
He, Kaikah167@pitt.eduKAH167
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairProkopyev,
Committee CoChairMaillart,
Committee MemberKharoufeh, Jeffrey
Committee MemberRoberts, Mark
Date: 1 February 2017
Date Type: Publication
Defense Date: 19 October 2016
Approval Date: 1 February 2017
Submission Date: 30 October 2016
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 130
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Industrial Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Maintenance optimization, medical decision models, stochastic processes
Additional Information: In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of University of Pittsburgh's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to to learn how to obtain a License from RightsLink.
Date Deposited: 01 Feb 2017 20:32
Last Modified: 01 Feb 2019 06:15


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