Document Type

Dissertation

Degree

Doctor of Philosophy

Major

Business Administration

Date of Defense

11-28-2018

Graduate Advisor

Haitao Li, PhD

Co-Advisor

Keith Womer, PhD

Committee

Paul Speck, PhD

Andrea Cadenbach, PhD

Abstract

With the continued increase in age of the United States housing and building stock, as well as the continued need to maintain properties across the U.S., the need for timely, cost-optimal maintenance is ever more critical. This paper proposes the application of a mathematical model to aid in the scheduling and assignment of construction and maintenance tasks, considering the multi-skilled workforce. The benefit of this approach is to take advantage of the economies of scale that can be developed using cross-functional skilled workers with varying levels of competence and efficiency. This approach schedules and assigns tasks using data from maintenance task software datasets, using the least-cost, competent worker available for the job while also considering the trade-off between skilled labor cost and travel costs, both in terms of travel wage and vehicle wear and tear. The model is enhanced to include pairing between a mentor and an apprentice, where combined efficiency and pairing costs are considered at the same time as travel costs. Due to the practical nature of this research, a case organization was used and data from that firm was analyzed so that operational insights into the necessity of such a model could be considered. The mathematical backbone of the optimization approach to multi-skilled resource allocation considers the temporal and spatial demands of a geographically dispersed property management program. Actual, as opposed to sample, data allows us to evaluate the real financial implications on the case firm, if such an approach to scheduling is used. The generalization of this data provides excellent fit for a model that can be used to assign the best capable worker to the most cost-efficient task, considering deadlines, priorities and availability. Results of this scheduling approach provide significant cost and resource reductions over the historical firm performance, though practical considerations should temper that expectation. The above approach offers exceptional scalability and adaptability with the continued advancement of algorithm approaches to network-distribution and peer-to-peer work platforms.

OCLC Number

1101622466

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