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Preventive Maintenance Scheduling: Decision Model Development and Case Study Analysis

Preventive Maintenance Scheduling: Decision Model Development and Case Study Analysis

S.A. Oke, O.E. Charles-Owaba, A.E. Oluleye
Copyright: © 2013 |Volume: 4 |Issue: 2 |Pages: 26
ISSN: 1947-9328|EISSN: 1947-9336|EISBN13: 9781466632998|DOI: 10.4018/joris.2013040105
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MLA

Oke, S.A., et al. "Preventive Maintenance Scheduling: Decision Model Development and Case Study Analysis." IJORIS vol.4, no.2 2013: pp.69-94. http://doi.org/10.4018/joris.2013040105

APA

Oke, S., Charles-Owaba, O., & Oluleye, A. (2013). Preventive Maintenance Scheduling: Decision Model Development and Case Study Analysis. International Journal of Operations Research and Information Systems (IJORIS), 4(2), 69-94. http://doi.org/10.4018/joris.2013040105

Chicago

Oke, S.A., O.E. Charles-Owaba, and A.E. Oluleye. "Preventive Maintenance Scheduling: Decision Model Development and Case Study Analysis," International Journal of Operations Research and Information Systems (IJORIS) 4, no.2: 69-94. http://doi.org/10.4018/joris.2013040105

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Abstract

In this work, the effectiveness of preventive maintenance scheduling (PMS) decisions was reported based on a techno-economic model that reflects cost objective function for ship maintenance activities. With a potential to impact on both transportation businesses and users of transportation services, the model provides an alternative to the combined classical literature problems of spare-parts inventory management and control, failure prediction and reliability. The PMS model developed incorporates separate and combined functions of indirect, direct and factor maintenance costs. Idleness period for various formulated schedules are evaluated and compared. First, a general form of the preventive maintenance cost function incorporating unit cost of maintaining ships, a set of cost function parameters and variables was developed. The operations research framework for the problem is then applied to obtain test cases in which cost parameter(s) was/were used for scheduling decisions. Monte Carlo simulation is applied to generate additional test problems. Practical data were used to validate the model. For each problem, optimal schedules based on one to four cost parameters were determined. For each schedule, the total maintenance cost, cost of idleness, total ship idle period and total ship operation period were computed under inflation, opportunity and combined opportunity and inflation and compared with the values corresponding to maintenance cost parameter using t-test (p<0.05). Thus, the use of combined data from maintenance, opportunity and inflation for preventive maintenance scheduling of a fleet of ships is more effective than direct maintenance cost approach.

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