Maintenance strategy selection methods

The use of the Multi-Criteria Decision Making (MCDM) such as such as the Analytical Hierarchy Process (AHP), the Analytical Network Process (ANP) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) in making optimum decision for when faced with multi-criteria decision problem is becoming popular (Gandhare and Akarte, 2012, Bevilacqua et al., 2000). One of such multi-criteria decision problem is the maintenance strategy selection. These techniques have either been applied singly or integrated with one another or they have been used in conjunction with other tools such as fuzzy set theory and mathematical programming. (Bevilacqua and Braglia, 2000) applied AHP to select the ideal maintenance strategy for an integrated gasification and combined cycle plant. The analysis took into consideration five possible alternatives: preventive, predictive, condition- based, corrective and opportunistic maintenance. The authors used AHP in conjunction with Failure Mode Effect and Criticality Analysis (FMECA) principles in order to choose the ideal maintenance strategy for each analysed item in the plant. Other examples of the application of AHP for maintenance strategy selection are: Triantaphyllou et al. (1997) proposed an AHP technique for the selection of a maintenance strategy taking into consideration four maintenance decision criteria; Nyström and Söderholm (2010) presented a methodology based on AHP for prioritising different maintenance actions in railway infrastructure, and Labib et al. (1998) developed a model based on AHP for optimum maintenance decision making for an integrated manufacturing system.

Bertolini and Bevilacqua (2006) presented an integrated AHP and Goal Programming (GP)

technique such that the best strategies for the maintenance of centrifugal pumps in an oil refinery is chosen. The model that was proposed considered decision criteria such as account budget and number of man-hour constraints in comparring three alternative maintenance strategies (corrective, preventive and predictive). The authors concluded that the application of an integrated AHP and GP methodology proved to be a viable tool for minimization of maintenance cost (Bertolini and Bevilacqua, 2006). Similar to this approach, Arunraj and Maiti (2010) used AHP and a GP method for the selection of a maintenance strategy for a benzene extraction unit within a chemical plant. Equipment failure risk and the cost of

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performing maintenance were considered as the relevant decision criteria. AHP was used to assign weights to the decision criteria by means of pairwise comparison and the GP considered the assigned weight to rank the alternative maintenance strategies (corrective, time based, condition based and shutdown maintenance). The main improvement to the work of Bertolini and Bevilacqua (2006) was the use of the Fussell-Vesely (F-V) importance measure by the authors in order to estimate the risk contributions of different items of equipment. Zaim et al. (2012) reported on the use of a combination of AHP and ANP techniques for selecting the optimum maintenance strategy for a newspaper printing facility located in Turkey. From the comparative study, the two techniques yielded almost the same results.

The use of integrated fuzzy logic and MCDM (such as AHP) for maintenance strategy selection has also been reported in literature. Al-Najjar and Alsyouf (2003) used integrated fuzzy logic and AHP techniques to select the most cost effective maintenance strategy for a pump station. Wang et al. (2007) also proposed a fuzzy logic-AHP technique in order to select optimal maintenance strategies for different equipment items in a manufacturing firm.

The Reliability Centered Maintenance (RCM) technique is also widely used (Bevilacqua and Braglia, 2000, Mohan et al., 2004). “RCM represents a method for preserving functional integrity and it is designed to minimise overall maintenance costs by balancing the higher cost of corrective maintenance against the cost of preventive maintenance” (Crocker and Kumar, 2000b). RCM has been applied to a greater or lesser extent in the maritime industry for example the use of RCM logic diagrams in order to select the most appropriate maintenance strategy for different components of a system from the failure modes perspective (Conachey, 2005, American Bureau of Shipping, 2004). However the use of RCM is a very time consuming exercise and generally limited to some specific equipment (Waeyenbergh and Pintelon, 2004). Another limitation of the RCM technique in selecting maintenance strategies is that it does not allow for ranking of maintenance alternatives such that the optimum solution can easily be selected. This prompted Lazakis et al. (2012) to develop a maintenance strategy selection methodology based on the integration of fuzzy set theory and TOPSIS for the selection of the maintenance strategy for a diesel generator in a cruise ship. The maintenance strategy selection model that the authors proposed compared three alternative maintenance strategies (corrective, preventive and predictive maintenance) against eight decision criteria: maintenance cost, efficiency/effectiveness, system reliability, management commitment, crew training, company investment, spare parts inventories and operation loss.

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From the analysis, condition based maintenance (CBM) was considered as the optimum maintenance strategy for the cruise ship diesel generator. However some doubts remain with regard to the practical use of the fuzzy approach because of the difficulty in testing and developing extensive sets of fuzzy rules (Zammori and Gabbrielli, 2012, Braglia, 2000). Additionally some important decision criteria such as applicability for maintenance strategy selection especially when dealing with the problem from the system failure modes perspective were not taken into account in Lazakis et al. (2012). In further work, Lazakis and Olcer (2015) aimed to improve the performance of the fuzzy-TOPSIS methodology by integrating AHP into it. AHP was introduced to assist in the weighting of the decision criteria. The result of the enhanced technique yielded preventive maintenance as the optimum maintenance strategy for the for ship diesel generator.

Goossens and Basten (2015) utilized AHP in the selection of maintenance strategies for naval ship systems. The authors involved three different groups within the industry in the decision making process namely: the shipbuilders, the owners/operators and the Original Equipment Manufacturers (OEM). In selecting the optimal maintenance strategy for the ship system from three maintenance strategies; corrective, time/use-based maintenance and condition based maintenance, three level decision criteria were applied. The first level consisted of two decision criteria; the second level consisted of eight and the third level consisted of 29. From the analysed results, the maintenance strategy preferred by the shipbuilder, owner/operator and the OEM is condition based maintenance. However the structuring of the problem made it computationally intensive as it required formation and analysis of numerous pairwise judgements from experts.

Resobowo et al. (2014) also applied AHP in prioritizing the factors that affect military ship maintenance management. In this case, the factors considered were; cost, availability, reliability, safety, human resource, operations, types of ship and ship characteristics. These factors were ranked using planned maintenance, preventive maintenance and routine maintenance as decision criteria. According to the authors the result of the analysis revealed that the most important factor is human resource. The major interest of the authors was to identify important factors for making maintenance decisions and as such does not completely address the problem of maintenance strategy selection.

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It is obvious that there is a need for a more systematic approach that can easily incorporate qualitatively and/or quantitatively the maintenance alternatives selection criteria for marine system applications. On this basis one of the objectives of this research was to develop an alternative maintenance strategy selection method which avoids the limitations of the approaches used in literature.