HEURISTIC APPROACH TO SCHEDULE PREVENTIVE MAINTENANCE OPERATIONS USING K-MEANS METHODOLOGY

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 10, October 2017, pp , Article : IJMET_08_10_033 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed HEURISTIC APPROACH TO SCHEDULE PREVENTIVE MAINTENANCE OPERATIONS USING K-MEANS METHODOLOGY Abdelhakim Abdelhadi Engineering Management Department, Prince Sultan University, Kingdom of Saudi Arabia ABSTRACT: This paper aims to implement non hierarchal clustering methods to cluster the maintainable machines and schedule their maintenance as part of a company s manufacturing strategy. We consider clustering maintainable machines into virtual groups based on their need for maintenance according to the time to failure and according to the location those machines. Nonhierarchical clustering method is used to cluster machines into virtual cells based on their location and time to conduct preventive maintenance. It is a methodology that identifies and exploits the common similarities among the attributes of a set of objects and clusters them into cells. It is designed to group items into clusters, K. This approach will lend itself to the reduction of scheduling process and standard process plans can be developed to complete the job in each cell. Spare parts needed for maintenance will be ordered in economical quantities which will help in cost reduction. Machine grouping will have a positive influence on human resource management and spare parts management. The result of this research will lead to will group machines into virtual clusters based on the time to failure and the geographical location of the maintainable items. s developed using k-means algorithm will minimize the cost of conducting the maintenance since the clusters have a main common factor, location and time to conduct the maintenance. Keywords: Operations strategy, Maintenance management, Maintenance, Nonhierarchical clustering method, Spare Parts Cite this Article: Abdelhakim Abdelhadi, Heuristic approach to schedule preventive maintenance operations using K-means methodology, International Journal of Mechanical Engineering and Technology 8(10), 2017, pp editor@iaeme.com

2 Heuristic approach to schedule preventive maintenance operations using K-means methodology 1. INTRODUCTION Maintenance is a vital part of any manufacturing operation, especially in the current global climate of intense competitive pressures where companies are looking for any source of competitive advantage. Maintenance is part of any manufacturing strategy and its objectives should be derived from the overall business plan as it can affect production by increasing capacity and controlling for the quality and quantity of the output. For many systems, such as aircraft, medical equipment and water utilities, it is extremely important that machines be kept running in accordance with their design specifications to prevent the occurrence of failure during operation. An appropriate maintenance strategy is very important to prevent any failure which could cause a catastrophic breakdown in the system which could lead to safety hazards (damage to humans and/or the environment). Pinjala and Pintelton [1] (2004) conducted a survey among Belgian industries that indicated that more than 70 per cent of the responders considered that maintenance can be used as a tool to enhance the competitiveness of a business. A good maintenance program can avoid machine failures at bad times by fixing them before any damage is caused; thus, it preserves the capital investment and avoids any production loss due to unexpected failures. Reactive maintenance is conducted after the equipment failure. Preventive maintenance is conducted based on time-based or operational based criteria (planned maintenance). Recently, a strategy called condition-based-maintenance (CBM) (Jardine et al, 2006) [2] has been implemented. Condition-Based Maintenance is a method that recommends maintenance decisions based on the actual health status of a machine or its components [3](Fraser et. al. 2015). Qinming and Wenyuan, (2015) [4] applied multi-component manufacturing system maintenance scheduling based on degradation information using genetic algorithm. Zhao et. al.(2014),[5] integrated production planning and maintenance using an iterative method. Farrero, et. al. [6] (2002), used optimization of replacement stocks using a maintenance program derived from reliability studies of production systems. Maintaining a strategy for each piece of equipment or system is a very complicated task due to several factors, such as: data collection, types of equipment and its function and the large number of criteria that affect the maintenance procedure itself. Hence, the decision makers have to decide on the appropriate maintenance strategy for the system under consideration. Several criteria must be considered when selecting a maintenance strategy, such as safety aspects, environmental issues, and reliability of the strategy, failure costs and manpower utilization. Accordingly, the selection of a maintenance strategy is a very complicated matter. Tsang (1998) [7] considers maintenance strategies as maximizing asset utilization, improving responsiveness and focusing on enhancing the core of the business. Alfonsus and Won, [8] (2017) and, Pravin and Makarand, [9] (2016) integrated cost and quality in their proposed maintenance policy. Cholasuke et al., (2004) [10] categorized the effectiveness measures of any maintenance approach into nine areas based on the available literature, namely: policy deployment and organization; human resource management; financial aspects; continuous improvement; contracting out maintenance; maintenance approach; task planning and scheduling; information management; and spare parts management. An effective maintenance strategy is one that fits the needs of the business. Several approaches are used to develop planning and scheduling programs to conduct preventive maintenance. Conditionbased maintenance to select equipments for maintenance scheduling is widely used approach. Recommendations from manufacturers and experts are used to develop a Preventive Maintenance schedule editor@iaeme.com

3 Abdelhakim Abdelhadi Several algorithms and techniques have been proposed to schedule PM activities, some of which are based on mixed integer programming whilst others integrate aggregate production and maintenance planning together. In this paper, multivariate statistical analysis is used to schedule preventive maintenance for equipments. The proposed approach is used to increase the effectiveness of preventive maintenance strategy by grouping machines into virtual cells to conduct preventive maintenance operations by considering their time to failure in conjunction with their locations. 2. PROPOSED METHODOLOGY: ing problem based on non-hierarchal clustering in conjunction with the application of a group technology to establish clusters of maintainable machines based on their need for maintenance. Groups of machines are established according to the time to failure and the location of the maintainable item. K-means method is used to develop clusters of the maintainable items. It works by assigns each item in the cluster having the nearest mean (centroid). Non hierarchal methods can start either by partitioning items into groups or by assigning a set of seed points which will form the nuclei of clusters. As described by MacQueen [11], the processes are as follows: Divide the items under study into k initial clusters. Assign an item to a cluster which has the closest centroid. Recalculate the new centroid for the cluster receiving the new item and for the cluster losing the item. Repeat the previous step until all items have been assigned. 3. ILLUSTRATIVE EXAMPLE: The data shown in Table (1) are based on a hypothetical manufacturing facility planned to conduct a preventive maintenance operation for its machines and equipments. There are 57 different machines needs preventive maintenance. The x and y coordinate and the forecasted time to conduct the maintenance is shown. Table 1 Maintainable machines locations ( x and y coordinates) and their planned maintenance time Machine X (m) Y(m) Time to conduct the maintenance (Day) Machine X (m) Y(m) Time to conduct the maintenance (Day) M M M M M M M M M M M M M M M M M M M M M M M M editor@iaeme.com

4 Heuristic approach to schedule preventive maintenance operations using K-means methodology M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M In the first step: divide the items/machines under study into initial seed clusters. This step can be achieved by arranging the machines in ascending order according to their time to conduct the preventive maintenance as per the predefined schedule, select the size of each based on the availability of resources to conduct preventive maintenance. Next step; apply k-mean clustering algorithm on each of the seed clusters by using the machines x and y coordination as the reference variables. Check outliers in the developed clusters and arrange their maintenance accordingly. The outliers mailer occurs when two machines or more havening same centroid, but with switched x and y coordinates. Table 2 presents the initial groups of machines arranged according to their time to conduct the preventive maintenance. Machine X (m ) Y(m) M M M M M M M M M M M M M Time (Day) Initial developed s G editor@iaeme.com

5 Abdelhakim Abdelhadi M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M M G2 G3 G4 G editor@iaeme.com

6 Heuristic approach to schedule preventive maintenance operations using K-means methodology Now, we have 5 seed cluster base on time to conduct preventive maintenance; G1, G2, G3, G4 and G5. Minitab software is used develop to clusters of machines according to their locations. Tables 3, 4, 5, 6 and 7 show the clusters of machines developed using k-mean method: Table 3 s of machines derived from G1 Machine X (m ) Y(m) Time (Day) M M M M M M M M M M M M M Table 4 s of machines derived from G2 Machine x, m y, m T, Day M M M M M M M Table 5 s of machines derived from G3 machine x, m y, m Time, Day M M M M M M M M M M editor@iaeme.com

7 Abdelhakim Abdelhadi M M M M M M M M M M M Table 6 s of machines derived from G4 Machine x, m y, m Time, Day M M M M M M M Table 7 s of machines derived from G5 Machine x, m y, m Time, Day M M M M M M M M Since each group is formed based on the closest centroid among its elements; some outliers may exist in some formed groups, for example: M34 joined M1, M3, M19, M40, M48 and M55 even its location is not in same area due to fact that its coordinates were in the opposite direction of the rest of the group, but it has a close centroid as the rest of the group. 4. DISCUSSION AND CONCLUSION The proposed approach to schedule maintenance operation is based on two factors, namely; location and pre-defined time to conduct preventive maintenance for the equipments under consideration for maintenance. This approach can be very useful when the maintainable items under considerations are far apart. developed using k-means algorithm will minimize the cost of conducting the maintenance since the clusters have a main common factor, location and time to conduct the editor@iaeme.com

8 Heuristic approach to schedule preventive maintenance operations using K-means methodology maintenance. For example, cluster 3 derived from G4 consist of M22, M14 and M37 having the following coordinate: (34, 436), (34, 234) and (54, 234) and time to conduct maintenance is 267, 324 and 324 respectively. This shows that the developed clusters are very close to each other and having a very close time to conduct the preventive maintenance. In the same group, G4, cluster number 1 which has a coordinate of (451, 436) and time to conduct maintenance at 324 has to be moved to some other clusters as it is an outlier. REFERENCES: [1] Pinjala, S.K. and Pintelon, L. (2004). Bridging the gap between manufacturing and, maintenance In Wassenhove, L.V., De Meyer, A., Yucesan, E. and Boer, H. (Eds), Operations Management as a Change Agent, Vol. II, Insead Business School, Fontainebleau, [2] Jardine, A.K.S. and Tsang, H.C. (2005), Maintenance Replacement and Reliability, CRC Press, New York, NY. [3] Kym Fraser, Hans-Henrik Hvolby, Tzu-Liang (Bill) Tseng, (2015) Maintenance management models: a study of the published literature to identify empirical evidence: A greater practical focus is needed, International Journal of Quality & Reliability Management, Vol. 32 Issue: 6, pp [4] Qinming Liu, Wenyuan Lv, (2015), Multi-component manufacturing system maintenance scheduling based on degradation information using genetic algorithm, Industrial Management & Data Systems, Vol. 115 Issue: 8, pp [5] Zhao, S.X., Wang, L.Y. and Zheng, Y. (2014), Integrating production planning and maintenance: an iterative method, Industrial Management and Data Systems, Vol. 114 No. 2, pp [6] Farrero, C., José, M., Guitart, T. and Bolancé, L. (2002), Optimization of replacement stocks using a maintenance programme derived from reliability studies of production systems, Industrial Management and Data Systems, Vol. 102 Nos 3-4, pp [7] Tsang, H.C.A. (1998). A strategic approach to managing maintenance performance, Journal of Quality in Maintenance Engineering, 4(2), [8] Alfonsus, J. E. and Won, Y. Y. (2017) A preventive maintenance of circular consecutive->k>-out-of->n>: F systems, International Journal of Quality & Reliability Management, Vol. 34 Issue: 6, pp [9] Pravin, P. T. and Makarand, S. K. (2016) Selective maintenance optimization under schedule and quality constraints, International Journal of Quality & Reliability Management, Vol. 33 Issue: 7, pp [10] Cholasuke, C., Bhardwa, R. and Antony, F. (2004). The status of maintenance management in UK manufacturing organizations: results from a pilot survey, Journal of Quality in Maintenance Engineering, 10(1), 5-15 [11] MacQueen, J. (1967) Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics, , University of California Press, Berkeley, Calif. [12] Mgonja, C. T. and Saidi, H., Effectiveness on Implementation of Maintenance Management System For off-grid Solar PV Systems In Public Facilities A Case Study of SSMP1 Project In Tanzania, International Journal of Mechanical Engineering and Technology, 8(7), 2017, pp [13] Dr. Leena Jain and GaganDeep Singh, A Review: Meta- Heuristic Approaches for Solving Rectangle Packing Problem, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, March April (2013), pp editor@iaeme.com