A Simulation Algorithm for Appraising the Effect of Spare Parts Inventory Model on Machine Availability

Yıl 2020, Cilt 35, Sayı 2, 455 - 468, 30.06.2020

Onur GÖLBAŞI

https://doi.org/10.21605/cukurovaummfd.792439

Öz

This study intends to develop a simulation algorithm that is capable of evaluating the effect of a spare parts inventory policy on the availability of mining machinery. Scheduled production and maintenance halts and the random downtimes caused by machinery failures are regarded in the algorithm. Available inventory stock levels and the active lead times are evaluated together with these downtimes. By this way, negative effects of the applied inventory policies on machinery availabilities can be measured. The developed algorithm was implemented for a mining machinery, and the inventory policies introduced for the thirteen different components of this machinery were discussed according to their potential negative effects on the machinery availability. In the results, it was detected that eight out of thirteen components have the spare part storage and procurement policies that are not accordant with the component failure behaviors, and this situation leads to a decrease of 9% in the machinery availability.

Anahtar Kelimeler

Mining machinery, Spare parts, Inventory policy, Machinery availability, Simulation algorithm

Yedek Parça Envanter Politikasının Makina Kullanılabilirliğine Etkisini Değerlendirmeye Yönelik Bir Simülasyon Algoritması

Öz

Bu çalışmada, yedek parça envanter politikasının maden makinası kullanılabilirliğine etkisini analiz edebilecek bir simülasyon algoritmasının geliştirilmesi amaçlanmıştır. Planlı üretim ve bakım duraksamaları ve makina arızalarının neden olacağı rasgele duraksamalar algoritma içerisinde dikkate alınmıştır. Her bir makina parçasının mevcut envanter miktarları ve tedarik süreçleri, uygulanan bakım- onarım kararları ile birlikte değerlendirilmektedir. Böylelikle, uygulanan envanter politikasının makina kullanılabilirliğine olumsuz etkileri ölçülebilmektedir. Geliştirilen bu algoritma bir maden makinasına uygulanmış ve envanter politikası tanımlanmış on üç farklı parçasının bu makinanın kullanılabilirliğine muhtemel olumsuz etkileri detaylı şekilde incelemiştir. Simülasyon sonucunda, sekiz parçaya ait yedek parça stoklama ve tedarik sürecinin, bu parçaların arızalanma davranışıyla uyumlu olmadığı ve makina kullanılabilirliğini %9 oranında azalttığı tespit edilmiştir.

Anahtar Kelimeler

Maden makinası, Yedek parça, Envanter politikası, Makina kullanılabilirliği, Simülasyon algoritması

Kaynakça

• 1. Samal, N.K., Pratihar, D.K., 2015. Joint Optimization of Preventive Maintenance and Spare Parts Inventory using Genetic Algorithms and Particle Swarm Optimization Algorithm. International Journal of Systems Assurance Engineering and Management, 6(3), 248–258. doi:10.1007/s13198-015-0349-3.
• 2. Panagiotidou, S., 2014. Joint Optimization of Spare Parts Ordering and Maintenance Policies for Multiple Identical Items Subject to Silent Failures. European Journal of Operational Research, 235(1), 300–314. doi:10.1016/j.ejor.2013.10.065.
• 3. Taleizadeh, A.A., Niaki, S.T.A., Barzinpour, F., 2011. Multiple-buyer Multiple-vendor Multi-product Multi-constraint Supply Chain Problem with Stochastic Demand and Variable Lead-time: A Harmony Search Algorithm. Applied Mathematics and Computation, 217(22), 9234–9253. doi:org/10.1016/j.amc.2011.04.001.
• 4. Panagiotidou, S., 2019. Joint Optimization of Spare Parts Ordering and Age-Based Preventive Replacement. International Journal of Production Research, 1–17. doi: 10.1080/00207543.2019.1677959.
• 5. Kader, B., Sofiene, D., Nidhal, R., Walid, E., 2013. Jointly Optimal Preventive Maintenance under Spare Parts Order Strategy. IFAC Proceedings Volumes, 46(9), 1376-1380. doi: 10.3182/20130619-3-RU-3018.00088.
• 6. Bounou, O., El Barkany, A., El Biyaali, A., 2017. Inventory Models for Spare Parts Management: A Review. In International Journal of Engineering Research in Africa Trans Tech Publications Ltd. (28, 182-198) doi: 10.4028/www.scientific.net/JERA.28.182.
• 7. Ghodrati, B., Kumar, U., 2005. Operating Environment-Based Spare Parts Forecasting and Logistics: A Case Study. International Journal of Logistics: Research and Applications, 8(2), 95-105. doi:10.1080/13675560512331338189.
• 8. Ghodrati, B., Akersten, P.A., Kumar, U., 2007. Spare Parts Estimation and Risk Assessment Conducted at Choghart Iron Ore Mine: A Case Study. Journal of Quality in Maintenance Engineering, 13(4), 353-363. doi:10.1108/13552510710829452.
• 9. Wang, L., Chu, J., Mao, W., 2009. A Condition-Based Replacement and Spare Provisioning Policy for Deteriorating Systems with Uncertain Deterioration to Failure. European Journal of Operational Research, 194, 184–205. doi:10.1016/j.ejor.2007.12.012.
• 10. Louit, D., Pascual, R., Banjevic, D., Jardine, A., 2011. Optimization Models for Critical Spare Parts Inventories-A Reliability Approach. Journal of the Operational Research Society, 62, 992-1004. doi:10.1057/jors.2010.49.
• 11. Martínez, A., Pascual, R., Maturana, S., 2016. A Methodology for Integrated Critical Spare Parts and Insurance Management. Applied Stochastic Models in Business and Industry, 32(1), 90-98. doi:10.1002/asmb.2125.
• 12. Zhang, Q., Lv, X., Shi, J., 2017. Research on Inventory Sharing Model of Frequent Mining Machinery Maintenance Spare Parts. Proceedings of 12th IEEE Conference on Industrial Electronics and Applications, 1224-1229. Siem Reap: IEEE. doi:10.1109/ICIEA.2017.8283026.
• 13. Qarahasanlou, N., Barabadi, A., Ataei, M., Einian, V., 2019. Spare Part Requirement Prediction Under Different Maintenance Strategies. International Journal of Mining, Reclamation and Environment, 33(3), 169- 182. doi:10.1080/17480930.2017. 1373883.
• 14. Reliasoft Reno© [Bilgisayar Yazılımı], 2019. Arizona, Tuscon.
• 15. Rossetti, M. D., 2015. Simulation Modeling and Arena. John Wiley & Sons, New Jersey.
• 16. Golbasi, O., Demirel, N., 2017. A Cost- Effective Simulation Algorithm for Inspection Interval Optimization: An Application to Mining Equipment. Computers & Industrial Engineering, 113, 525-540. doi:10.1016/ j.cie.2017.09.002.