Designing an Improved Stochastic Planning Model for Supply Chain Considering Maintenance and Operations (MRO) in Rahiab Sanat Sepahan Engineering Technical Company

الموضوعات :

Sayyed Mohammad Reza Davoodi ¹ , Amir Hortamani ² , Nafiseh Bakhtiary Dastgerdi ³

1 - Assistant professor, Department of Management, Dehaghan Branch,Islamic Azad University
2 - Assistant Professor, Department of Economic, Dehaghan Branch, Islamic Azad University , Dehaghan, Iran
3 - Master of Industrial Management, Production and Operational Orientation, Amin Institute of Higher Education, Foolad Shahr, Isfahan

تاريخ الإرسال : 24 الأربعاء , ذو القعدة, 1441 تاريخ التأكيد : 25 الأحد , محرم, 1442 تاريخ الإصدار : 11 السبت , ذو الحجة, 1441

الکلمات المفتاحية: Supply chain design, Spare Parts, Probability Density Function, Maintenance-Repair-Operation,

ملخص المقالة :

The science of maintenance and the active life of the equipment is becoming increasingly important and is defined as a subset of asset management. This necessitates the design and development of a maintenance planning model for the supply chain. This research in the production and distribution program of the spare parts supply chain system, according to the specific and definite order and demand program of users in periods, leads to cost reduction and achieving economic goals in the production unit. In order to achieve this goal, some limitations for model equilibrium and justifying the answers obtained from the model solution are presented, in which we have achieved the optimal answer using the genetic algorithm method in MATLAB software. The present study is descriptive in terms of method and nature and applied in terms of purpose. This research has been done in terms of cross-sectional time and in six time periods and has no specific period. Data were collected through interviews and information available in the production unit of Rahyab Sanat Sepahan Company. According to the functions, the goal of minimizing system costs includes minimizing production, storage, and distribution costs, as well as reducing the difference between actual production time and nominal production capacity in the target function has been investigated. Reality can help make acceptable decisions despite various fluctuations in production lines, distribution centers, and transportation costs.

المصادر:

[1] Talebi, D. and Iron, F. 2015. Identifying supply chain risks and supplier selection using network analysis process (Case study: Automotive industry). Industrial Management Perspectives. 5 (1): 31-43.

[2] Jafarnejad, A., Morvati Sharifabadi, A. and Ataiee, A. 2012. Management of logistics supply chain, Tehran: Gostaresh-e-Ulum Publications.

[3] Ghazanfari, M and Fathullah, M. 2006. A comprehensive approach to supply chain management, Tehran: Iran University of Science and Technology. Technical report.

[4] Hamidiyeh, A. Arshadi Khamseh, A and Naderi, b. 2016. Combining new robust planning with credit constraints for a responsive-dependent closed-loop supply chain network under uncertainty and disruption. Journal of Engineering and Quality Management. 6 (4): 213-227.

[5] Rahmani, M., Ebrahimi, B., Khakzar Befrouei and M. 1396. Development of a comprehensive model of data envelopment analysis to determine the best supplier of inaccurate waste and weight constraints. Production and Operations Management. 8 (1): 101-118.

[6] Hosseini, M. and Shekhi, N. 2012. Explaining the strategic role of supply chain management operations in improving company performance: A study of the Iranian food industry, Strategic Management Studies. 3(10):35-60.

[7] Langenberg, K., Seifert, R., and Tancrez, J. 2012.Aligning supply chain portfolios with product portfolios. International Journal of Production Economics. 135(1): 500-513.

[8] Tavana.M., Zarook.Y. and Santos-Arteaga. F.j. 2015. An integrated three-stage maintenance scheduling model for unrelated parallel machines with aging effect and multi-maintenance activities. Computers & Industrial Engineering. 83: 226-236.

[9] Wibowo, A., Tjahjono, B. and Tomiyama, T. 2017. Designing Contracts for Aero-Engine MRO Service Providers:Models and Simulation. Procedia CIRP. 59:246-251.

[10] Rajesh,R. and Ravi,V. 2014. Supplier selection in resilient supply chains: a grey relational analysis approach. Journal of Cleaner Production. 86:343-359.

[11] Yoav, B. and Gur, M. 2016. A single machine scheduling problem to minimize total early work. Computers & Operations Research. 73: 115-118.

[12] Zhu.H., Li.M. and Zhou.Z. 2015. Machine scheduling with deteriorating and resource-dependent maintenance activity. Computers & Industrial Engineering. 88: 479-486.

[13] Busstra, M. and Van dongen.L. 2015.Creating  value by integrating logistac trains  services and maintenance activities. Procedia cirp. 38:250-254.

[14] Giri, B.C. and Sharma, S. 2017.Optimizing a closed-loop supply chain with manufacturing defects and quality dependent return rate. Journal of Manufacturing Systems. 35:92-111.

[15] Taherkhani, M. and Tavakoli Moghaddam, R. 2017. Development of a two-objective quadrilateral supply chain model using STEM method. Production and Operations Management. 8 (1): 65-78.

[16] Azimian, M., Javadi, H., Farshchiha, A. and Nasoohi, A. 1396. Selecting the best supplier combination by providing a combined approach to decision making with multiple criteria and analysis tree ‌ and error analysis. Production and Operations Management, 8 (1): 45-64.

[17] Yousefinejad, M. and Neyshabouri, A. 2009. Critical path analysis of finite resources based on discrete event simulation and group optimization of components, 5^th International Conference on Project Management, Tehran, Ariana Research Group.

[18] Karami, A., Arab, A. and Fallah Lajimi, H. 2016. Assessing the effect of key success factors of supply chain agility on the strategic performance of electronics companies in Iran, Tehran: Institute of Humanities and Cultural Studies, a comprehensive protocol of humanities. 19(4): 185-206. |

[19] Aghajani, H., Samadi Miarkalaei, H. and Farmanzadeh, M. 2014. Investigation and measurement of supply chain elasticity with fuzzy logic approach (Experimental control: auto parts manufacturing industry in Mazandaran province). Industrial Management Quarterly. 9 (27):83-94.

[20] Agha Ahmadi, F. and Mahoutchi, M. 2013. Two-stage random planning for supply chain design of perishable materials, 10^th International Conference on Industrial Engineering, Tehran, Iran Industrial Engineering Association, Amir Kabir University of Technology.

[21] Arab, L and Davoodi, S. M. R. 2019. Designing a closed-loop supply chain model in conditions of uncertainty, taking into account intermediate warehouses (Case study: Khodrang Company). Industrial Management Quarterly. 14 (49):73-94.