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

محورهای موضوعی : Operation managment in manufacturing

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

تاریخ دریافت : 1399/04/25 تاریخ پذیرش : 1399/06/23 تاریخ انتشار : 1399/05/11

کلید واژه: Supply chain design, Spare Parts, Probability Density Function, Maintenance-Repair-Operation,

چکیده مقاله :

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.

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