Optimization of maintenance in supply chain process and risk-based critical failure situations (case study: Iranian oil pipeline and telecommunication company, north district)

A system’s approach depends on the low malfunction of the equipment and processes of that system, and maintenance plays an essential role in achieving this goal. In addition, over time, the equipment quality decreases, and a quality transfer from controlled to uncontrolled mode may occur, characterized by an increase in the rate of return of the product and the tendency to fail. One of the methods that researchers have widely used in analyzing the risk of net operations is the analysis of the effect and failure modes to identify critical failure modes and focus planning and net resources on them. In analyzing the effect and failure modes, one of the essential steps is prioritizing the equipment to determine the critical equipment, as well as determining the fundamental failure modes and prioritizing them to plan the net operation purposefully. This paper aims to dynamically rank equipment in intuitionistic fuzzy environments with interval values ​​to identify and prioritize critical equipment and present a mathematical model for combining optimization of preventive maintenance intervals and control parameters. For this purpose, a model is presented that calculates the dynamic weights of each piece of equipment according to the conditions of each piece of equipment in the indicators of failure probability, failure consequence, and lack of fault detection power. Therefore, dynamic ranking is provided for the equipment. In this research, for dynamic prioritization of equipment, the method of analysis of the ratio of intuitionistic fuzzy gradual weighting with quantitative values ​​(IVIF-SWARA) was presented. Then, a mathematical model was presented for the identified critical equipment. The proposed model can determine the optimal value of each of the four decision variables, i.e., sample size, inspection rotation time, control limit coefficient, and preventive repair intervals of each of the critical equipment of the Northern Oil Pipeline and Telecommunication Company and the total expected cost of integration per unit. Minimize time. The results show that the proposed model is much more flexible in calculating equipment’s weight and dynamic rating and provides more logical rating results.