The maintenance of highway traffic control devices faces problems based on the conventional and experimental methods. To resolve the above mentioned complexity, this paper sets forth mathematical models which can provide an optimum interval for non-operational devices, considering the financial limitations. For the optimized selection of various marking materials another model has also been presented. This paper has also tried to propose an appropriate management system for the maintenance of traffic control devices, along with the development of computer software for control devices, dentifying the necessary procedure, time, tools, and manpower. The computer software and the mathematical models presented leads to optimization of the Repair and maintenance operations.

With the evolution of technology, the maintenance of sophisticated systems is of concern for system engineers and system designers. The maintenance cost of the system depends in general on the replacement and Repair policies. The system replacement may be in a strictly periodic fashion or on a random basis depending upon the maintenance policy. At failure, the Repair of the system may be performed perfectly or minimally associated with some probability. When perfect Repair is done, it makes the system as good as the new one. In case of minimal Repair, it returns to the working condition of the system at the time of failure. In the present paper, we study the replacement policies for the system wherein minimal or perfect Repair is done at the time of failure. The expressions for s-expected cost for the system with replacement and minimal or perfect Repair are evaluated. The maintenance costs are discussed for various policies. Numerical simulation is performed to validate the analytical results.

Offering maintenance Service Contracts (MSCs) for production equipment can be a good source of revenue for theProviders of these services. To do this, designing an optimal MSC with a minimum price will be of great interest to service providers. In this paper, the effect of conducting periodic preventive maintenance on the failure process of a given equipment item and its corresponding cost are studied. Assuming minimal Repair at failure with a random Repair cost, the expected cost of maintenance service is estimated. Due to the time delay between selling the contract and paying the Repair bills, the time value of service cost at the time of selling the contract is derived. Then, the cost-plus approach is used to determine the price of the MSC. In the presented model, the service provider determines the number of preventive maintenance and the improvement level to minimize the expected price of the MSC. A numerical example with comprehensive sensitivity analysis is presented to illustrate the model and its parameters' effect. The result shows that the presented model helps the service provider to design an MSC with a minimum price while assuring the profit margin.

The management of machine replacement (specifically tractors) is one of the most critical factors for performing field operations on time. Therefore, tractor Repair and maintenance costs must be accurately predicted. This research was carried out to evaluate the use of the regression technique in predicting tractor Repair and maintenance costs. The study was conducted using empirical data on 60 two-wheel drive tractors from Astan Ghods-e Razavi agro-industry. Regression analyses were carried out. The response variable was Repair and maintenance cost value and the independent variable was cumulative hours of use. Different second and third-order polynomial models and exponential and power models of age were examined. The results showed that the cubic regression model was the best model for predicting Repair, oil and fuel costs based on the coefficient of determination and significance of regression coefficients. The performance cubic regression model performed much better in the test phase for the prediction of Repair costs with a mean absolute percentage error of 3.72 in comparison to oil and fuel costs with a mean absolute percentage errors of 5.21 and 9.51, respectively.

An appropriate maintenance policy says that Repairs should be done when needed. Although preventive Repairs can reduce sudden and unexpected Repairs, they still reduce availability and increase Repair costs. Companies need to develop online and predictive maintenance strategies that can anticipate that any failure could occur at any time, and recognize this need from the signs and symptoms of equipment. This is called predictive maintenance or condition-based maintenance. in this paper we have tried to design a decision support model for predictive maintenance based on conditions based on data mining techniques. This project has been carried out in one of the oil and gas exploitation companies in the south of the country and the selected equipment for this project is gas turbines, which is one of the most basic and critical equipments in oil processing factories. In this project, MPL neural network data mining technique has been used to predict the occurrence of failure in the equipment. Finally, suggestions such as the development of this model for other equipment, controlling the duration of viewing the equipment status and determining the optimal maintenance time for the future are presented.

Provision of a prioritization algorithm for maintenance and Repair of railways in Iran has been targeted to optimize and update short and long term plans for rail maintenance operations in this research. In this article، the current conditions of the railway in Iran were studied and international studies were conducted to develop a prioritization algorithm. Consequently، the method of designing and developing the necessary models to provide a priority algorithm for maintenance of tracks based on the geometry of the route with the aim of achieving the required safety level of the passenger travel comfort in the current track maintenance methods was presented. The benefits of the new maintenance approach are illustrated by the use of evaluating its outputs with the current approach. The new maintenance algorithm ensures not only the safety of the route، but also the level of comfort needed for the traveler. The results indicate the applicability and feasibility of using the proposed model or algorithm in providing a priority program and planning for the maintenance of the railway.

Farm machinery managers often need to make complex economic decisions on machinery replacement.Repair and maintenance costs can have significant impacts on this economic decision. The farm manager must be able to predict farm machinery Repair and maintenance costs. This study aimed to identify a regression model that can adequately represent the Repair and maintenance costs in terms of machine age in cumulative hours of use. The regression model has the ability to predict the Repair and maintenance costs for longer time periods.Therefore, it can be used for the estimation of the economic life. The study was conducted using field data collected from 11 John-Deer 955 combine harvesters used in several western provinces of Iran. It was found that power model has a better performance for the prediction of combine Repair and maintenance costs. The results showed that the optimum replacement age of John-Deer 955 combine was 54300 cumulative hours.