فهرست مطالب سمانه بابایی مراد

Machines and equipment continuous operation exposes them to failure (such as aging, wear, corrosion and creep). Consequences of such deteriorations are not limited to efficiency, reliability and life cycle reduction, they also may lead to environmental degradation. Investigating adverse effects of system’s failures have led to many academic studies on condition-based maintenance strategies. One of prominent approaches in this area is   maintenance policy based on the system’s performance and status, which reduces the overhead costs of the associated penalties by controlling environmental damages. The purpose of this study is to model an optimal maintenance policy including production and inspection periods scheduling with minimal average total cost, under uncertainty conditions. In this article, the threshold exceeding period, time to failure from exceeding threshold point and the delay time to start maintenance activities are considered random variables and their effects have been studied. Numerical examples of the proposed maintenance model have been solved by Nelder–Mead, PSO and Genetic algorithms, and optimal inspection times are computed. Sensitivity analyses have been done numerically to study the effect of changes of different parameters on the optimal inspection times.

In recent years, the increasing pollution of the environment and its destructive consequences, as well as the growing concern about it, has led to new concepts and approaches to reduce these effects and preventive environmental measures. One of the most important of these approaches is the green supply chain. Green supply chain integrates supply chain management with environmental requirements in all stages of product design, selection and supply of raw materials, production and manufacturing, distribution and transfer processes, delivery to customer and finally after consumption, recycling management and Re-consumption is designed to maximize energy efficiency and resources while improving the overall performance of the supply chain. In this paper presents a multi-objective model for a green supply chain network. To solve it, the LP-metric method is used, which is coded in Gams software. The results show that the study of two important factors of cost and carbon dioxide emissions, as one of the most important and well-known pollutants, has led to the application of this model in the supply network and. The results also show that it is more important to pay attention to greenhouse gas emissions at higher demand.

Joint optimal production scheduling and preventive maintenanceare interested in many research andhas a potential impact on the performance of manufacturing systems. In addition, due to the uncertainty in demand, maintenance and inventory shortages are almost inevitable. Therefore, determining the optimal amount of buffer level, the time required to create additional storage space to address the loss, and the maintenance time is a concern for many manufacturers. Paper studied a single-machine production unit with incremental failure rates. The system begins with a h-sized inventory stored in period A and stops as soon as it reaches period m, whichever occurs earlier, and is subject to maintenance. The buffer inventory during this period. A mathematical model and a numerical approach are used to obtain optimal values of variables simultaneously to minimize the average total cost and satisfy the access constraint. The results show that, in the presented model, the total cost and decision variables are highly sensitive to the inventory holding cost but not also for the occurred scenario.

In this study, the model in the framework of hierarchical covering location by taking a dynamic approach (the radius of coverage and the amount of demand in different periods dynamically) in the fuzzy provide and studied. Location and allocation model for hospitals and facilities that includs rescue helicopter and ambulance to the demand, to establish and cover of demand that they are in the radius of coverage, provided, and also considered the possibility of being busy facility. The status of Subspecialty hospital, hospital and clinic locate and in different periods of time are fixed. Fuzzy concept has been used to draw closer to reality. Site of the service facilities, including ambulances and helicopters are variable in different periods. Also in models for this movement is considered cost. Services machines and hospitals and clinics have limited capacity. Given the fact that the goal is just to validate the model, numerical data is used. The method of solving this problem is using the numerical example of the GAMs software definitive method, and for larger scales, the ABC algorithm and ICA are used. To validate the proposed model, it compares it with Bashiri et al. Model model. The numerical results show the optimal efficiency of the proposed solution method and the problem model.

In this study, a model is presented for fire station’s locating and facilities allocating to stations in different periods and emergency situations. This model is designed, considering amount of demands and facilities coverage radius, being dynamic (based on traffic and type of region) in different periods. According to fact, in the presented model, amount of demand for each demand point depends on number of coverage by facilities and amount of demand of demand point .in this model , location of stations is determined once in different periods. The numbers of facilities which are allocated to stations are allocated dynamically and can be relocated in different periods. In the model, each strategic demand point (arsenal, food storage and so on) can be potential point for facilities. This is a complicated model so to solve this model, particle swarm optimization algorithm and combinatorial matrix have been suggested. In the suggested algorithm, method of making matrix is such that locating matrix and early and final allocation are presented in a single matrix. Finally the results of proposed algorithm with artificial bee colony were compared the results show that this algorithm is better in terms of quality of answers and solving time.