سال انتشار: ۱۳۹۰
محل انتشار: هشتمین همایش ملی انرژی
تعداد صفحات: ۱۷
Mona Golchinpour – Department of industrial engineering, Iran University of Science and Technology (IUST), Tehran, Iran
Ali Barzegar – Department of industrial engineering, Iran University of Science and Technology (IUST), Tehran, Iran
Appropriate and accurate allocation of resources is the main objects for the most oil depots around the world. Modeling is one of the fastest and cheapest procedures in problem solving methods in the resource allocation field among the world complex activities. Key problems of the application of modeling & simulation for the management of the Northwest of Tehran oil products’ depot (Kan Oil Depot) are discussed. The aim of the investigation is to improve the processes at the oil depot. The model simulates all processes with a high level of accuracy. The resources allocation modeled in this paper, minimizes the average staying time of tanker trucks so the performance of the oil depot is maximized.A queuing network model of the logistic activities related to the arrival, loading, and departure processes of tanker trucks at the Oil depot is presented in this paper. Non standard service stations, priority mechanisms, and complex policies prevent the use of analytical approaches to the solution. Computer simulation, is an appropriate & efficient means for the analysis & evalution of different models including the queue system. This technique provides the analyzer with acceptable quantitive data for the strategic programming & improving the efficency of the oil depots. A simulation model for the above queuing network has been developed. Based on data from a real case study, this paper describes a number of simulation experiments to assess the impact of loading arms combinations on tanker trucks waiting times. Good validation results, against response measures on a real system, are obtained. Simulation results illustrate the use of the model for optimization approach to loading arm planning.We simulated the Oil Depot system, in a popular simulation package, namely Enterprise Dynamics, Next we searched for its optimal control levels, using OptQuest. The resulting optimum was compared with optima estimated by simulated annealing. We employ the trial & error method to tune different parameters of SA algorithm.