safety,risk and reliability
Amir Yousefli; reza Norouzi; Amir Hosein Hamzeiyan
Abstract
Reliability Redundancy Allocation (RRA) is one of the most important problems facing the managers to improve the systems performance. In the most RRA models, presented in the literature components’ reliability used to be assumed as an exact value in (0,1) interval, while various factors might affect ...
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Reliability Redundancy Allocation (RRA) is one of the most important problems facing the managers to improve the systems performance. In the most RRA models, presented in the literature components’ reliability used to be assumed as an exact value in (0,1) interval, while various factors might affect components’ reliability and change it over time. Therefore, components reliability values should be considered as uncertain parameters. In this paper, by developing a discrete - continuous inference system, an optimization - oriented decision support system is proposed considering the components’ reliability as stochastic variables. Proposed DSS uses stochastic if - then rules to infer optimum or near optimum values for the decision variables as well as the objective function. Finally, In order to evaluate the efficiency of the proposed system, several examples are provided. Comparison of the inferred results with the optimal values shows the very good performance of the developed stochastic decision support system.
Amir Yousefli; Vahid Karimpour Khameneh; Reza Norouzi
Abstract
Refineries, including the conversion industries, use oil and gas as row materials and their production processes and products could be so harmful to the environment if they could not manage properly. Nowadays, these companies are working hard to minimize the environmental damages caused by their products, ...
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Refineries, including the conversion industries, use oil and gas as row materials and their production processes and products could be so harmful to the environment if they could not manage properly. Nowadays, these companies are working hard to minimize the environmental damages caused by their products, production processes, supply chain, distribution methods and etc. The first step to manage environmental impacts properly, is considering the current condition and correct evaluation of products. In this paper a new method is presented to evaluate the refinery products degree of greenness. The developed model consists of influencing parameters on greenness degree, which are classified in a hierarchical structure. Due to the difficulties in defining an explicit function, fuzzy controllers are implemented to infer degree of greenness based on influencing parameters values. Effectiveness of the presented model is evaluated by assessing the greenness degree of Bandar Abbas refinery products and the results reflect good performance of the developed model.
