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Game theory based solution selection for multi-objective redundancy allocation in interval-valued problem parameters
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DOI：10.1016/j.ress.2020.106932.png)
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Abstract
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This paper presents a multi-objective redundancy allocation problem (MORAP) for maximizing system reliability and simultaneously minimizing system cost with limitations on system entropy, which is essential for achieving system stability and sustainability. Entropy is an important part of this new model because it provides a measure of randomness, which should be limited to avoid risky solutions. Both component reliability estimates and component cost estimates are considered to formulate the model in more realistic sense. In real-life MORAP, the Pareto optimal set can be extremely large. A selection procedure based on the notion of game theory is then proposed to determine representative solutions for solving the problem. For MORAP, the game theoretical framework can select representative solutions effectively with higher system reliability, lower associated variance of the reliability estimate and higher system entropy. The validity and the performance of the proposed approach are tested through three numerical examples, and computational results are analyzed.
Keywords:
Reliability optimization
Multi-objective optimization
Entropy
Game theory
Interval numbers
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