Solving the redundancy allocation problem using a combined neural network/genetic algorithm approach
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Genetic algorithm for non-linear mixed integer programming problems and its applications
Computers and Industrial Engineering
Reliability optimal design problem with interval coefficients using hybrid genetic algorithms
Proceedings of the 23rd international conference on on Computers and industrial engineering
Computers and Industrial Engineering
Multi-level redundancy optimization in series systems
Computers and Industrial Engineering - Special issue: Selected papers from the 27th international conference on computers & industrial engineering
An efficient heuristic for series-parallel redundant reliability problems
Computers and Operations Research
A knowledge management system for series-parallel availability optimization and design
Expert Systems with Applications: An International Journal
Improvement of 3P and 6R mechanical robots reliability and quality applying FMEA and QFD approaches
Robotics and Computer-Integrated Manufacturing
Computers and Operations Research
Fault identification for robot manipulators
IEEE Transactions on Robotics
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The objective of the study is to compute various reliability parameters for multi-robotic system, using Real Coded Genetic Algorithms (RCGAs) and Fuzzy Lambda-Tau Methodology (FLTM). The paper contains a new idea about the reliability analysis of robotic system. The optimal values of mean time between failures (MTBF) and mean time to repair (MTTR) are obtained using GAs. Petri Net (PN) tool is applied to represent the interactions among the working components of multi-robotic system. To enhance the relevance of the reliability study, triangular fuzzy numbers (TFNs) are developed from the computed data, using possibility theory. The use of fuzzy arithmetic in the PN model increases the flexibility for application to various systems and conditions. Various reliability parameters, namely failure rate, repair time, MTBF, expected number of failures (ENOF), reliability and availability, are computed using FLTM. Sensitivity analysis has also been performed and the effects on system MTBF are addressed. The adopted methodology improves the shortcomings/drawbacks of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation. The analysis presented, may be helpful for the system analyst to analyze and predict the system behavior and to reallocate the required resources.