This paper studies the reliability evaluation of a stochastic manufacturing system with multiple production lines in parallel. Multiple repairs and different failure rates, never simultaneously addressed in earlier wo...This paper studies the reliability evaluation of a stochastic manufacturing system with multiple production lines in parallel. Multiple repairs and different failure rates, never simultaneously addressed in earlier works, are taken into account. First, a revised graphical methodology integrating transformation and decomposition is utilized to construct the stochastic manufacturing system as a multi-state manufacturing network （MSMN）. In particular, a ＂predecessor-set＂ technique is proposed to deal with multiple repairs. An algorithm is proposed to generate the lowest capacity vectors （LCVs） that stations should provide to satisfy the workloads. Subsequently, the system reliability of the MSMN, which is defined as the probability of demand satisfaction, is calculated in terms of the LCVs. A real case of a printed circuit board manufacturing system is utilized to demonstrate how the system reliability can be evaluated. A further decision making issue is addressed based on the derived system reliability.展开更多
基金supported in part by the Ministry of Science and Technology of Taiwan under Grant No.MOST 102-2221-E-011-080-MY3supported by Ministry of Science and Technology of Taiwan under Grant No.MOST 103-2218-E-011-010-MY3
文摘This paper studies the reliability evaluation of a stochastic manufacturing system with multiple production lines in parallel. Multiple repairs and different failure rates, never simultaneously addressed in earlier works, are taken into account. First, a revised graphical methodology integrating transformation and decomposition is utilized to construct the stochastic manufacturing system as a multi-state manufacturing network （MSMN）. In particular, a ＂predecessor-set＂ technique is proposed to deal with multiple repairs. An algorithm is proposed to generate the lowest capacity vectors （LCVs） that stations should provide to satisfy the workloads. Subsequently, the system reliability of the MSMN, which is defined as the probability of demand satisfaction, is calculated in terms of the LCVs. A real case of a printed circuit board manufacturing system is utilized to demonstrate how the system reliability can be evaluated. A further decision making issue is addressed based on the derived system reliability.
