A Novel Reliability Evaluation Technique for Stochastic-Flow Manufacturing Networks With Multiple Production Lines

This paper presents a novel technique to measure the performance of a stochastic-flow manufacturing network (SMN) which violates the so-called flow conservation law due to the failure rates of stations. We address the mission reliability, the probability of demand satisfaction, as a performance indicator for the SMN while considering both the stochastic capacities and the multiple production lines. First, we construct a manufacturing system as an SMN through a graphical transformation, and decompose the transformed SMN into several paths for further analysis. Subsequently, two algorithms for different scenarios are designed to generate all minimal capacity vectors that stations should provide to satisfy the given demand. The first scenario is for the SMN with identical production lines in parallel. The second scenario is for distinct production lines with common stations in the SMN. We derive the mission reliability in terms of minimal capacity vectors by applying the recursive sum of disjoint products (RSDP) algorithm. A decision making issue is also discussed to decide a reliable production strategy.