Neurally-inspired stochastic algorithm for determining multi-stage multi-attribute sampling inspection plans

Abstract

In manufacturing industries, sampling inspection is a common practice for quality assurance and cost reduction. The basic decisions in sampling inspection are how many manufactured items to be sampled from each lot and how many identified defective items in the sample to accept or reject each lot. Because of the combinatorial nature of alternative solutions on the sample sizes and acceptance criteria, the problem of determining an optimal sampling plan is NP-complete. In this paper, a neurally-inspired approach to generating acceptance sampling inspection plans is proposed. A Bayesian cost model of multi-stage-multi-attribute sampling inspections for quality assurance in serial production systems is formulated. This model can accommodate various dispositions of rejected lots such as scraping and screening. The model also can reflect the relationships between stages and among attributes. To determine the sampling plans based on the formulated model, a neurally-inspired stochastic algorithm is developed. This algorithm simulates the state transition of a primal-dual stochastic neural network to generate the sampling plans. The simulated primal network is responsible for generation of new states whereas the dual network is for recording the generated solutions. Starting with an arbitrary feasible solution, this algorithm is able to converge to a near optimal or an optimal sampling plan with a sequence of monotonically improved solutions. The operating characteristics and performance of the algorithm are demonstrated via numerical examples.