Inspection allocation in manufacturing systems using stochastic search techniques

Quality is the hallmark of a competitive product. It is necessary to use inspection stations to check product quality and process performance. In this paper, we are concerned with the problem of location of inspection stations in a multistage manufacturing system. We present two stochastic search algorithms for solving this problem, one based on Simulated Annealing and the other on Genetic Algorithms. These algorithms are developed to determine the location of inspection stations resulting in a minimum expected total cost in a multistage manufacturing system. The total cost includes inspection, processing and scrapping cost at each stage of the production process. A penalty cost is also included in it to account for a defective item which is not detected by the inspection scheme. A set of test examples are solved using these algorithms. We also compare performance of these two algorithms.