A Genetic Algorithm with Monte-Carlo Simulation for an Optimal Inspection Allocation in a Batch Assembly Line with Tolerance Stack-up

In this thesis, the total inspection policy cost of a batch assembly line in the multi-stage production system (MSPS) is optimized by using the Genetic Algorithm with Monte Carlo simulation. Total inspection policy cost (consist of inspection, rework and penalty costs) can be optimized by allocating different inspection strategies without compromising the quality of the final product. Inspection (Full, Sample, No inspection) is allocated at each station in such a way as to reduce the total inspection policy cost. As far as concerning tolerance stack-up which mainly depends on optimizing the limits (lower and upper inspection limits), the Genetic Algorithm is trying to optimize the limits as closely as possible to reduce the penalty cost. In multi quality characteristics problem if the part fails in any of the impacted quality characteristics, it goes for rework and reworks cost is added depends on part fails in which quality characteristics.