Simulated annealing and ant colony optimization algorithms for the dynamic throughput maximization problem

In many industries, inspection data is determined to merely serve for verification and validation purposes. It is rarely used to directly enhance the product quality because of the lack of approaches and difficulties of doing so. Given that a batch of subassembly items have been inspected, it is sometimes more profitable to exploit the data of the measured features of the subassemblies in order to further reduce the variation in the final assemblies so the rolled yield throughput is maximized. This can be achieved by selectively and dynamically assembling the subassemblies so we can maximize the throughput of the final assemblies. In this paper, we introduce and solve the dynamic throughput maximization (DTM) problem. The problem is found to have grown substantially by increasing the size of the assembly (number of subassembly groups and number of items in each group). Therefore, we resort to five algorithms: simple greedy sorting algorithm, two simulated annealing (SA) algorithms and two ant colony optimization (ACO) algorithms. Numerical examples have been solved to compare the performances of the proposed algorithms. We found that our ACO algorithms generally outperform the other algorithms.