Optimization of high-speed multistation SMT placement machines using evolutionary algorithms

Surface mount technology (SMT) is a robust methodology that has been widely used in the past decade to produce circuit boards. Analyses of the SMT assembly line have shown that the automated placement machine is often the bottleneck, regardless of the arrangement of these machines (parallel or sequential) in the assembly line. Improving and automating the placement machine is a key issue for increasing SMT production line throughput. This paper presents experimental results using genetic algorithms to optimize the feeder slot assignment problem for a high-speed parallel, multistation SMT placement machine. Four crossover operators, four selection methods, and two probability settings are used in our experiments. A penalty function is used to handle constraints. A comparison of genetic algorithms with several other optimization methods (human experts, vendor supplied software, expert systems, and local search) is presented, which supports the use of genetic algorithms for this problem.