Setup Optimization in High-Mix Surface Mount PCB Assembly Setup Optimization in High-Mix Surface Mount PCB Assembly

The thesis discusses machine setup problems which arise in high-mix, lowvolume production environments of printed circuit board assembly (PCB) industry. We concentrate especially on the hybrid setup problem that is a combination of the job grouping and minimum setup problems. We define an objective function that minimizes a weighted sum of the number of setup occasions and the number of feeder changeovers. The aim of this function is to model real-world production situation. With the help of the weighting parameter one can put more weight on the number of setup occasions or on feeder changes depending on the production planning situations. We describe the problem in an exact way by constructing an integer programming (IP) model. While there are problem instances of practical size which can be solved optimally by current software systems there are still cases where the problem solving with exact methods turns out to be inefficient and time consuming. Unfortunately, the hybrid problem is also so hard combinatorial problem that it can be solved optimally only in small problem instances even with the best exact methods. Therefore, we develop heuristics methods that help us to find near optimal solutions and sometimes even optimal solutions in an easy way. In our publications we present several new algorithms for the problem and compare their efficiency with other methods found in literature. Furthermore, we give a general model that can be applied to construct an efficient hybrid setup heuristic.