Using BUA algorithm to solve a sequential pick and place problem

There has been always a high demand for process optimization in industry. Different mathematical and heuristic approaches have been used and developed for process optimization. Generally, the purpose of the process optimization is to maximize the throughput of the process under study, and also to minimize the cost associated with this process. In most cases, researchers study processes and analyze them in order to find the parameters that will optimize the system. In fact, some systems are harder to analyze and optimize compared to other systems. In this paper, a new optimization algorithm is introduced. The proposed algorithm is named Best Uniform Algorithm (BUA). This algorithm is a meta-heuristic approach that generates a set of random solutions randomly from the entire search space, and the best solution is then used to generate other solutions. The BUA algorithm is designed to optimize the process in the sequential type of pickup and place machines. There are multiple stationary feeders in the sequential machine, and each feeder is assumed to store the same type of components. The head usually moves to pick up components from feeders and place them into different places on the PCB based on the design. The head starts from a fixed place, and then it moves to pick up one component from one of the feeders and place it on the PCB. Afterwards, the head moves to pick up the next component from the feeder that stores it. The process will continue until all the components are placed. In order to optimize this process, that distance that the head must travel until all the components are placed need to be optimized. The optimal parameters that optimize the total distance will be also found; these parameters will include the position of the feeders and the sequence of the components that need to be placed. Throughout this paper, the new optimization algorithm is introduced and the methodology is discussed. Next, this algorithm is applied to the sequential pickup and place machine in order to optimize the distance that the placement head travels. Finally, the results of applying this algorithm to the pick and place optimization problem are elaborated and compared to the well-known Genetic Algorithm (GA).