A modified particle swarm optimization for production planningproblems in the TFT Array process

In this study, a practical production planning problem in the TFT (thin film transistor) Array process is introduced. Several researchers have referred to the capacitated production lot-sizing allocation problems as NP-Hard. Naturally, it is harder to solve the capacitated production allocation problem considering its practical characteristics and constraints, such as allocation problems among bottleneck machines, photo masks, and products with different re-entrant layers. In response to this, we proposed a novel variation of the particle swarm optimization (PSO) model called the modified PSO (MPSO), which is a binary PSO model with dynamic inertia weight and mutation mechanism. It improves some weaknesses as opposed to the original version of the PSO, including a propensity for obstruction near the optimal solution regions that hardly improve solution quality by fine tuning. In addition, it is converted to be able to solve the model of binary decision variables. In order to illustrate effectiveness, the traditional PSO (TPSO), genetic algorithm (GA), and the proposed MPSO are compared by application of the literature’s well-known test problems as well as the practical production planning problem in the TFT Array process. Based on the results of the investigation, it can be concluded that the proposed MPSO is more effective than the other approaches in terms of superiority of solution and required CPU time.