Optimization of Setup Planning by Combined Permutation-Based and Simulated Annealing Algorithms

Several different setups may be introduced as candidate options in a process plan for manufacturing a workpiece. It is required to employ an efficient optimization method to decide on the most competent setups. In the existing methods for optimizing the process plan, the main problem is the large search space size, which reduces the speed of finding the optimal solution or causes getting caught in the local minima. To remedy this problem, an optimization method has been developed in the present study based on a combined approach by employing the simulated annealing and the permutation-based setup planning algorithms. A new cost function has been introduced in this approach. The proposed method also has other advantages and capabilities in line with minimizing the manufacturing error and obtaining a uniquely optimized solution. A comparative study has been conducted to illustrate the capability of the proposed method. The results indicate that the rate of convergence to the optimal solution increases and the performance in finding the solution improves by using the method described in this paper.