Improved crow search algorithm for optimal flexible manufacturing process planning

Abstract

The enhancement of manufacturing processes due to the growing demand for products and services is a problem that must be constantly updated. Several schemas have been conducted to improve the manufacturing processes. These schemas are based on the flexible use of machines, tools, and tool access directions to determine the best process planning (PP) for a specific piece or part. The combination of different tools and machines leads to the formulation of complicated combinatory problems that can be treated as optimization problems. On the other hand, bioinspired optimization techniques have been widely employed for solving several complex engineering and real-life problems showing competitive results. The crow search algorithm (CSA) is an optimization technique based on the intelligent foraging behavior of crows. Our work uses an improved CSA (ICSA) to determine the optimal flexible process planning (FPP) with a low computational effort. Several AND/OR networks FPP problems are employed in the experimentation stage, where the production cost and production time are considered as fitness functions. To determine the performance and accuracy of the proposed technique, different results previously reported in the literature are used for a comparison showing that ICSA-FPP is able to outperform similar approaches.