Multicriteria optimization of process plans for reconfigurable manufacturing systems An evolutionary approach

Abstract

Production systems have developed over the years due to changing environment, external and internal drivers and conditions like new technologies, developed products and customer needs. These needs were the main drivers for integrated and evolved manufacturing systems which can be more responsive and customer focused. Prototypes of manufacturing industries which have been recently introduces as flexible and reconfigurable manufacturing systems are responding to these recent needs in peculiar ways focusing not only on product design level but also on integrated manufacturing systems and process planning level. Optimization is central to any problem involving decision making whether in engineering or any other field of life. Manufacturing problems are considered as complex combinatorial problems involving non linear constraints and large solution search space. The area of optimization has received enormous attention in recent years, primarily because of the rapid development in computer technology, advance manufacturing methods and products. Many software packages now come with a special optimization tool like Optimization toolbox of MATLAB. When these techniques are used to solve the design problems in engineering then this becomes engineering optimization or design optimization. The aim is to fill the gap between theory of optimization and engineering practices. Since most of the engineering problems are NP hard problems which cannot be solved by conventional techniques that lead to the promotion of developing advance techniques and search methods in optimization. Evolutionary Algorithms is a class of evolutionary computation which uses mechanisms inspired by evolution. In this work, methodologies are presented to solve NP-Hard problem of process planning through evolutionary optimization using Genetic Algorithms and Cuckoo Search to generate and then find the optimized process plan for a part or part family. Furthermore, an approach has been provided in order to achieve reconfigurability, accommodating new feature in the already generated process plan and thus creating a hybrid between generative and variant process planning approach.