Genetic algorithm approach to modelling fractal manufacturing layout

Fractal Manufacturing System (FrMS) basically structurally builds up from units called 'fractals' or fractal objects which are independent entities and contain essential features and congenital attributes of the entire manufacturing configuration. They can selfadapt quickly to dynamic changes in an unpredictable manufacturing environment. They are also self regulating and fall under organizational work groups, each within its own area of competence. An optimal shop floor design and implementation is key and an integral part of achieving a successful FrMS. and is concerned with issues of shop floor planning, arrangement and function layout. The fractal shop floor layout develops from individual cells and is conceptually capable of producing a variety of products with minimal reconfiguration. Keen attention is paid to determination of capacity level, cell composition and flow distances of products. In this paper, Genetic Algorithm (GA) is adopted to efficiently and effectively design flexible FrMS shop floor layout, needed in agile manufacturing system to cope with new and dynamic manufacturing environments that need to adapt to changing products and technologies. Its stochastic search algorithm is used in simulating natural evolutionary process techniques, which in turn solves the many FrMS combinatorial optimization problems. The design implementation is done using MATLAB. The end result interestingly is a fault tolerant structure that is better suited to survive and stand the pressure for lead time reduction and inventories, product customization and challenges of a dynamic and unpredictable operational environment.