Design of Cellular Manufacturing System Using Non-Traditional Optimization Algorithms

Abstract

For the last many years a lot of study has been done on design of Cellular Manufacturing System (CMS). Cellular Manufacturing is an application of Group Technology (GT) philosophy in which similar parts are identified and grouped together to take advantage of their similarities in design and manufacturing. The design of CMS involves three stages i) grouping of parts and production equipments into cells (Cell Formation), ii) allocation of the machine cells to the areas within the shop floor and iii) layout of the machines within each cell. In recent years non-traditional optimization algorithms/techniques have fascinated scientists and engineers all over the world. Particularly in complex dynamic environments, these algorithms/techniques are needed to explore beyond the vicinity of existing knowledge. These algorithms have the ability to think and learn from own experience. They are called Meta heuristics because they perform considerable search before terminating to provide a good solution to the problem. Popular Meta heuristics are Genetic Algorithms (GA), Simulated Annealing (SA), Tabu Search (TS), Artificial Neural Networks (ANN), Artificial Immune System (AIS), and Sheep Flock Heredity Algorithm (SFHA). In this chapter the implementation of Meta heuristics for the design of Cell Formation problem in CMS is discussed.