An improved genetic algorithm for the cellular manufacturing system planning

Cellular manufacturing system planning is a combinatorial optimization problem. Because of its nonlinearity, NP-Complete, and other complex characteristics, it can not be easily solved by conventional methods. Development of modern computing technology provides the possibility. This paper introduces the grouping efficiency and performance indicators to measure the merits of system planning. By focusing on the fuzzy nature of processing paths exised in either the whole machine or in parts, an improved genetic algorithm based on fuzzy theory is proposed to achieve cellular manufacturing system planning. This algorithm uses the fuzzy relation matrix coding of chromosomes to narrow the search space. Simulation results showed the effectiveness of the designed algorithm.