Heuristic optimization algorithm based line balancing in a fuzzy environment

Abstract

A,sembly Line Balancing (ALB) is one of the important problems of production management. As small improvemcnts in the performance of the ,ystem can lead to significant monetary consequences, it is of utmost importance to develop practical solutIon proccdures that yield high-quality design decisions wlth milllmal computalional requirements. Due to the NP-hard nature of the ALB problem, heuri5tlcs are gencrally used to solve rcallifc problems. The constraints and paranleters of fuzlY natllre exist in lme balancing problem,. Fuay optimization can be implemented effectively in solving ASLBP. Fuay sets or funy numbers can appropriatcly reprc~ent imprecisc paramcters, and can be mampulatcd through dilTerent operations on fuzzy ~etsor fuzzy numbers. Since imprecise parameters are trcaled as imprecise valucs instead of precise ones, the proccss will be more powerful and its results more credible. An cnieient heuristic to solve the tilzzy single-model ALB problem has been presented in this research work. The propo5ed heunstie is a Genetic Algorithm (GAl with a special chromosome structure that is efficient to handle fuz'l.yjob lime through the evolution process. Elitism is also implemented in the moJel by usmg illness function value. In this contcxt, the proposed approach can be viewed as a unified framework which combines sevcral new concepts of GA in the algorithmic design.