Application of efficient sfha and tlbo algorithms for cell formation problems in cellular manufacturing environment

Abstract

Cellular Manufacturing (CM) is a basic Group Technology (GT) application that has been utilized in a few true modern applications. The issue of cell arrangement is considered as the principal most critical criteria in the design of Cellular Manufacturing Systems (CMS), so as to limit lead time and setup time to amplify productivity and profitability. In this paper, an efficient Sheep Flock Heredity algorithm (SFHA) and another proficient optimization technique, called Teaching–Learning-Based Optimization (TLBO) are discussed for the advancement of cellular manufacturing issues. That simple genetic algorithm SFHA is referred as multi-level genetic operations, which can obtain good solutions. SFHA algorithm was generally based on the natural change of sheep in the flock. The procedure of Teaching Learning Based Optimization is partitioned into two sections: the initial segment comprises of 'Teacher Phase' and the second part comprises of the 'Learner Phase'. 'Teacher Phase' implies gaining knowledge from the teacher and 'Learner Phase' implies learning by the communication among learners. So in this paper, the main aim is applying SFHA �TLBO algorithms for Cell Formation problems in Cellular Manufacturing Environment. Graphical User Interface (GUI) is developed for both SFHA and TLBO algorithms to solve any size of Cell Formation problem within fraction of seconds to obtain cells with number of Exceptional Elements and Grouping Efficacy.