TY  - JOUR
T1  - A Discrete Particle Swarm Optimization for Cellular Manufacturing System
AU - Kamalakannan, R. AU - Pandian, R. Sudhakara AU - Sornakumar, T. AU - Mahapatra, S.S. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 17
SP  - 3287
EP  - 3295
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.3287.3295
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.3287.3295
KW  - Cellular manufacturing
KW  -part-machine grouping
KW  -particle swarm optimization
KW  -investigators
KW  -established
AB  - Group Technology (GT) is a helpful approach to expand productivity with a high caliber in cell manufacturing frameworks in which cell development is a key stride to the GT theory. The cell development problem is considered as a major issue by many of the investigators who utilize binary machine part occurrence matrix that is formed by the course sheet in the cell manufacturing system. The ones that are present in the binary matrix symbolize the visit of the components to the corresponding machines and the zeros that are represented as components of non-visit. The present study addresses the problem of assembling the cell development through the Discrete Particle Swarm Optimization (DPSO) algorithm. Particle Swarm Optimization (PSO) is a population-based evolutionary algorithm that approaches a social manner of the swarm. The condition used to cluster the machines and components in cells is based upon the minimization of exceptional elements and voids. In this study, we utilized the permutation predicated representation for the encoding scheme for particle position representation. The proposed algorithm performance is verified over the issues that are formed from the open literature and the results that are obtained is then compared with that of benchmark issues which are established from the literature.
ER  -