TY - CHAP
T1 - Applying an electromagnetism-like algorithm for solving the manufacturing cell design problem
AU - Lanza-Gutierrez, Jose M.
AU - Soto, Ricardo
AU - Crawford, Broderick
AU - Gomez-Pulido, Juan A.
AU - Fernandez, Nicolas
AU - Castillo, Carlos
N1 - Publisher Copyright:
© 2017, IGI Global. All rights reserved.
PY - 2017/3/9
Y1 - 2017/3/9
N2 - Group technology has acquired a great consideration in the last years. This technique allows including the advantages of serial production to any manufacturing industry by dividing a manufacturing plant into a set of machine-part cells. The identification and formation of the cells are known as the Manufacturing Cell Design Problem (MCDP), which is an NP-hard problem. In this paper, the authors propose to solve the problem through a swarm intelligence metaheuristic called ElectroMagnetism-like (EM-like) algorithm, which is inspired by the attraction-repulsion mechanism of particles in the context of the electromagnetic theory. The original EM-like algorithm was designed for solving continuous optimization problems, while the MCDP is usually formulated by assuming a binary approach. Hence, the authors propose an adaptation of this algorithmfor addressing theproblem. Such adaptation is applied for solving a freely available dataset of the MCDP, obtaining competitive results compared to recent approaches.
AB - Group technology has acquired a great consideration in the last years. This technique allows including the advantages of serial production to any manufacturing industry by dividing a manufacturing plant into a set of machine-part cells. The identification and formation of the cells are known as the Manufacturing Cell Design Problem (MCDP), which is an NP-hard problem. In this paper, the authors propose to solve the problem through a swarm intelligence metaheuristic called ElectroMagnetism-like (EM-like) algorithm, which is inspired by the attraction-repulsion mechanism of particles in the context of the electromagnetic theory. The original EM-like algorithm was designed for solving continuous optimization problems, while the MCDP is usually formulated by assuming a binary approach. Hence, the authors propose an adaptation of this algorithmfor addressing theproblem. Such adaptation is applied for solving a freely available dataset of the MCDP, obtaining competitive results compared to recent approaches.
UR - http://www.scopus.com/inward/record.url?scp=85027497650&partnerID=8YFLogxK
U2 - 10.4018/978-1-5225-2322-2.ch002
DO - 10.4018/978-1-5225-2322-2.ch002
M3 - Chapter
AN - SCOPUS:85027497650
SN - 1522523227
SN - 9781522523222
SP - 37
EP - 61
BT - Recent Developments in Intelligent Nature-Inspired Computing
PB - IGI Global
ER -