TY - JOUR
T1 - Solving the manufacturing cell design problem through binary cat swarm optimization with dynamic mixture ratios
AU - Soto, Ricardo
AU - Crawford, Broderick
AU - Toledo, Angelo Aste
AU - De la Fuente-Mella, Hanns
AU - Castro, Carlos
AU - Paredes, Fernando
AU - Olivares, Rodrigo
N1 - Publisher Copyright:
Copyright © 2019 Ricardo Soto et al. This is an open access article distributed under the Creative Commons Attribution License
PY - 2019
Y1 - 2019
N2 - In this research, we present a Binary Cat Swarm Optimization for solving the Manufacturing Cell Design Problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through Cat Swarm Optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the Autonomous Search algorithm was developed with dynamic mixture ratios. The experimental results for both normal Binary Cat Swarm Optimization (BCSO) and Autonomous Search BCSO reach all global optimums, both for a set of 90 instances with known optima, and for a set of 35 new instances with 13 known optima.
AB - In this research, we present a Binary Cat Swarm Optimization for solving the Manufacturing Cell Design Problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through Cat Swarm Optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the Autonomous Search algorithm was developed with dynamic mixture ratios. The experimental results for both normal Binary Cat Swarm Optimization (BCSO) and Autonomous Search BCSO reach all global optimums, both for a set of 90 instances with known optima, and for a set of 35 new instances with 13 known optima.
UR - http://www.scopus.com/inward/record.url?scp=85061665887&partnerID=8YFLogxK
U2 - 10.1155/2019/4787856
DO - 10.1155/2019/4787856
M3 - Article
C2 - 30906316
AN - SCOPUS:85061665887
SN - 1687-5265
VL - 2019
JO - Computational Intelligence and Neuroscience
JF - Computational Intelligence and Neuroscience
M1 - 4787856
ER -