Paper Info
Title
Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios.
Abstract
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.
Year
DOI
Venue
2019
10.1155/2019/4787856
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
Field
DocType
Volume
Mathematical optimization,Search algorithm,Pattern recognition,Manufacturing cell,Swarm behaviour,Computer science,Artificial intelligence,Tracing,Metaheuristic,Binary number
Journal
2019
ISSN
Citations 
PageRank 
1687-5265
0
0.34
References 
Authors
13
7
Name
Order
Citations
PageRank
Ricardo Soto119447.59
Broderick Crawford244673.74
Angelo Aste Toledo300.34
Hanns de la Fuente-Mella400.68
Carlos Castro525529.05
Fernando Paredes623027.21
Rodrigo Olivares7459.07