TY - JOUR
T1 - Solving the Manufacturing Cell Design Problem Using Human Behavior-Based Algorithm Supported by Autonomous Search
AU - SOTO DE GIORGIS, RICARDO JAVIER
AU - CRAWFORD LABRIN, BRODERICK
AU - Gonzalez, Francisco
AU - Vega, Emanuel
AU - Castro, Carlos
AU - Paredes, Fernando
N1 - Publisher Copyright:
© 2013 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - The manufacturing Cell Design Problem (MCDP) is a classical optimization problem that finds application in lines of manufacture. The problem consist in distributing machines in cells, where the parts processed by each machine travels in the production process in such a way that productivity is improved. To solve the MCDP we employ a novel metaheuristic, which is inspired by actions, attitudes, and conducts that people normally have in life, named Human behavior-based optimization (HBBO). An individual try to evolve in life by trying his best in order to be a better human being with a brilliant future, successful at life, and be an example for others. We couple the HBBO with Autonomous Search (AS), which allows the modification of internal components of our approach when exposed to changing external forces and opportunities. We compare our HBBO-AS with the classic HBBO and an implementation using IRace, which is a software package that allows us to automatize the configuration of an algorithm through automatic configuration procedures. Additionally, in order to test the competitiveness of our results, we compare with other algorithms proved to perform well solving the MCDP. We illustrate experimental results, where the proposed approach is able to obtain interesting performance and robustness in the 125 well-known instances of the MCDP.
AB - The manufacturing Cell Design Problem (MCDP) is a classical optimization problem that finds application in lines of manufacture. The problem consist in distributing machines in cells, where the parts processed by each machine travels in the production process in such a way that productivity is improved. To solve the MCDP we employ a novel metaheuristic, which is inspired by actions, attitudes, and conducts that people normally have in life, named Human behavior-based optimization (HBBO). An individual try to evolve in life by trying his best in order to be a better human being with a brilliant future, successful at life, and be an example for others. We couple the HBBO with Autonomous Search (AS), which allows the modification of internal components of our approach when exposed to changing external forces and opportunities. We compare our HBBO-AS with the classic HBBO and an implementation using IRace, which is a software package that allows us to automatize the configuration of an algorithm through automatic configuration procedures. Additionally, in order to test the competitiveness of our results, we compare with other algorithms proved to perform well solving the MCDP. We illustrate experimental results, where the proposed approach is able to obtain interesting performance and robustness in the 125 well-known instances of the MCDP.
KW - Autonomous search
KW - HBBO
KW - manufacturing cell design problem
KW - metaheuristics
UR - http://www.scopus.com/inward/record.url?scp=85077970686&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2940012
DO - 10.1109/ACCESS.2019.2940012
M3 - Article
AN - SCOPUS:85077970686
VL - 7
SP - 132228
EP - 132239
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 8827495
ER -