SHERFUDEEN, Sheik Sulaiman ;ATHINAMILAGI, Muthiah ;VENKATARAMANUJAM, Janakiraman . Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 68, n.11, p. 683-692, october 2022. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/optimization-techniques-for-green-layout-design-in-manufacturing-industries-a-meta-heuristic-analysis/>. Date accessed: 16 apr. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2022.321.
Sherfudeen, S., Athinamilagi, M., & Venkataramanujam, J. (2022). Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis. Strojniški vestnik - Journal of Mechanical Engineering, 68(11), 683-692. doi:http://dx.doi.org/10.5545/sv-jme.2022.321
@article{sv-jmesv-jme.2022.321,
author = {Sheik Sulaiman Sherfudeen and Muthiah Athinamilagi and Janakiraman Venkataramanujam},
title = {Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {68},
number = {11},
year = {2022},
keywords = {adaptive salp swarm optimization; bi-objective function; emission; evolutionary computation; layout design; total handling costs; },
abstract = {Many research papers and much legislation has been published in recent years to control or reduce factory pollution. However, only a few articles have discussed pollution from manufacturing facilities, specifically shop floors, even though this a specific single objective problem. In this research framework, a new variant technique of the jelly fish concept adaptive salp swarm optimization (ASSO) with a familiar Lagrangian relaxation model for lowering Total Material Handling Costs (TMHC) and carbon dioxide (CO2) emissions is presented. Using the Mat Lab software and the improved ASSO, the dragon fly optimization (DFO) algorithm technique, experimental simulations of the existing and recognized design of the studied industry were performed. The simulation results were validated and compared to those of other optimization techniques such as ant bee colony (ABC), simulated annealing (SA), and genetic algorithm (GA). It was determined that the proposed methodology, ASSO, was the most efficient, resulting in 40 % reduction compared to ABC, 38 % DFO, 50 % SA, and 40 % GA in the lowest TMHC, as well as an average 20 % reduction of emission rate in green layout design. These techniques could be combined into a hybrid format for further reduction of the emission rate up to 80 %.},
issn = {0039-2480}, pages = {683-692}, doi = {10.5545/sv-jme.2022.321},
url = {https://www.sv-jme.eu/article/optimization-techniques-for-green-layout-design-in-manufacturing-industries-a-meta-heuristic-analysis/}
}
Sherfudeen, S.,Athinamilagi, M.,Venkataramanujam, J. 2022 October 68. Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 68:11
%A Sherfudeen, Sheik Sulaiman %A Athinamilagi, Muthiah %A Venkataramanujam, Janakiraman %D 2022 %T Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis %B 2022 %9 adaptive salp swarm optimization; bi-objective function; emission; evolutionary computation; layout design; total handling costs; %! Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis %K adaptive salp swarm optimization; bi-objective function; emission; evolutionary computation; layout design; total handling costs; %X Many research papers and much legislation has been published in recent years to control or reduce factory pollution. However, only a few articles have discussed pollution from manufacturing facilities, specifically shop floors, even though this a specific single objective problem. In this research framework, a new variant technique of the jelly fish concept adaptive salp swarm optimization (ASSO) with a familiar Lagrangian relaxation model for lowering Total Material Handling Costs (TMHC) and carbon dioxide (CO2) emissions is presented. Using the Mat Lab software and the improved ASSO, the dragon fly optimization (DFO) algorithm technique, experimental simulations of the existing and recognized design of the studied industry were performed. The simulation results were validated and compared to those of other optimization techniques such as ant bee colony (ABC), simulated annealing (SA), and genetic algorithm (GA). It was determined that the proposed methodology, ASSO, was the most efficient, resulting in 40 % reduction compared to ABC, 38 % DFO, 50 % SA, and 40 % GA in the lowest TMHC, as well as an average 20 % reduction of emission rate in green layout design. These techniques could be combined into a hybrid format for further reduction of the emission rate up to 80 %. %U https://www.sv-jme.eu/article/optimization-techniques-for-green-layout-design-in-manufacturing-industries-a-meta-heuristic-analysis/ %0 Journal Article %R 10.5545/sv-jme.2022.321 %& 683 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 68 %N 11 %@ 0039-2480 %8 2022-10-03 %7 2022-10-03
Sherfudeen, Sheik Sulaiman, Muthiah Athinamilagi, & Janakiraman Venkataramanujam. "Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis." Strojniški vestnik - Journal of Mechanical Engineering [Online], 68.11 (2022): 683-692. Web. 16 Apr. 2024
TY - JOUR AU - Sherfudeen, Sheik Sulaiman AU - Athinamilagi, Muthiah AU - Venkataramanujam, Janakiraman PY - 2022 TI - Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2022.321 KW - adaptive salp swarm optimization; bi-objective function; emission; evolutionary computation; layout design; total handling costs; N2 - Many research papers and much legislation has been published in recent years to control or reduce factory pollution. However, only a few articles have discussed pollution from manufacturing facilities, specifically shop floors, even though this a specific single objective problem. In this research framework, a new variant technique of the jelly fish concept adaptive salp swarm optimization (ASSO) with a familiar Lagrangian relaxation model for lowering Total Material Handling Costs (TMHC) and carbon dioxide (CO2) emissions is presented. Using the Mat Lab software and the improved ASSO, the dragon fly optimization (DFO) algorithm technique, experimental simulations of the existing and recognized design of the studied industry were performed. The simulation results were validated and compared to those of other optimization techniques such as ant bee colony (ABC), simulated annealing (SA), and genetic algorithm (GA). It was determined that the proposed methodology, ASSO, was the most efficient, resulting in 40 % reduction compared to ABC, 38 % DFO, 50 % SA, and 40 % GA in the lowest TMHC, as well as an average 20 % reduction of emission rate in green layout design. These techniques could be combined into a hybrid format for further reduction of the emission rate up to 80 %. UR - https://www.sv-jme.eu/article/optimization-techniques-for-green-layout-design-in-manufacturing-industries-a-meta-heuristic-analysis/
@article{{sv-jme}{sv-jme.2022.321},
author = { Sherfudeen, S., Athinamilagi, M., Venkataramanujam, J.},
title = {Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {68},
number = {11},
year = {2022},
doi = {10.5545/sv-jme.2022.321},
url = {https://www.sv-jme.eu/article/optimization-techniques-for-green-layout-design-in-manufacturing-industries-a-meta-heuristic-analysis/}
}
TY - JOUR AU - Sherfudeen, Sheik Sulaiman AU - Athinamilagi, Muthiah AU - Venkataramanujam, Janakiraman PY - 2022/10/03 TI - Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 68, No 11 (2022): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2022.321 KW - adaptive salp swarm optimization, bi-objective function, emission, evolutionary computation, layout design, total handling costs, N2 - Many research papers and much legislation has been published in recent years to control or reduce factory pollution. However, only a few articles have discussed pollution from manufacturing facilities, specifically shop floors, even though this a specific single objective problem. In this research framework, a new variant technique of the jelly fish concept adaptive salp swarm optimization (ASSO) with a familiar Lagrangian relaxation model for lowering Total Material Handling Costs (TMHC) and carbon dioxide (CO2) emissions is presented. Using the Mat Lab software and the improved ASSO, the dragon fly optimization (DFO) algorithm technique, experimental simulations of the existing and recognized design of the studied industry were performed. The simulation results were validated and compared to those of other optimization techniques such as ant bee colony (ABC), simulated annealing (SA), and genetic algorithm (GA). It was determined that the proposed methodology, ASSO, was the most efficient, resulting in 40 % reduction compared to ABC, 38 % DFO, 50 % SA, and 40 % GA in the lowest TMHC, as well as an average 20 % reduction of emission rate in green layout design. These techniques could be combined into a hybrid format for further reduction of the emission rate up to 80 %. UR - https://www.sv-jme.eu/article/optimization-techniques-for-green-layout-design-in-manufacturing-industries-a-meta-heuristic-analysis/
Sherfudeen, Sheik Sulaiman, Athinamilagi, Muthiah, AND Venkataramanujam, Janakiraman. "Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 68 Number 11 (03 October 2022)
Strojniški vestnik - Journal of Mechanical Engineering 68(2022)11, 683-692
© The Authors 2022. CC BY 4.0 Int.
Many research papers and much legislation has been published in recent years to control or reduce factory pollution. However, only a few articles have discussed pollution from manufacturing facilities, specifically shop floors, even though this a specific single objective problem. In this research framework, a new variant technique of the jelly fish concept adaptive salp swarm optimization (ASSO) with a familiar Lagrangian relaxation model for lowering Total Material Handling Costs (TMHC) and carbon dioxide (CO2) emissions is presented. Using the Mat Lab software and the improved ASSO, the dragon fly optimization (DFO) algorithm technique, experimental simulations of the existing and recognized design of the studied industry were performed. The simulation results were validated and compared to those of other optimization techniques such as ant bee colony (ABC), simulated annealing (SA), and genetic algorithm (GA). It was determined that the proposed methodology, ASSO, was the most efficient, resulting in 40 % reduction compared to ABC, 38 % DFO, 50 % SA, and 40 % GA in the lowest TMHC, as well as an average 20 % reduction of emission rate in green layout design. These techniques could be combined into a hybrid format for further reduction of the emission rate up to 80 %.