Execution of revised BMIM similarity coefficient for part family formation in reconfigurable manufacturing system DOI
Gaurav Kumar, Kapil Kumar Goyal, Neera Batra

et al.

Journal of Adhesion Science and Technology, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 23

Published: Oct. 9, 2024

The reconfigurable manufacturing system (RMS) is an advanced strategy that enables precise adjustment of functionality and capacity to meet fluctuating demands economically. RMS focuses on part families, allowing configurations be adapted for new requirements. Optimizing flow line design produce various parts involves minimizing reconfigurations associated costs by enhancing operation sequence similarity. This article proposes a novel optimization using the Longest Common Subsequence (LCS) method reduce bypassing moves machine idle times. study introduces similarity coefficient derived from LCS employs average linkage hierarchical clustering categorize in case study. Unlike traditional methods, this approach considers material movements both before initial after final processing station, addressing gaps move calculations. impact different weighting scenarios Type-II (ω) idleness (β) was examined. For example, with weights {1.0, 0.6, 0.3, 0.0} equal weightings (α) set at 0.5, threshold value 0.3 results eight clusters, such as Cluster 1 {1, 11, 10, 12} 3 {3, 5, 6, 4, 15, 9, 13, 14, 7, 8}. Lower values lead fewer clusters larger sizes, indicating more consolidated family grouping. Various handling demonstrate how affect sizes. enhances efficiency integrating comprehensive considerations optimizing based operational similarities.

Language: Английский

A multi-dimensional co-evolutionary algorithm for multi-objective resource-constrained flexible flowshop with robotic transportation DOI
Jiake Li,

Rong-hao Li,

Junqing Li

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: 170, P. 112689 - 112689

Published: Jan. 2, 2025

Language: Английский

Citations

3

An optimal workflow scheduling in IoT-fog-cloud system for minimizing time and energy DOI Creative Commons

Roqia Rateb,

Ahmed Adnan Hadi,

Venkata Mohit Tamanampudi

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 29, 2025

Today, with the increasing use of Internet Things (IoT) in world, various workflows that need to be stored and processed on computing platforms. But this issue, causes an increase costs for resources providers, as a result, system Energy Consumption (EC) is also reduced. Therefore, paper examines workflow scheduling problem IoT devices fog-cloud environment, where reducing EC MakeSpan Time (MST) main objectives, under constraints priority, deadline reliability. order achieve these combination Aquila Salp Swarm Algorithms (ASSA) used select best Virtual Machines (VMs) execution workflows. So, each iteration ASSA execution, number VMs are selected by ASSA. Then using Reducing (RMST) technique, MST reduced, while maintaining reliability deadline. Then, VM merging Dynamic Voltage Frequency Scaling (DVFS) technique output from RMST, static dynamic respectively. Experimental results show effectiveness proposed method compared previous methods.

Language: Английский

Citations

3

A Q-learning driven multi-objective evolutionary algorithm for worker fatigue dual-resource-constrained distributed hybrid flow shop DOI
Haonan Song, Junqing Li,

Zhaosheng Du

et al.

Computers & Operations Research, Journal Year: 2024, Volume and Issue: unknown, P. 106919 - 106919

Published: Nov. 1, 2024

Language: Английский

Citations

9

Ship pipe production optimization method for solving distributed heterogeneous energy-efficient flexible flowshop scheduling with mobile resource limitation DOI
Hua Xuan, Xiaofan Zhang, Yixuan Wu

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126603 - 126603

Published: Jan. 1, 2025

Language: Английский

Citations

1

Evolving chimp optimization algorithm using quantum mechanism for engineering applications: A case study on fire detection DOI Creative Commons
Ziyang Zhang, Mohammad Khishe,

Leren Qian

et al.

Journal of Computational Design and Engineering, Journal Year: 2024, Volume and Issue: 11(5), P. 143 - 163

Published: Aug. 16, 2024

Abstract This paper introduces the Quantum Chimp Optimization Algorithm (QU-ChOA), which integrates (ChOA) with quantum mechanics principles to enhance optimization capabilities. The study evaluates QU-ChOA across diverse domains, including benchmark tests, IEEE CEC-06–2019 100-Digit Challenge, real-world problems from IEEE-CEC-2020, and dynamic scenarios IEEE-CEC-2022. Key findings highlight QU-ChOA’s competitive performance in both unimodal multimodal functions, achieving an average success rate (SR) of 88.98% various functions. demonstrates robust global search abilities, efficiently finding optimal solutions fitness evaluations (AFEs) 14 012 calculation duration 58.22 units fire detection applications. In outperforms traditional algorithms, a perfect SR 100% Challenge for several underscoring its effectiveness complex numerical optimization. Real-world applications significant improvements objective function values industrial processes, showcasing versatility applicability practical scenarios. identifies gaps existing strategies positions as novel solution these challenges. It advancements, such 20% reduction AFEs compared methods, illustrating efficiency different tasks. These results establish promising tool addressing intricate fields.

Language: Английский

Citations

7

A novel balanced teaching-learning-based optimization algorithm for optimal design of high efficiency plate-fin heat exchanger DOI
He Dong, Zhile Yang, Hangcheng Yu

et al.

Applied Thermal Engineering, Journal Year: 2024, Volume and Issue: 256, P. 124052 - 124052

Published: July 29, 2024

Language: Английский

Citations

6

A novel approach for energy consumption management in cloud centers based on adaptive fuzzy neural systems DOI
Hongwei Huang, Yu Wang,

Yue Cai

et al.

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(10), P. 14515 - 14538

Published: July 21, 2024

Language: Английский

Citations

4

An efficient algorithm for multi-objective structural optimization problems using an improved pbest-based differential evolution algorithm DOI

Truong-Son Cao,

Hoang-Anh Pham, Viet-Hung Truong

et al.

Advances in Engineering Software, Journal Year: 2024, Volume and Issue: 197, P. 103752 - 103752

Published: Aug. 9, 2024

Language: Английский

Citations

4

Metaheuristics for multi-objective scheduling problems in industry 4.0 and 5.0: a state-of-the-arts survey DOI Creative Commons
Wenqiang Zhang,

Xuan Bao,

Xinchang Hao

et al.

Frontiers in Industrial Engineering, Journal Year: 2025, Volume and Issue: 3

Published: Jan. 27, 2025

The advent of Industry 4.0 and the emerging 5.0 have fundamentally transformed manufacturing systems, introducing unprecedented levels complexity in production scheduling. This is further amplified by integration cyber-physical Internet Things, Artificial Intelligence, human-centric approaches, necessitating more sophisticated optimization methods. paper aims to provide a comprehensive perspective on application metaheuristic algorithms shop scheduling problems within context 5.0. Through systematic review recent literature (2015–2024), we analyze categorize various including Evolutionary Algorithms (EAs), swarm intelligence, hybrid methods, that been applied address complex challenges smart environments. We specifically examine how these handle multiple competing objectives such as makespan minimization, energy efficiency, costs, human-machine collaboration, which are crucial modern industrial settings. Our survey reveals several key findings: 1) metaheuristics demonstrate superior performance handling multi-objective compared standalone algorithms; 2) bio-inspired show promising results addressing environments; 3) tri-objective higher-order warrant in-depth exploration; 4) there an trend towards incorporating human factors sustainability optimization, aligned with principles. Additionally, identify research gaps propose future directions, particularly areas real-time adaptation, sustainability-aware algorithms. provides insights for researchers practitioners field scheduling, offering structured understanding current methodologies evolution from

Language: Английский

Citations

0

Extended material requirement planning (MRP) within a hybrid energy-enabled smart production system DOI
Rekha Guchhait,

Mitali Sarkar,

Biswajit Sarkar

et al.

Journal of Industrial Information Integration, Journal Year: 2024, Volume and Issue: unknown, P. 100717 - 100717

Published: Oct. 1, 2024

Language: Английский

Citations

3