Journal Article10.1016/j.jclepro.2022.135738
An improved multi-objective firefly algorithm for energy-efficient hybrid flowshop rescheduling problem
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TL;DR: In this paper , an improved multi-objective firefly algorithm is proposed to optimize the production efficiency, energy consumption and production stability in a hybrid flow shop rescheduling problem under the machine breakdown.
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About: This article is published in Journal of Cleaner Production. The article was published on 01 Dec 2022. The article focuses on the topics: Firefly algorithm & Computer science.
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Citations
Multi-objective energy-efficient hybrid flow shop scheduling using Q-learning and GVNS driven NSGA-II
TL;DR: This study proposes a multi-objective energy-efficient hybrid flow shop scheduling model and a novel Q-learning and GVNS driven NSGA-II algorithm to minimize total tardiness, energy cost, and carbon trading cost, outperforming existing methods in terms of Pareto solutions and computational efficiency.
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Multimodal hierarchical distributed multi-objective moth intelligence algorithm for economic dispatch of power systems
Linfei Yin,Zhenjian Cai +1 more
TL;DR: A multimodal hierarchical distributed multi-objective moth intelligence algorithm is proposed for economic dispatch in power systems, improving computational efficiency, accelerating computation speed, and achieving coordinated optimization, outperforming traditional methods in cost and carbon emission reduction.
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Multi-Objective Optimization for Controlling the Dynamics of the Diabetic Population
Karim El Moutaouakil,A. El Ouissari,Vasile Palade,Anas Charroud,Adrian Olaru,H. Baïzri,Saliha Chellak,Mouna Cheggour +7 more
TL;DR: In this article , a multi-objective approach consisting of four steps is introduced to model the problem of controlling the diabetic population dynamics using a multiobjective mathematical model, discretizing the model using the trapezoidal rule and the Euler-Cauchy method, and using swarm-intelligence-based optimizers to solve the model and structuring the set of controls using soft clustering methods, known for their flexibility.
A two-step scheduling and rescheduling framework for integrated production and usage-based maintenance planning under TOU electricity tariffs: A case study of the tile industry
TL;DR: In this paper , a two-step framework is proposed which can also be applied to parallel machines in make-to-order environments to provide an easily implementable and cost-effective solution for reducing energy expenses, improving slurry quality, mitigating system instability due to random breakdowns, and minimizing maintenance frequencies in the ball mills of the tile industry.
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Firefly Algorithm with Tabu Search to Solve the Vehicle Routing Problem with Minimized Fuel Emissions: Case Study of Canned Fruits Transport
TL;DR: The proposed FA-TS algorithm effectively minimized fuel consumption, cost, and GHG emissions for canned fruit transport, achieving significant reductions compared to existing approaches.
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References
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
Firefly algorithms for multimodal optimization
Xin-She Yang
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TL;DR: In this article, a new Firefly Algorithm (FA) was proposed for multimodal optimization applications. And the proposed FA was compared with other metaheuristic algorithms such as particle swarm optimization (PSO).
Industry 4.0 and Industry 5.0—Inception, conception and perception
TL;DR: In this paper, the authors discuss the co-existence of two industrial revolutions, namely Industry 4.0 and Industry 5.0, and present five questions that need to be answered.
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Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions
TL;DR: An energy-aware multi-objective optimization algorithm for solving the hybrid flow shop (HFS) scheduling problem with consideration of the setup energy consumptions with the highly effective proposed EA-MOA algorithm compared with several efficient algorithms from the literature.
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Carbon-efficient scheduling of flow shops by multi-objective optimization
Jian-Ya Ding,Shiji Song,Cheng Wu +2 more
TL;DR: Numerical computations show that the energy-saving module of the extended NEH-Insertion Procedure in MONEH and MMOIG significantly helps to improve the discovered front and the proposed algorithms perform more effectively than other tested high-performing meta-heurisitics in searching for non-dominated solutions.
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