Journal Article10.3390/coatings14030368
A Decomposition-Based Multi-Objective Evolutionary Algorithm for Solving Low-Carbon Scheduling of Ship Segment Painting
1
TL;DR: A decomposition-based multi-objective evolutionary algorithm for solving low-carbon scheduling of ship segment painting effectively reduces carbon emissions and improves painting efficiency.
read more
Abstract: Ship painting, as one of the three pillars of the shipping industry, runs through the whole process of ship construction. However, there are low scheduling efficiency and excessive carbon emissions in the segmental painting process, and optimizing the scheduling method is an important means to achieve the sustainable development of the ship manufacturing industry. To this end, firstly, a low-carbon scheduling mathematical model for the segmented painting workshop is proposed, aiming to reduce carbon emissions and improve the painting efficiency of the segmented painting workshop. Second, an artificial bee colony algorithm designed based on a decomposition strategy (MD/ABC) is proposed to solve the model. In the first stage, five neighborhood switching methods are designed to achieve the global search employed for each solution. In the second stage, the Technique of Ordering the Ideal Solutions (TOPSIS) improves the competition mechanism through the co-evolution between neighboring subproblems and designs the angle to define the relationship between neighboring subproblems to enhance the localized search and improve population quality. The solution exchange strategy is used in the third stage to improve the efficiency of the algorithm. In addition, a two-stage coding method is designed according to the characteristics of the scheduling problem. Finally, the algorithm before and after the improvement and with other algorithms is analyzed using comparative numerical experiments. The experimental results show the effectiveness of the algorithm in solving the low-carbon scheduling problem of ship segmental painting and can provide reliable guidance for the scheduling program of segmented painting workshops in shipyards.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Correction: Bu et al. A Decomposition-Based Multi-Objective Evolutionary Algorithm for Solving Low-Carbon Scheduling of Ship Segment Painting. Coatings 2024, 14, 368
Abstract: In the original publication [...]
References
An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector
Hamidreza Eskandari,Qingyao Qiao,Hassan Saadatmand,Mohammad-Ali Sahraei +3 more
TL;DR: This study proposes an interpretable multi-stage forecasting framework for energy consumption and CO2 emissions in the UK's transportation sector, using SHAP to balance model accuracy and interpretability, identifying road carbon intensity as the most influential variable.
19
An improved multi-objective firefly algorithm for energy-efficient hybrid flowshop rescheduling problem
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.
19
A many-objective evolutionary algorithm based on rotation and decomposition
TL;DR: A many-objective evolutionary algorithm based on rotation and decomposition is proposed (MaOEA-RD) to overcome the shortcoming of insufficient selection pressure caused by the Pareto dominance and is competitive compared with nine state-of-the-art many- objective algorithms.
18
Solving multi-objective optimization problem using cuckoo search algorithm based on decomposition
TL;DR: A new decomposition-based multi-objective CS algorithm that achieves a better balance between convergence and diversity is adopted to preserve diversity and was demonstrated to be effective and competitive for MOPs.
18
Solving multi-objective functions for cancer treatment by using Metaheuristic Algorithms.
Farid Heydarpoor,Seyed Mehdi Karbassi,Narges Bidabadi,Javad Mohammad Ebadi +3 more
- 03 Jan 2020
TL;DR: A comparison is made between the two important and useful methods of non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) and the results show that in both criterions the convergence and expansion of Pareto optimal fronts of the performance of the NSGA- II method is better compared to MOPSO.