Non-dominated Sorting Genetic Algorithm II Research Articles

A novel decision support system for designing fixed shading systems in the early design stage: A case study in Egypt

Solar shading is a crucial element that can significantly impact lighting conditions and energy consumption, especially in hot regions. However, selecting the best shading design that achieves optimal daylight performance while meeting architects' requirements is very challenging. This is because it involves considering many design parameters and balancing multiple objectives. The paper introduces a decision support system for designing fixed shading systems to enhance interior environments and reduce energy consumption. Parametric simulation, a meta-model based on automatic selection of regression machine learning algorithms (RMLA), and non-dominated sorting genetic algorithm II (NSGA-II) are used to prepare the designs for selection. Three multiple criteria decision-making (MCDM) methods are employed: the analytic hierarchy process (AHP), entropy, and the technique for order preference by similarity to the ideal solution (TOPSIS) in two combinations (AHP-TOPSIS and entropy-TOPSIS) to adapt to different expert availability scenarios. The proposed method is coded and automated with an easy-to-use interactive interface for flexibility and easy application. The method is applied to help design a fixed shading system for hot climate regions in Cairo, Egypt, which involves considering five objectives: useful day light illuminance (UDI), energy use intensity (EUI), solar gains (SG), daylight autonomy (DA), and continuous daylight autonomy (CDA). The proposed method can provide an efficient design that significantly improves daylight performance. Different dataset sizes are used for training the meta model to study their effect on the final selected design. A sensitivity analysis is done to explore the impact of changes in experts’ opinions on the decision-making process.

A multiobjective optimizer with a K-means cluster algorithm for a distributed flexible flowshop rescheduling problem

A distributed flexible flowshop problem (DFFSP) has been extensively studied over recent years. It is assumed that all jobs to be processed are exactly known in advance, and machines are able to work continuously. In practice, however, new jobs often arrive suddenly. Machines sometimes break down unexpectedly. These lower the performance of the scheduling generated, even make it infeasible. To address this problem, this paper considers a rescheduling DFFSP (DFFRP) with new job arrivals and machine breakdowns. The objective is to minimize makespan and the robustness metrics at the same time. Firstly, we propose a multi-objective mixed-integer linear programming model and a non-dominated sorting genetic algorithm-II based on K-means clustering algorithm (KNSGA-II). Secondly, the problem-specific knowledge is explored and a speed-up strategy is designed to save the algorithmic computation. Thirdly, an initialization strategy based on K-means clustering algorithm is developed to generate high-quality initial solutions. And a novel crossover and mutation operator is employed to accelerate the convergence of the algorithm. Finally, by comparing with a number of advanced multi-objective algorithms in the literature in comprehensive experiments, the proposed algorithm has been demonstrated to be much more effective for solving the DFFRP under consideration.

Combined economic emission load dispatch solution using Cuckoo Search Algorithm

The Cuckoo Search Algorithm (CSA) has been selected to find the best optimum solution for Combined Economic and Emission Load Dispatch Network (CEELD) problems. The objective, in the (CEELD) Analysis, that operating a generator schedule is necessary with both minimum fuel costs and emission levels, together, while satisfying the load demand and operational limitations. In this paper, the objective of research is oriented to minimize the fuel cost and emission for generation sets. The numerical results captured from the suggested method are compared with other techniques such as the Gravitational Search Method, strength Pareto evolutionary algorithm and nondominated sorting genetic algorithm-II to illustrate the efficiency of suggested method.

Prediction-based multi-objective optimization method for 3D printing resource consumption

A prediction-based multi-objective optimization (PBMO) method is proposed in this paper to forecast and reduce 3D printing (3DP) resources on demand, including time, energy, and material. In the authors’ previous research work, a hybrid code-based and data-driven modeling (HCDM) scheme was proposed to customize the predictive models based on process parameters, material deposition paths, and machine behaviors. This study further utilizes the models as multi-objectives to be minimized, aiming at the appropriate solution of process parameters that consume the least resources. Non-dominated sorting genetic algorithm II (NSGA-II), one of the commonly used metaheuristic algorithms, is adopted to construct the PBMO framework, where the HCDM process is embedded in the fitness evaluation step. The corresponding computing program is compiled and then validated on two material extrusion (MEX) machines. Based on the optimization results, hypervolume, as a Lebesgue measure, is used to evaluate the superiorities of all near-optimal solutions, thereby recommending the best-performing solutions for real 3DP. Apart from the 3DP process, the proposed optimization method is adaptable to other mainstream computer numerical control (CNC) manufacturing processes and will guide process design to promote resource conservation for cleaner production.

Multi-Objectives Optimization of Plastic Injection Molding Process Parameters Based on Numerical DNN-GA-MCS Strategy.

An intelligent optimization technique has been presented to enhance the multiple structural performance of PA6-20CF carbon fiber-reinforced polymer (CFRP) plastic injection molding (PIM) products. This approach integrates a deep neural network (DNN), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Monte Carlo simulation (MCS), collectively referred to as the DNN-GA-MCS strategy. The main objective is to ascertain complex process parameters while elucidating the intrinsic relationships between processing methods and material properties. To realize this, a numerical study on the PIM structural performance of an automotive front engine hood panel was conducted, considering fiber orientation tensor (FOT), warpage, and equivalent plastic strain (PEEQ). The mold temperature, melt temperature, packing pressure, packing time, injection time, cooling temperature, and cooling time were employed as design variables. Subsequently, multiple objective optimizations of the molding process parameters were employed by GA. The utilization of Z-score normalization metrics provided a robust framework for evaluating the comprehensive objective function. The numerical target response in PIM is extremely intricate, but the stability offered by the DNN-GA-MCS strategy ensures precision for accurate results. The enhancement effect of global and local multi-objectives on the molded polymer-metal hybrid (PMH) front hood panel was verified, and the numerical results showed that this strategy can quickly and accurately select the optimal process parameter settings. Compared with the training set mean value, the objectives were increased by 8.63%, 6.61%, and 9.75%, respectively. Compared to the full AA 5083 hood panel scenario, our design reduces weight by 16.67%, and achievements of 92.54%, 93.75%, and 106.85% were obtained in lateral, longitudinal, and torsional strain energy, respectively. In summary, our proposed methodology demonstrates considerable potential in improving the, highlighting its significant impact on the optimization of structural performance.

Optimal Design of an Interior Permanent Magnet Synchronous Motor for Electric Vehicle Applications Using a Machine Learning-Based Surrogate Model

This paper presents an innovative design for an interior permanent magnet synchronous motor (IPMSM), targeting enhanced performance for electric vehicle (EV) applications. The proposed motor features a double V-shaped rotor structure with irregular ferrite magnets embedded in the slots between the permanent magnets. This design significantly enhances torque performance. Furthermore, a machine learning-based surrogate model is developed by integrating fine and coarse mesh data. Optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), this surrogate model effectively reduces computational time compared to traditional finite element analysis (FEA).

Two-stage meta-ensembling machine learning model for enhanced water quality forecasting

Accurate short-term forecasting of water quality variables (WQVs) such as dissolved oxygen (DO) and chlorophyll-a (Chl-a) is crucial for the effective management of aquatic resources. This study introduces a robust two-stage optimization-ensembling framework that integrates the Grey Wolf Optimizer (GWO) and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to enhance the forecasting capabilities of machine learning (ML) models. Focusing on Small Prespa Lake, Greece, we implemented an array of diverse ML techniques, including eXtreme Gradient Boosting (XGB), Gradient Boosting Regressor (GBR), Light Gradient-Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). These models were fine-tuned using GWO to optimize their performance over critical WQVs predicted six hours in advance. Our methodology employed rigorous data preprocessing techniques, including lag time feature engineering and principal component analysis (PCA), to handle the high dimensionality of the dataset. Optimal lag times ranging from 6 to 24 hour were evaluated, with the 24-hour lag proving to be the most effective in utilizing historical data to enhance forecasting accuracy. The GWO not only facilitated hyperparameter tuning but also demonstrated a notable improvement (7.6%) in the Kling-Gupta Efficiency (KGE) over conventional randomized search methods. Subsequently, the NSGA-II was utilized for multi-objective optimization, constructing powerful model ensembles that outperformed the individual GWO-optimized models by up to a 7% in KGE. In comparision to a standard genetic algorithm-based ensemble, the NSGA-II ensemble demonstrated superior effectiveness in balancing solution quality. This innovative approach not only establishes a new benchmark in water quality forecasting but also contributes substantially to proactive environmental monitoring and management strategies.

Multi-objective optimization of daylighting performance and energy consumption of educational buildings in different climatic zones of China

With the implementation of China's “Healthy Building” and “Dual Carbon” strategies, daylight health and energy-saving carbon reduction in buildings have become key research areas. In educational buildings, early design decisions significantly impact students' eye health and energy consumption reduction, making it crucial to balance these concerns. Studies indicate that sufficient and continuous daylight can significantly improve students' eye health, mood and sleep. To maximize the UDI level at the eye level (seated) and minimize EUI and DGP, this study used the multi-objective optimization tool Wallacei based on NSGA-II (Non-dominated Sorting Genetic Algorithm II) for genetic optimization. This study uses the Shiji Building of Central South University in Changsha, Hunan, as a reference model to provide optimal classroom designs for China's five major thermal design zones: severe cold, cold, hot summer and warm winter, hot summer and cold winter, and mild. The results indicate that optimizing the classroom's deflection angle, length, width, height, and WWR significantly improves energy efficiency and daylighting performance. Specific data shows that an orientation 5° east or 5° west of south helps balance energy consumption and daylighting performance in both large and small classrooms. The length and width of the classrooms vary greatly, but the height generally converges to 3.2 m. Increasing the WWR to 0.5–0.7 demonstrates good energy efficiency and daylighting performance in both large and small classrooms in hot climate zones. In all climate zones, compared to the initial scheme, the Knee solutions reduced the EUI of both types of classrooms by up to 19.0 %, reduced DGP by up to 56.4 %, and increased UDI by up to 19.0 %.

Multi-objective optimization of comprehensive performance enhancement for proton exchange membrane fuel cell based on machine learning

The comprehensive performance of proton exchange membrane fuel cells depends on operating conditions. This paper innovatively uses the Pearson correlation coefficient to screen the optimization objectives (uniformity index of oxygen, standard deviation of temperature, net power density), and obtains the optimal operating conditions of the proton exchange membrane fuel cell through a multi-objective optimization method. The optimized dataset comes from the simulation results of the three-dimensional numerical model, and the regression model is established through the response surface method. Moreover, the non-dominated sorting genetic algorithm-II is used for processing to obtain the Pareto front solution set, and the optimal operating conditions are obtained from it through the Technique for order preference by similarity to an ideal solution. The analysis of variance result shows that the influence of cathode operating conditions on the comprehensive performance is greater than that of anode, especially the influence of cathode stoichiometry ratio is the most significant. The optimal solution obtained 1.0981 %, 10.5845 %, and 1.0376 % enhancement compared to the optimal values in the simulation results. The differences between the three optimization objectives are only 0.8190 %, 1.0315 %, and 0.8789 % as verified by numerical simulation, thus the machine learning results are reliable and accurate.

Multi-objective optimization of heat transfer performance of H-type fin with vortex generator based on NSGA-II

To further enhance the performance of heat exchangers and bolster waste heat recovery, vortex generators (VGs) of various shapes are added to the H-type fins. This study explores the impact of VGs’ attack angles and configurations on the thermal performance of H-type finned tubes under different Reynolds (Re) numbers. Additionally, multiple dimensionless evaluation metrics are introduced to assess the flow and heat transfer characteristics with VGs, and correlations for predicting the Nusselt (Nu) number and pressure drop at varying attack angles are developed. The results reveal that at low Re numbers, trapezoidal winglets yield the highest comprehensive performance factor (R-factor), while at high Re numbers, rectangular winglets prevail. Compared to trapezoidal winglet configurations, rectangular winglets enhance the Nu by 6.49%-6.67%. Among the various attack angle configurations, the 45° rectangular winglets exhibit the most favorable overall heat transfer performance, with a Nu increase of 12.07% compared to standard H-type fins. It is observed that installing more than two VGs can result in an R-value less than 1, suggesting that excessive VG placement in H-type fin heat exchangers is not advisable. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to identify Pareto optimal points, which exhibit lower resistance and enhanced convective heat transfer. The Pareto optimal set, in comparison to different attack angle CFD data, shows a 31.27% reduction in resistance and a 15.72% increase in Nu.

A multiobjective optimization framework based on FEA, ANN, and NSGA-II to optimize the process parameters of tube-to-tubesheet joint

This study presents a multiobjective optimization framework that integrates Artificial Neural Network (ANN) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for the optimization of rolling process parameters of tube-to-tubesheet joint (TTT-joint). During the rolling process, both beneficial contact pressure and detrimental tensile residual stress are generated within the joint. The primary objective of this framework is to minimize the tensile residual stress while maximizing the contact pressure in the TTT-joint. To achieve this, a backpropagation ANN model is trained to rapidly estimate the residual stress and contact pressure for various sets of rolling process parameters. For training purposes, a series of nonlinear elastoplastic finite element (FE) simulations are performed to generate the input database for the neural network. A detailed parametric study is performed based on the axisymmetric FE model of the TTT-joint. The trained neural network is then incorporated into the NSGA-II optimization algorithm to find the fitness function and optimized process parameters. The contact pressure and residual stress predicted by the proposed ANN-NSGA-II framework are validated by finite element analysis (FEA) using the optimized parameters. The present analysis established that the proposed methodology can be applied in practical engineering problems to obtain the process parameters that yield the maximum contact pressure and minimum tensile residual stress in the TTT-joint.

Seismic Isolators Layout Optimization Using Genetic Algorithm Within the Pymoo Framework

In most previous studies, seismic base isolation system optimization has mainly focused on determining isolation layer parameters. However, the subsequent steps of isolator device selection and positioning can significantly impact overall system performance. To address these shortcomings, we propose an alternative optimization approach demonstrated through two models: regular and irregular 8-storey reinforced concrete structures. This approach utilizes the Pymoo framework and commercially available isolators to find optimal isolator layout configurations in two steps. First, using the equivalent lateral force (ELF) procedure, an initial population of seismic isolators meeting shear strain, base shear coefficient, and buckling requirements was randomly selected from suppliers' elastomeric bearing catalogs. Second, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used to improve the seismic response of the models under the fast nonlinear analysis (FNA) method by minimizing peak roof acceleration, inter-story drift ratio, displacement of the isolated base layer, as well as maximizing the fundamental period. The results underscore the effectiveness of this approach in improving seismic response. Compared to fixed-base structures, the optimal solutions achieved more than double the fundamental period, reduced peak roof acceleration by over 70%, and diminished base shear force by approximately 50%. This methodology can serve as a reference for future research across various structure types, including hybrid isolation systems and steel structures. Doi: 10.28991/CEJ-2024-010-08-07 Full Text: PDF

Exergoeconomic and environmental optimization of gas turbine-based CCHP systems: A comprehensive study with multi-objective optimization and decision making

This study comprehensively assesses two gas turbine-based combined cooling, heating, and power (CCHP) systems, employing exergoeconomic and environmental evaluations. Utilising a dedicated in-house code within the MATLAB environment, the study conducts a thorough performance analysis of the CCHP systems under base case operating conditions. The base case results demonstrate the superiority of the first system over the second system in various aspects. Exergy analysis indicates the combustion chamber as the key source of irreversibility, accounting for more than 70 % of exergy destruction in both systems, emphasising the need to address combustion-related inefficiencies. Furthermore, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to perform multi-objective optimisation of both systems, taking system cost rate and normalized CO2 emissions as objective functions. The investigation encompasses ten optimisation runs for each system, followed by statistical analysis to ensure methodological reliability. The multi-criteria decision analysis is also performed to select the final optimal solution from the Pareto fronts by using a novel approach of integrating Entropy method with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision-maker. The study showcases the significant improvements in system performance and environmental impact achieved through multi-objective optimization. Both systems experience around 8–9 % reductions in system cost rates and around 1.5–2 % reduction in the normalized CO2 emissions. Scattered distribution plots highlight the trade-offs amongst key decision variables, particularly the pressure ratio and isentropic efficiency of the air compressor. In conclusion, this research offers valuable insights into the performance, efficiency, and optimization potential of CCHP systems.

MULTI-OBJECTIVE OPTIMIZATION STUDY OF RESIDENTIAL ENERGY CONSUMPTION BASED ON EDH AND THE STOCHASTIC OPERATION OF AIR CONDITIONING

ABSTRACT Current requirements for residential energy-efficient design solutions in China are relatively homogeneous, requiring only improvements in overall building energy performance, often ignoring the energy differences between different residential housing units. This paper proposes a multi-objective research framework based on an occupant behavior model of air conditioning and a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to study energy-efficient design solutions for residential envelopes. The optimal energy-saving design scheme has a 22.56% and 35.34% reduction in energy consumption difference between residential units and overall building energy consumption, respectively, both of which have been optimized substantially. Moreover, the overall energy performance of the building can be improved when the “energy performance difference between housing units” (EDH) is reduced, which verifies that EDH is an important criterion for improving the energy performance of residential housing units.

Exergy, exergoeconomic optimization and exergoenvironmental analysis of a hybrid solar, wind, and marine energy power system: A strategy for carbon-free electrical production

In this research, aligned with global policies aimed at reducing CO2 emissions from traditional power plants, we developed a holistic energy system utilizing solar, wind, and ocean thermal energy sources, tailored to regions optimal for ocean thermal energy conversion (OTEC). The selected site, characterized by favorable wind and solar conditions close to areas with high OTEC potential, is designed to meet the electricity needs of a coastal community. The system's core components include an Organic Rankine Cycle, turbines, thermoelectric elements, pumps, a heat exchanger, a wind turbine, and a solar collector. A detailed system analysis and thermodynamic evaluation based on thermodynamic principles were carried out using the Engineering Equation Solver (EES) software. Key factors such as wind speed, solar radiation, and collector area were critical in determining system performance. To enhance the system's effectiveness, we conducted a comprehensive comparison of optimization algorithms, incorporating the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and utilizing a Pareto front for value optimization. This approach significantly outperformed other algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) in terms of system efficiency and cost-effectiveness. The developed system achieved an exergy efficiency of 14.46 % and a cost rate of $74.98 per hour, demonstrating its suitability for its intended functions. Moreover, exergoenvironmental evaluation was conducted for the proposed plant. The findings revealed that key component HEX has a high exergoenvironmental factor due to their use of hot water, which has zero unit exergoenvironmental impact. Additionally, pumps demonstrated a zero exergoenvironmental impact factor, indicating negligible component-related environmental impacts. Sensitivity analysis further evaluated critical performance parameters, revealing that increases in solar irradiation lead to decreased total system cost rates, while higher turbine temperatures resulted in a remarkable 14.08 % reduction in the system's cost rate. These results underscore the economic viability of operating the system at higher temperatures and strengthen the argument for its adoption from a financial perspective.

High-dimensional multi-objective optimization of coupled cross-laminated timber walls building using deep learning

This paper introduces a deep learning-based multi-objective optimization framework, applying for advancing the design of the Cross-Laminated Timber Coupled Wall system. While traditional optimization methods often struggle with the curse of dimensionality in high-dimensional problems, the approach proposed in this study employs an autoencoder to effectively reduce the dimensionality of the design space. Subsequently, a neural network establishes a mapping between input variables and latent spaces, with another neural network forming the crucial link between these latent variables and the output responses. The proposed framework is integrated into the design process of a 20-story Cross-Laminated Timber Coupled Wall system, where uncertainties inherent in connection elements are systematically addressed to optimize the structural parameters. The non-dominated sorting genetic algorithm-II is utilized to estimate optimal design variables by minimizing three conflicting objective functions, thereby generating a Pareto front. This optimized design is then bench-marked against three deterministic models with varying coupling beam shear strengths. A two-dimensional numerical model developed in OpenSees facilitates nonlinear time history analysis using 50 bi-directional ground motion records, representative of the seismicity of Vancouver, Canada. The results of this study not only highlight the efficacy of the deep learning-based framework in enhancing the structural integrity and resilience of high-rise timber structures in seismic regions but also significantly contribute to the evolution of computational approaches in structural engineering.

Point Cloud Measurement of Rubber Tread Dimension Based on RGB-Depth Camera

To achieve an accurate measurement of tread size after fixed-length cutting, this paper proposes a point-cloud-based tread size measurement method. Firstly, a mathematical model of corner points and a reprojection error is established, and the optimal solution of the number of corner points is determined by the non-dominated sorting genetic algorithm II (NSGA-II), which reduces the reprojection error of the RGB-D camera. Secondly, to address the problem of the low accuracy of the traditional pixel metric ratio measurement method, the random sampling consensus point cloud segmentation algorithm (RANSAC) and the oriented bounding box (OBB) collision detection algorithm are introduced to complete the accurate detection of the tread size. By comparing the absolute error and relative error data of several groups of experiments, the accuracy of the detection method in this paper reaches 1 mm, and the measurement deviation is between 0.14% and 2.67%, which is in line with the highest accuracy standard of the national standard. In summary, the RGB-D visual inspection method constructed in this paper has the characteristics of low cost and high inspection accuracy, which is a potential solution to enhance the pickup guidance of tread size measurement.

Adaptive machine learning surrogate based multiobjective optimization for scavenging residual saltwater behind subsurface dams

High-fidelity physically based groundwater flow and solute transport models have been limited for seawater intrusion remediation design because of computationally intensive evolutionary algorithms. Data-driven machine learning approaches are promising to substitute expensive-cost groundwater numerical models within optimization due to computing efficiency. However, machine learning surrogates may accumulate error of forecasting and thereby result in infeasible optimal solutions. To achieve desired fidelity level, this study proposes a novel adaptive machine learning surrogate based multiobjective optimization method for coastal aquifer desalination. An adaptive modeling algorithm is newly introduced to iteratively retrain poorly-performed machine learning models and enhance predicting accuracy. The method is demonstrated to seek optimal extraction and injection strategies for scavenging residual saltwater trapped in an upstream aquifer behind a subsurface dam. Two conflicting objectives of minimizing the total extraction-injection rate and maximizing saltwater removal effectiveness are considered. Three machine learning models including artificial neural network (ANN), Gaussian process (GP) and response surface regression model (RSR) are developed to replace a high-fidelity seawater intrusion model for predicting chloride concentration and salinity mass. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to derive Pareto fronts. Pareto optimal solutions obtained from machine learning models are compared against those from the seawater intrusion model. Results indicate that the developed machine learning models do not only have strong predicting capability, but also maintain good quality of Pareto optimal solutions, while achieving substantial computational saving up to 95%. Especially, the adaptively retrained RSR model inhibits error accumulation of forecasting and accelerates correct convergence to the true Pareto front. The proposed method is found superior performance in accurate convergence, widely-spread diversity as well as high computing efficiency than conventional methods.

Geometrical parameters optimization to improve the effective thermal conductivity of the gas diffusion layer for PEM fuel cell

The main concern of this article is a numerical investigation of the geometrical parameters of composite gas diffusion layers (CGDLs) and their impact on effective thermal conductivity (ETC). The geometric parameters under investigation include porosity, thicknesses of the gas diffusion layer (GDL) and micro porous layer (MPL), as well as fibers diameter and orientation. Additionally, non-geometric parameters such as saturation level and operating temperature are examined. This study utilizes realistic full microstructure simulations (GDL + MPL + PolyTetraFluoroEthylene/PTFE + binder) of a paper GDL type (SGL 25BC) to examine and enhance ETC in both in-plane (IP) and through-plane (TP) directions by optimizing the parameters under investigation. To achieve this, a MATLAB code was used to generate microstructures under various conditions, which were simultaneously imported into COMSOL multi-physics via live link technique (LLT). Subsequently, the non-dominated sorting genetic algorithm II (NSGAII) was employed as an optimization method to refine the geometric/non-geometric parameters. Finally, the ETC results for both TP and IP directions were compared with experimental data from the literature. The optimized microstructure exhibits higher ETC values compared to both the initial simulated microstructure and the experimental data. This finding indicates that fabricating CGDLs within the recommended range of optimal geometric values can lead to a substantial increase in their ETC.

Multi-Objective Optimization of Short-Inverted Transport Scheduling Strategy Based on Road–Railway Intermodal Transport

This study focuses on the ‘short-inverted transportation’ scenario of intermodal transport. It proposes a vehicle unloading reservation mechanism to optimize the point-of-demand scheduling system for the inefficiency of transport due to the complexity and uncertainty of the scheduling strategy. This paper establishes a scheduling strategy optimization model to minimize the cost of short backhaul and obtain the shortest delivery time window and designs a hybrid NSGWO algorithm suitable for multi-objective optimization to solve the problem. The algorithm incorporates the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm based on the Grey Wolf Optimizer (GWO) algorithm, compensating for a single algorithm’s premature convergence. The experiment selects a logistics carrier’s actual road–rail intermodal short-inverted data and compares and verifies the above data. The results show that the scheduling scheme obtained by this algorithm can save 41.01% of the transport cost and shorten the total delivery time by 46.94% compared with the original scheme, which can effectively protect the enterprise’s economic benefits while achieving timely delivery. At the same time, the optimized scheduling plan resulted in a lower number of transport vehicles, which positively impacted the sustainability of green logistics.