Optimization Techniques Research Articles

Sustainability-driven application of waste steel and tyre rubber fibres as reinforcement in concrete: An optimization study using response surface methodology

Innovative use of waste materials has proven to be effective for improving concrete’s technical properties while eliminating the threat of environmental pollution. There is paucity of research regarding the combined use of Waste Steel Fibres (WSFs) and Waste Tire Rubber Fibres (WTRFs) in concrete. Thus, this study aimed to investigate the influence of WSFs and WTRFs as reinforcement in concrete and determine their optimal fractions for enhancing the concrete strength properties using Response Surface Methodology (RSM). WSFs of 0.3-1.5% with a 0.6% increment and WTRFs of 0.3-1.5% with a 0.6% increment by concrete volume as reinforcement in concrete with water-to-cement ratio (W/C) of 0.25–0.65 with 0.2 increment were designed using the Box-Behnken Design of RSM. A total of fourteen (14) concrete mixes with a design strength of 25 N/mm2 were prepared. The fresh and hardened properties (slump, density, water absorption, 7- and 28-day compressive and split tensile strengths) of concrete were determined using standard procedures. The RSM was used to evaluate the interaction between the concrete variables and identify their optimum combination which gave the peak values of responses. Furthermore, the characteristics and sustainability of the concrete under optimum variables were compared with that of the conventional concrete. The outcomes revealed that the inclusion of WSFs and WTRFs yielded concretes with maximum slump, density, water absorption and 28-day compressive and split tensile strengths of 25 mm, 2633 kg/m3, 5%, 41 N/mm2 and 4.54 N/mm2, respectively. The optimization technique showed the optimal variables combination which yielded the peak response values of WSFs (1.40%), WTRFs (0.66%) and W/C (0.54). The nominal variance with the absolute percent error of less than (9%) between the actual experimental verified results and predicted results for all the responses validates the predictability of the model. The split tensile strength and compressive strength increased by 28.33% and 48.85%, respectively at the optimum setpoint of variable with respect to the reference concrete. In addition, the WSFs and WTRFs-reinforced concrete exhibited lower embodied CO2 emission but the embodied energy and cost are slightly higher relative to conventional concrete. Nevertheless, the embodied energy of the concrete was significantly lesser than that of concrete that used individual fibers for reinforcement. Thus, the enhancement of concrete strength properties with the prospect for sustainable fibre-reinforced concrete production through the use of waste steel and tyre rubber fibres is feasible.

Enhanced Prairie Dog Optimization with Differential Evolution for solving engineering design problems and network intrusion detection system

This paper introduces a novel hybrid optimization algorithm, PDO-DE, which integrates the Prairie Dog Optimization (PDO) algorithm with the Differential Evolution (DE) strategy. This research aims to develop an algorithm that efficiently addresses complex optimization problems in engineering design and network intrusion detection systems. Our method enhances the PDO's search capabilities by incorporating the DE's principal mechanisms of mutation and crossover, facilitating improved solution exploration and exploitation. We evaluate the effectiveness of the PDO-DE algorithm through rigorous testing on 23 classical benchmark functions, five engineering design problems, and a network intrusion detection system (NIDS). The results indicate that PDO-DE outperforms several state-of-the-art optimization algorithms regarding convergence speed and accuracy, demonstrating its robustness and adaptability across different problem domains. The PDO-DE algorithm's potential applications extend to engineering challenges and cybersecurity issues, where efficient and reliable solutions are critical; for example, the NIDS results show significant results in detection rate, false alarm, and accuracy with 98.1%, 2.4%, and 96%, respectively. The innovative integration of PDO and DE contributes significantly to stochastic optimization and swarm intelligence, offering a promising new tool for tackling diverse optimization problems. In conclusion, the PDO-DE algorithm represents a significant scientific advancement in hybrid optimization techniques, providing a more effective approach for solving real-world problems that require high precision and optimal resource utilization.

Combination of machine learning and COSMO-RS thermodynamic model in predicting solubility parameters of coformers in production of cocrystals for enhanced drug solubility

In this study, we develop predictive models for three target variables, denoted as δd, δp, and δh using a dataset with 86 features and 181 samples. The response parameters, which are Hansen solubility parameters, were correlated to input parameters via several machine learning techniques. The input features are molecular descriptors of coformers which are calculated based on COMSO-RS thermodynamic model and group contribution approach. The analysis includes outlier detection via Cook's distance, normalization with a min-max scaler, and feature selection through L1-based methods. Three regression models—Gaussian Process Regression (GPR), Passive Aggressive Regression (PAR), and Polynomial Regression (PR)—are employed, with hyperparameter optimization achieved using Transient Search Optimization (TSO). The results indicate that for δd, the PAR model outperforms others with an R2 score of 0.885, RMSE of 0.607, MAE of 0.524, and a maximum error of 1.294. The GPR model shows slightly lower performance with an R2 of 0.872, RMSE of 0.816, MAE of 0.579, and a maximum error of 2.755 for δd. The PR model performs on δd with an R2 of 0.814, RMSE of 0.923, MAE of 0.597, and a maximum error of 2.814. For δp, the GPR model provides the best performance, achieving an R2 score of 0.821, RMSE of 1.693, MAE of 1.391, and a maximum error of 3.457. The PAR model performs on δp with an R2 of 0.740, RMSE of 2.025, MAE of 1.980, and a maximum error of 6.609. Also, The PR model predicts δp with a R2 of 0.7, RMSE of 2.329, MAE of 2.02, and maximum error of 6.366. Similarly, for δh, the GPR model again shows superior performance with an R2 score of 0.983, RMSE of 1.243, MAE of 1.005, and a maximum error of 2.577. The PAR model also accurately predicts δh with a R2 of 0.924, RMSE of 2.713, MAE of 2.416, and maximum error of 6.307. Additionally, the PR model predicts δh with a R2 of 0.927, RMSE of 2.757, MAE of 2.334, and maximum error of 8.064. These results highlight the efficacy of the chosen models and optimization techniques in accurately predicting the specified outputs, demonstrating significant potential for application in relevant predictive modeling tasks.

Enterprise supply chain network optimization algorithm based on blockchain-distributed technology under the background of digital economy

Demand forecasting is essential for streamlining supply chain operations in the digital economy and exceeding customer expectations. On the other hand, traditional forecasting techniques cannot frequently present real-time data and respond to dynamic changes in the supply chain network, leading to less-than-ideal decision-making and higher costs. This research aims to create a technique for optimizing the supply chain network based on blockchain-distributed technology (SCN-BT) to overcome these drawbacks and fully utilize the potential of the digital economy. The suggested framework uses the hybridized LSTM network and Grey Wolf Optimization (GWO) algorithm to examine demand forecasting in the supply chain network for inventory planning. The SCN-BT framework develops a safe and productive, enabling precise and flexible demand by combining blockchain with optimization techniques. A thorough case study utilized information collected from an enterprise supply chain that operates in the digital economy to show the efficiency of the suggested framework. Compared to conventional approaches, the results show considerable gains in demand forecasting precision, responsiveness of the supply chain, and cost-effectiveness. In the context of the digital economy, demand sensing and prediction enable firms to react to changes swiftly, shorten turnaround times, optimize inventory levels, and improve overall supply chain performance. The results highlight how blockchain technology has the potential to enhance collaboration, trust, and transparency inside intricate supply chain networks working in the digital economy. The experimental results show the proposed to achieve prediction rate of demand prediction rate of 128.93, demand forecasting accuracy ratio of 92.18%, optimum efficiency of 94.25%, RMSE rate of 1841.25, MAE rate of 260.74, and sMAPE rate of 0.1002 compared to other methods.

Optimal channel assignment on dense Wi-Fi networks using Thermodynamic Threshold Accepting

Channel assignment in Wi-Fi 6 dense networks is a challenging problem which critically impacts the performance of WLAN communications. Although there have been many proposals in the area of IEEE 802.11 channel assignment, LCCS has emerged as the de facto standard for distributed channel selection. Notwithstanding, centralized optimizers overcome the performance of distributed channel assignment algorithms, so their study is of utmost importance. One of the most successful optimization proposals in the state-of-the-art for Wi-Fi 4 is based on simulated annealing (SA). However, SA presents a significant limitation: its parameters must be adjusted manually for each scenario, otherwise yielding suboptimal solutions. In this paper, we propose a new technique for channel assignment optimization of Wi-Fi dense networks, called Thermodynamic Threshold Accepting (TTA), including modifications inspired by Threshold Accepting (TA) and Thermodynamic Simulated Annealing (TSA). With respect to SA, TTA avoids manual tuning, being able to adapt to the specific setting automatically. We evaluate TTA in a dense Wi-Fi model and, compared to SA, we show that our approach reaches optimal results without requiring any prior fine-tuning. We also show that TTA is even able to slightly overcome the performance of an accurately calibrated SA optimizer for the same number of iterations.

UNIFY: A unified policy designing framework for solving integrated Constrained Optimization and Machine Learning problems

The integration of Machine Learning (ML) and Constrained Optimization (CO) techniques has recently gained significant interest. While pure CO methods struggle with scalability and robustness, and ML methods like constrained Reinforcement Learning (RL) face difficulties with combinatorial decision spaces and hard constraints, a hybrid approach shows promise. However, multi-stage decision-making under uncertainty remains challenging for current methods, which often rely on restrictive assumptions or specialized algorithms. This paper introduces unify, a versatile framework for tackling a wide range of problems, including multi-stage decision-making under uncertainty, using standard ML and CO components. unify integrates a CO problem with an unconstrained ML model through parameters controlled by the ML model, guiding the decision process. This ensures feasible decisions, minimal costs over time, and robustness to uncertainty. In the empirical evaluation, unify demonstrates its capability to address problems typically handled by Decision Focused Learning, Constrained RL, and Stochastic Optimization. While not always outperforming specialized methods, unify’s flexibility offers broader applicability and maintainability. The paper includes the method’s formalization and empirical evaluation through case studies in energy management and production scheduling, concluding with future research directions.

SLG-SLAM: An integrated SLAM framework to improve accuracy using semantic information, laser and GNSS data

Visual Simultaneous Localization and Mapping (V-SLAM) is pivotal for precise positioning and mapping. However, visual data from crowd-sourced datasets often contains deficiencies that may lead to positioning errors. Despite existing optimization techniques, current algorithms do not adequately adapt to varied data in vehicle driving scenarios. To address this gap, this study introduces a novel SLAM framework (SLG-SLAM). This framework refines trajectories by integrating semantic information, laser point cloud, and global navigation satellite system (GNSS) data into V-SLAM. Initial trajectory estimates are made after filtering out dynamic targets and are subsequently refined with matched laser point clouds, then corrected for scale and direction using GNSS. The efficacy of this approach is assessed using four public datasets and one self-collected dataset, showing significant enhancements across all datasets. The proposed method reduces the mean absolute trajectory error by 43.50% on the KITTI dataset and 14.91% on the MVE dataset compared to the baseline. Unlike the baseline, which fails on three other datasets, the proposed method successfully performs localization and mapping. Additionally, compared to three other single-source methods (DynaSLAM, MCL, MVSLAM), the proposed method consistently outperforms, demonstrating its superior adaptability and effectiveness.

Genetic modification optimization technique: A neural network multi-objective energy management approach

In this study, a Neural Network-Enhanced Gene Modification Optimization Technique was introduced for multi-objective energy resource management. Addressing the need for sustainable energy solutions, this technique integrated neural network models as fitness functions, representing an advancement in artificial intelligence-driven optimization. Data collected in the European Union covered greenhouse gas emissions, energy consumption by sources, energy imports, and Levelized Cost of Energy. Since different configurations of energy consumption by sources lead to varying greenhouse gas emissions, costs, and imports, neural network prediction models were used to project the effect of new energy combinations on these variables. The projections were then fed into the gene modification optimization process to identify optimal configurations. Over 28 generations, simulations demonstrated a 46 percent reduction in energy costs and a 9 percent decrease in emissions. Human bias and subjectivity were mitigated by automating parameter settings, enhancing the objectivity of results. Benchmarking against traditional methods, such as Euclidean Distance, validated the superior performance of this approach. Furthermore, the technique's ability to visualize chromosomes and gene values offered clarity in optimization processes. These results suggest significant advancements in the energy sector and potential applications in other industries, contributing to the global effort to combat climate change.

Machine learning chain models for multi-response prediction of electrical equipment in substation subjected to earthquakes

Engineering structures often exhibit multiple potential vulnerable positions during strong earthquakes, such as porcelain insulators and connection flanges of electrical equipment in substations. This paper proposes two types of machine learning (ML) chain models, where multiple individual models are linked end to end, to predict multiple peak seismic responses of substation equipment at the same time using intensity measures (IMs). One is a simple chain where the next input is the previous output, while in another one, the next input is combined by IMs and the previous output. The training mechanisms of ML chain models are presented by means of bio-inspired multi-objective optimization techniques for the hyperparameters of multiple individual ML models. A case study on a 1100 kV transformer bushing is then conducted. Artificial neural network-gradient boosting regression and artificial neural network-kernel ridge regression chain models are respectively established for predicting the peak stresses at two most vulnerable positions. Both the evaluation indicators and shaking table tests show enough prediction accuracy of the two ML chain models. Such chain models are qualified for identifying the initial damage to equipment, which can be employed for assisting post-earthquake rapid judgement and relief.

Puma optimizer technique for optimal planning of different types of distributed generation units in radial distribution network considering different load models

Puma optimizer technique for optimal planning of different types of distributed generation units in radial distribution network considering different load models

Multi-objective optimization design of aerothermal performance for turbine blade squealer tip with inclined suction-side rim

To optimize the inclined rim design, multi-objective optimization was numerically conducted on the suction-side rim of the squealer tip in the GE-E3 rotor blade. The RANS equations, incorporating with the standard k-ω model, were numerically solved. Through the implementation of a Kriging surrogate model coupled with the NSGA-II evolutionary optimization technique, six geometric parameters on the suction-side rim went through iterative optimization. This process yielded three optimized squealer tip geometries on the Pareto front: Structure 1 with minimum total pressure loss, Structure 2 with optimal comprehensive aerothermal performance, and Structure 3 with minimum tip heat load. Comparative analysis revealed that with the inner and external wall inclined toward the cavity and passage respectively, the optimization results in a 3 %–11 % reduction in tip heat load, accompanied by an 8.5 %–10.2 % reduction in aerodynamic loss, facilitated by an attenuated upper passage vortex (UPV) and strengthened tip leakage vortex (TLV). The critical inclined region of the external rim wall, spanning 7 %–80 %, influences the exit total pressure loss distribution. The inclined inner wall upstream of 50 % Cax obstructs coolant jets from exiting the cavity, reducing the heat load on the cavity bottom. However, the inclined external wall from 66 % to 90 % Cax amplifies the impingement of the scraping vortex (SV), increasing local heat transfer. The thermal behavior of Structure 1 is sensitive to changes in tip clearance, whereas Structure 2 and 3 show more robust advantages. As the clearance increased, the aerodynamic losses associated with the TLV rise for all three optimized tips, but losses from the UPV remain similar. This study provides a novel approach for designing efficient squealer tip geometries incorporating an inclined rim.

An automated OpenMP mutation testing framework for performance optimization

Performance optimization continues to be a challenge in modern HPC software. Existing performance optimization techniques, including profiling-based and auto-tuning techniques, fail to indicate program modifications at the source level thus preventing their portability across compilers. This paper describes Muppet, a new approach that identifies program modifications called mutations aimed at improving program performance. Muppet’s mutations help developers reason about performance defects and missed opportunities to improve performance at the source code level. In contrast to compiler techniques that optimize code at intermediate representations (IR), Muppet uses the idea of source-level mutation testing to relax correctness constraints and automatically discover optimization opportunities that otherwise are not feasible using the IR. We demonstrate the Muppet’s concept in the OpenMP programming model. Muppet generates a list of OpenMP mutations that alter the program parallelism in various ways, and is capable of running a variety of optimization algorithms such as delta debugging, Bayesian Optimization and decision tree optimization to find a subset of mutations which, when applied to the original program, cause the most speedup while maintaining program correctness. When Muppet is evaluated against a diverse set of benchmark programs and proxy applications, it is capable of finding sets of mutations that induce speedup in 75.9% of the evaluated programs.

Techno-economic analysis and optimization of a binary geothermal combined district heat and power plant for Puga Valley, India

This article investigates the energy, exergy, and economic performance of a Puga Valley-specific binary geothermal combined district heat and power plant, taking into account geotechnical data and related cost functions. The power plant uses an organic Rankine cycle (ORC) with R123 as the working fluid, utilizing waste heat for district heating to enhance efficiency. Variations in operating and design parameters such as geothermal fluid flow rate, temperature, and ORC temperature of the evaporator and condenser are found to have significant effects on the overall performance of the system and cost rate. A multi-objective grey wolf optimization technique based on artificial neural networks has been used to determine the ideal values of the above-mentioned parameters. The optimization study revealed a Pareto optimal curve with overall exergy efficiency and total cost rate as objective functions from which the optimal point was identified using the technique for order preference by similarity to the ideal solution (TOPSIS) method. While operating the proposed CHP plant at TOPSIS optimal point, the plant delivers a net electrical output of 1.08 MW alongside 4.19 MW of thermal energy, resulting in a total cost rate of 33.84 USD/h, with energy and exergy efficiencies peaking at 32.26 % and 36.8 %, respectively. The exergy analysis at the optimal point also revealed a notable reduction of 550 kW in the total exergy destruction rate compared with the base-case scenario. Furthermore, the design requirements of a 5 MW net power output CHP plant for effective recovery of the Puga Valley geothermal resource are discussed.

Development and analysis of a novel power-to-gas-to-power system driven by the Allam cycle for simultaneous electricity and water production

The global energy sector faces dual challenges of reducing carbon emissions and managing the intermittency of renewable energy sources. Addressing the challenge of renewable energy intermittency, the power-to-gas technique emerges as a potent solution and the Allam cycle stands out as a remarkably efficient, semi-closed, supercritical carbon dioxide cycle, boasting a zero-carbon emission process. In the current study, the combination of the Allam cycle and power-to-gas technology yields a zero-carbon emission process, culminating in a highly efficient multi-generation system. This innovative system aims to generate power, methane, and freshwater simultaneously. The waste heat produced as a by-product of the Allam cycle is effectively utilized by integrating it with the multi-effect desalination-thermal vapor compression unit. The system’s performance was evaluated using Engineering Equation Solver for exergy and economic assessments, while MATLAB incorporates an artificial neural network and moth-flame algorithm for multi-objective optimization. The optimization process operates under the influence of nine key decision variables, ensuring comprehensive analysis and robust decision-making. Based on the optimization results and Technique for Order Preference by Similarity to Ideal Solution method, the optimal solution for freshwater mass flow rate, exergy efficiency, and cost rate is determined to be 378.5 kg/s, 39.03 %, and 11,021 $/h, respectively. Finally, this innovative system demonstrates the potential to address renewable energy intermittency and carbon emissions simultaneously, offering a promising approach for sustainable energy generation and water production.

An optimized ensemble model for predicting average localization error of wireless sensor networks

Wireless sensor networks (WSNs) are widely utilized in various applications due to their compact size, cost-effectiveness, and ease of deployment. Nonetheless, one of the biggest problems in WSNs is getting a reasonable estimate of the average location error of a node at the setup in the least amount of time. Wireless sensor networks can undergo changes over time due to various external and internal factors, such as environmental conditions, network congestion, hardware failures, or software updates. When these changes occur, the network may require redesigning, which can incur significant expenses. Traditional WSNs approaches, on the other hand, have been explicitly programmed, which makes it hard for networks to respond dynamically. Therefore, machine learning (ML) techniques can be used to respond appropriately in such scenarios. In this work, we proposed an optimized ML ensemble model for (i) identifying the critical network parameters for node localization when setting up wireless sensor networks with the accuracy needed in a short amount of time and (ii) predicting the average localization error of wireless sensor networks. We used the random forest algorithm with optimized hyperparameters from different optimization techniques to predict average localization error (ALE) using independent features like node density, anchor ratio, transmission range, and iterations.

Understanding the Dynamics of Ocean Wave-Current Interactions Through Multivariate Multi-Step Time Series Forecasting

ABSTRACT Understanding ocean wave-current interactions’ complex dynamics is crucial for coastal engineering, marine operations, and climate research applications. This study introduces a pioneering data-driven approach by employing advanced deep learning techniques, specifically Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) models, to forecast both wave and current parameters at varying depths. The models are designed to capture the complex temporal relationships inherent in ocean dynamics, considering wave speed and direction, current speed, and direction as multivariate time series inputs. Two comprehensive experiments are conducted, one utilizing historical values of all parameters and another focusing on using wave parameters to forecast current parameters. Model performance is rigorously evaluated across forecast horizons of 5, 12, and 24 hours ahead using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). BiLSTM emerges as the superior model, demonstrating lower errors, particularly at higher depths, while nearshore predictions reveal challenges in shallower waters. Furthermore, the methodology incorporates hyperparameter optimization and cross-validation techniques to enhance the model’s robustness. Ultimately, this work represents a transformative leap toward smarter oceans, emphasizing the fusion of fluid dynamics and bathymetry to advance our understanding of coupled wave-current dynamics. The results showcase high accuracy and reliability across various forecast horizons and depths, signifying the method’s potential applications in oceanography, hydrodynamics, coastal engineering, and ocean renewable energy.

An enhanced energy management system for coordinated energy storage and exchange in grid-connected photovoltaic-based community microgrids

This paper introduces an enhanced coordinated community energy management system (CEMS) for a community microgrid. It is designed to optimize residential energy consumption and facilitate energy sharing among smart homes. The proposed solution integrates constrained mixed-integer programming (CMIP) approach along with advanced optimization technique and cooperative game theory-based pricing mechanism to address critical challenges in existing energy management strategies, including load scheduling and efficient peer-to-peer energy trading. The CEMS approach is adopted for grid-connected community power systems that incorporate local energy sources such as photovoltaic and battery storage systems. This solution is implemented under a time-of-use dynamic pricing model for energy transfer from the main grid to the community peers, a modified mid-market pricing mechanism for the P2P energy exchange, and feed-in tariff for energy transfer from the community peers to the main grid. Besides, seagull optimization algorithm is applied to relax the CMIP problem to get the optimal rational solution of the scheduling problem which is subjected to a rounding process to obtain the best feasible solution. The obtained results indicate a substantial reduction in electricity costs, achieving daily savings of 72.21 % and reducing grid dependency by 41.95 %. Moreover, 82.2 % of the community's energy demand was met through locally generated resources with 18.39 % enhancement of the overall CMG self-consumption ratio comparing to the reference operating scenario. Ultimately, the study demonstrates the effectiveness of the management system in improving load profiles, maximizing the use of local renewable energy sources, and reducing electricity expenses, thereby providing a sustainable and economically viable model for future smart grids.

Generation and evaluation of unimaginable three-dimensional structural joints using generative adversarial networks

Compared to the increasingly programmable and automated structural analysis methods, the current joint design suffers from long design time, poor quality and a low degree of automation. Developing an efficient and intelligent method to generate structurally novel and high-performing joints has become an urgent task. This paper combines artificial intelligence, topology optimization and reverse engineering techniques to develop a method named HD-JointGEN, and elaborates the idea and implementation steps of this method through an actual engineering example of a single-layer reticulated shell structure. The joints generated in batch by HD-JointGEN are analyzed and evaluated, revealing that the selected representative joint can reduce self-weight by 29.55%, maximum displacement by 0.99%, and maximum equivalent stress by 1.28% compared to the topology optimization joint. HD-JointGEN not only automatically generates a variety of innovative and previously unimaginable joints but also offers design solutions with superior force performances. It leads the research on intelligent generative design of the connection in complex three-dimensional structures and has broad application prospects.

A pointwise weighting prediction variance–high-dimensional model representation model-based global optimization approach for ship hull parametric design

Ship hull optimization techniques based on computer-aided design/computational fluid dynamics can effectively enhance the efficiency and stability of ship designs, with significant application prospects. To enhance the potential of ship hull optimization, increasing design variable dimensionality is essential, but can cause a significant increase in hydrodynamic simulations. To reduce simulations required for high-dimensional ship hull optimization, a new surrogate method, pointwise weighting prediction variance–high-dimensional model representation (PWPV-HDMR), which uses pointwise weighting prediction variance (PWPV) to aggregate different a priori assumptions, is developed. Moreover, a differential evolution algorithm is used to identify promising hull design parameters, using the PWPV-HDMR model instead of costly simulation as the fitness function. The proposed approach is tested on the hydrostatic resistance optimization of KRISO Container Ship. The results show that PWPV-HDMR outperforms kriging-HDMR, with a better resistance optimization effect, illustrating the effectiveness of the PWPV-HDMR-based global optimization approach in discovering promising ship hull parameters.

Fuzzy expert system for ergonomic assembly line worker assignment and balancing problem under uncertainty

ABSTRACT In the era of Industry 5.0, there is a significant gap in addressing ergonomic risks and imprecise task times in manufacturing systems. This study aims to fill this gap by extending the ergonomic assembly line balancing problem with worker assignment. It employs a novel two-phase framework combining a constructive heuristic for feasibility with a unique ergonomic assessment method developed through a fuzzy expert system. Validated using 96 synthesized numerical instances, the proposed method addresses the scarcity of fuzzy and ergonomic-oriented data in benchmarks. Then, computational results are thoroughly analyzed to evaluate the method’s performance and identify potential areas for further research. The proposed optimization method provided high-quality solutions with a majority demonstrating low ergonomic risk and high worker safety, contributing to an overall improvement in ergonomic conditions. The integration of fuzzy expert system and advanced optimization techniques yields a robust framework for achieving a safer and more efficient manufacturing environment.