Multi-objective Optimization Model Research Articles

Assessing Environmental Performance of Water Infrastructure Based on an Attention-Enhanced Adaptive Neuro-Fuzzy Inference System and a Multi-Objective Optimization Model

Assessing Environmental Performance of Water Infrastructure Based on an Attention-Enhanced Adaptive Neuro-Fuzzy Inference System and a Multi-Objective Optimization Model

Flexible Job Shop Dynamic Scheduling and Fault Maintenance Personnel Cooperative Scheduling Optimization Based on the ACODDQN Algorithm

In order to address the impact of equipment fault diagnosis and repair delays on production schedule execution in the dynamic scheduling of flexible job shops, this paper proposes a multi-resource, multi-objective dynamic scheduling optimization model, which aims to minimize delay time and completion time. It integrates the scheduling of the workpieces, machines, and maintenance personnel to improve the response efficiency of emergency equipment maintenance. To this end, a self-learning Ant Colony Algorithm based on deep reinforcement learning (ACODDQN) is designed in this paper. The algorithm searches the solution space by using the ACO, prioritizes the solutions by combining the non-dominated sorting strategies, and achieves the adaptive optimization of scheduling decisions by utilizing the organic integration of the pheromone update mechanism and the DDQN framework. Further, the generated solutions are locally adjusted via the feasible solution optimization strategy to ensure that the solutions satisfy all the constraints and ultimately generate a Pareto optimal solution set with high quality. Simulation results based on standard examples and real cases show that the ACODDQN algorithm exhibits significant optimization effects in several tests, which verifies its superiority and practical application potential in dynamic scheduling problems.

Multi-Objective Optimization of Milling Ti-6Al-4V Alloy for Improved Surface Integrity and Sustainability Performance

Ti-6Al-4V is a titanium-based alloy that is widely used in a diverse range of applications, especially in industries such as biomedical and aerospace. Several lubricooling techniques have been introduced to enhance the machinability of these materials. Among them, environmentally friendly strategies are gaining in importance, with sustainability trends rising in manufacturing. The present research investigates the effect of two eco-friendly lubricooling techniques (minimum quantity lubrication and cryogenic cooling), along with other cutting parameters (cutting speed and feed per tooth), on the surface roughness and microhardness of the machined surfaces, which are identified as one of the most frequently implemented indicators of surface integrity in the ball-end milling of the Ti-6Al-4V alloy. In addition, the total electrical energy consumption of the machine tools under different cooling/lubrication conditions was also analyzed. The results obtained showed that cryogenic cooling enhanced milling performance as compared to MQL. Moreover, a multi-objective parameter optimization model integrating the machining responses (surface roughness, microhardness, energy consumption, and productivity) and sustainability metrics (environmental impact, operator’s health and safety, and waste management) was introduced. It was found that cryogenic cooling outperformed the MQL method in terms of both machining performance and environmental impact. An analysis of variance (ANOVA) was carried out to evaluate the significance of each process parameter on the multiple performance index. The results indicate that feed per tooth, cooling method, and cutting speed were significant, with respective contributions of 39.4%, 36.8%, and 22.9%. Finally, the optimal parameter setting was verified through a confirmation test and the results reveal that an improvement was observed in the machining responses and multiple performance index.

Calibration and measurement method of PMD based on the optimization algorithm of swarm intelligent body

Traditional phase measuring deflectometry (PMD) systems encounter challenges related to the stability of calibration parameter optimization, often resulting in insufficient reconstruction accuracy and low computational efficiency. In response to this technical challenge, this paper proposes an optimization method for the two-step calibration of the PMD system based on a swarm-intelligent body optimization algorithm, aiming to significantly improve the reconstruction accuracy and processing efficiency of the system. First, a traditional calibration method is used for the initial calibration of the measurement system. Subsequently, the dung beetle optimization (DBO) algorithm—to our knowledge, a novel swarm intelligence optimization algorithm known for its rapid convergence and global search capabilities—is introduced to construct a multi-objective optimization model with the three-dimensional system reconstruction error as the objective function. By applying the DBO to solve this model, a set of optimal calibration parameters is obtained. Experimental results demonstrate that the DBO algorithm achieves significant improvements in both reconstruction accuracy and stability compared to traditional calibration methods, thereby verifying its effectiveness and practicality in PMD systems.

A Bionic-Based Multi-Objective Optimization for a Compact HVAC System with Integrated Air Conditioning, Purification, and Humidification

This study is dedicated to the development of a multifunctional device that integrates air conditioning, humidification, and air purification functions, aimed at meeting the demands for energy efficiency, space-saving, and comfortable indoor environments in modern residential and commercial settings. The research focuses on achieving a balance between performance, energy consumption, and noise levels by combining bionic design principles with advanced optimization algorithms to propose innovative design and optimization methods. Specific methods include the establishment and optimization of mathematical models for air conditioning, air purification, and humidification functions. The air conditioning module employs a nonlinear programming model optimized through the Parrot Optimizer (PO) Algorithm to achieve uniform temperature distribution and minimal energy consumption. The air purification function is based on a bionic model and optimized using the Deep ACO Algorithm to ensure high efficiency and low noise levels. The humidification function utilizes a mist diffusion model optimized through the Slime Mold Algorithm (SMA) to enhance performance. Ultimately, a multi-objective optimization model is constructed using the Beluga Whale Optimization (BWO), successfully integrating the three main functions and designing a compact segmented cylindrical device that achieves a balance of high efficiency and multifunctionality. The optimization results indicate that the device exhibits superior performance, with a Clean Air Delivery Rate (CADR) of 400 m3/h, a humidification rate of 1.2 kg/h, a temperature uniformity index of 0.08, and a total power consumption controlled within 1600 W. This study demonstrates the significant potential of bionic design and optimization technology in the development of multifunctional indoor environment control devices, enhancing not only the overall performance of the device but also the comfort and sustainability of the indoor environment. Future work will focus on system scalability, experimental validation, and further optimization of bionic characteristics to expand the device’s applicability and enhance its environmental adaptability.

Multi-Objective Structural Optimization of Hydraulically Controllable Reciprocating Seals With Comprehensive Static and Dynamic Performance

Abstract The hydraulically controllable reciprocating seal (HCRS) is a novel intelligent sealing technology capable of online performance regulation, making it one of the preferred solutions for highly reliable seals in the industrial field. However, the impact of structural parameters on the performance of it remains unclear, complicating efforts to provide precise guidance for structural design. To address these issues, this article first employs a mixed thermoelastohydrodynamic lubrication (TEHL) model to perform a parametric analysis of how structural parameters affect the static and dynamic performance of seals, identifying key influencing factors. Second, a multiobjective optimization model was established to derive the optimal structural design using a comprehensive balance method. Finally, the performance results before and after optimization were compared. The results indicate that the width of the slide ring, the air side angle of the slide ring, the inner diameter of the slide ring, and the length of the internal pressure cavity are critical factors influencing sealing performance. Following optimization, the maximum von Mises stress in the seal was reduced by 51.5%, net leakage decreased by 12.9%, and friction power loss reduced by 2.25%. This demonstrates that the optimization method is effective for designing seals with low leakage, low friction, and high reliability.

Towards a sustainable viticultural supply chain under uncertainty: Integration of data envelopment analysis, artificial neural networks, and a multi-objective optimization model.

Towards a sustainable viticultural supply chain under uncertainty: Integration of data envelopment analysis, artificial neural networks, and a multi-objective optimization model.

Determining Human-Hepatitis C Virus Protein Interactions: A synergism of fuzzy multi-objective optimization and machine intelligent models

Determining Human-Hepatitis C Virus Protein Interactions: A synergism of fuzzy multi-objective optimization and machine intelligent models

Sustainable Energy Integration: Enhancing the Complementary Operation of Pumped-Storage Power and Hydropower Systems

Sustainable Energy Integration: Enhancing the Complementary Operation of Pumped-Storage Power and Hydropower Systems

A new multi-objective optimization model for an integrated energy system based on life-cycle composite technical, economic and environmental indices

A new multi-objective optimization model for an integrated energy system based on life-cycle composite technical, economic and environmental indices

A Novel Multi-objective Optimization Model for Production Routing Problem with Sustainability Dimensions

A Novel Multi-objective Optimization Model for Production Routing Problem with Sustainability Dimensions

OPTIMIZING JOB CREATION AND SOLID WASTE MANAGEMENT SYSTEM: A GOAL PROGRAMMING APPROACH

Unemployment and solid waste management (SWM) are pressing challenges in Nigeria, with youth unemployment reaching critical levels and waste disposal practices straining environmental sustainability. This study explores the integration of job creation and SWM through a goal programming framework, aiming to address resource allocation, sustainability, and managerial decision-making. By leveraging the potential of integrated SWM, such as recycling and material recovery, this research identifies innovative pathways to transform waste into wealth, generate employment opportunities, and promote environmental conservation.Using secondary data from the National Bureau of Statistics (NBS) and Abuja Environmental Protection Board (AEPB), alongside stakeholder interviews, the study develops a multi-objective optimization model using goal programming approach. The results indicate that optimal allocation of resources in waste collection and recycling could create over 1,400 jobs, providing a practical solution to Nigeria's unemployment crisis while fostering sustainable waste management practices. This research underscores the need for stakeholder collaboration, policy reforms, and investments in infrastructure to maximize the dual benefits of job creation and environmental sustainability in the waste management sector.The findings have significant implications for policymakers and waste management stakeholders, offering a replicable framework to balance economic growth and ecological preservation.

Enhancing Efficiency and Resilience Multiobjective Optimization Model and Algorithm for Site Selection of Emergency Material Storage Sites

Enhancing Efficiency and Resilience Multiobjective Optimization Model and Algorithm for Site Selection of Emergency Material Storage Sites

Drilling Parameter Control Based on Online Identification of Drillability and Multi-Objective Optimization

Aiming at the problem that drilling parameters are difficult to adjust in time for the driller due to the complex geological environment in underground coal mines, a drilling parameter control method based on online identification of drillability and multi-objective optimization of drilling parameters is proposed. A drillability grade identification model is established, with rotational speed and torque as input parameters, which can accurately identify the current drilling state. A multi-objective optimization model of the optimal drilling parameters is established with the mechanical specific energy and drilling speed prediction model as the objective functions, and the NSGA-II algorithm and TOPSIS algorithm are used for solutions and decision-making. A fuzzy PID controller is established. For the control of rotational speed parameters and drilling pressure parameters, the advantages and disadvantages of the fuzzy PID control method and the traditional PID control method are compared through simulation and experiments. A control method based on the drillability identification model and the multi-objective optimization model is established. According to the different drillability grades, the drilling parameters are adjusted in time to ensure the normal drilling state. By constantly approaching the optimal parameters through the drilling parameters, the drilling efficiency is improved. Through experimental verification, this control method effectively prevents the occurrence of drilling speed reduction and intermittent sticking and can adjust the drilling parameters to continuously optimize.

Robust fuzzy dynamic integrated environmental-economic-social scheduling considering demand response and user’s satisfaction with electricity under multiple uncertainties

Aiming at the problems that the uncertainty modeling process of price-based demand response is oversimplified, inconsistent for change pattern of error, and the reliability in user’s comprehensive satisfaction is neglected in the optimal scheduling for power system under multiple uncertainties, integrated factors of economy, environment and society, a multi-objective robust fuzzy optimal scheduling model considering power demand response and user’s comprehensive satisfaction with electricity under multiple uncertainties is proposed. On the basis of analyzing the operation mechanism of the multi-source system and the characteristics of multiple uncertainty sources, the robust theory is used to construct power output model of wind and photovoltaic (PV), and the fuzzy theory is used to construct power demand response model. Taking the lowest comprehensive operation cost as the economic objective, the smallest emissions of CO2 and atmospheric pollutants as environmental objective and the largest user’s comprehensive satisfaction with electricity as the social objective, based on the robust fuzzy theory, the multi-objective uncertainty optimal scheduling model is constructed, which is transformed into deterministic model and then solved by intelligent optimization algorithm. Based on the improved IEEE-39 node system to verify the validity and superiority of the price-based demand response uncertainty modelling method and multiple uncertainty modelling method in this paper, as well as the reasonableness and necessity for considering the reliability in the user’s comprehensive satisfaction with electricity and the multiple uncertainties in power system optimal scheduling.

Research On Optimal Water Level of Great Lakes Based on Dynamic Optimization Model and PID Control Algorithm

This study aims to determine the optimal water levels of the Great Lakes by analyzing historical data from 2012 to 2022, including water levels of the lakes and flow rates of the rivers connecting them, to identify underlying trends. A multi-objective optimization model is developed, which holistically accounts for the welfare of local ecological, economic, and social stakeholders. By applying a genetic algorithm, the model calculates the optimal water levels for different times of the year. Furthermore, a dynamic relationship among the Great Lakes is modeled using differential equations, and a PID control algorithm is implemented to manage the optimal water levels under natural conditions. Compared with traditional algorithm, this approach not only enhances the precision of water level predictions but also introduces an adaptive control system that responds to real-time data. The integration of these methods allows for a more nuanced management of water levels, catering to the fluctuating demands of ecological, economic, and social stakeholders. A subsequent sensitivity analysis reveals that the proposed control algorithm demonstrates a high sensitivity to varying natural conditions and yields water levels more favorable than the actual recorded levels from the perspective of most stakeholders.

Research on Summer Maize Irrigation and Fertilization Strategy in Henan Province Based on Multi-Objective Optimization Model

Research on Summer Maize Irrigation and Fertilization Strategy in Henan Province Based on Multi-Objective Optimization Model

A multi-objective optimisation model for pump scheduling using renewable solar energy in water distribution networks

A multi-objective optimisation model for pump scheduling using renewable solar energy in water distribution networks

Enhancing unmanned aerial vehicles logistics for dynamic delivery: a hybrid non-dominated sorting genetic algorithm II with Bayesian belief networks

Abstract To address the complexities of managing networks of unmanned aerial vehicles (UAVs) and Just-in-Time problem solving, this study introduces a cutting-edge multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, significantly enhancing the efficiency of UAV operations. It reduces battery consumption and transportation costs while optimizing delivery times and reducing operational risks. The model improves the refinement of delivery schedules by accounting for uncertain traffic scenarios, thereby increasing its accuracy and reliability in dynamic environments. Additionally, a Bayesian belief networks approach for risk assessment introduces a new layer to operational risk management. The model’s performance and its trade-offs are demonstrated through advanced data visualizations such as 3D Pareto fronts, pair plots, and network graphs, with validation via the NSGA-II approach confirming its reliability and practical applicability. This research represents a major leap forward in drone routing strategies, focusing on efficiency, adaptability, and risk management in UAV logistics and provides a comprehensive framework that bridges the gap between theoretical exploration and practical application.

Application of ACO in financial statement quality evaluation: From the perspective of corporate governance

This paper proposes an improved Ant Colony Optimization (ACO) algorithm to enhance the efficiency and accuracy of financial statement quality evaluation. Traditional ACO suffers from slow convergence and the risk of falling into local optima, particularly in multi-objective optimization problems involving complex financial data. To address these limitations, the proposed method incorporates Principal Component Analysis (PCA) for feature extraction, reducing the impact of redundant data and providing clearer input for optimization. A dynamically adjusted pheromone update strategy and a local search mechanism are introduced to strengthen the algorithm’s global search capability and prevent premature convergence. A multi-objective optimization model is developed, incorporating key financial indicators such as profitability, debt-paying ability, and management efficiency, with differentiated weights assigned to each objective based on its relevance in corporate governance. The algorithm’s performance is optimized through cross-validation and parallel computing, accelerating execution speed while minimizing time overhead in large-scale data processing. Experimental results demonstrate that the improved ACO algorithm achieves stable performance, converging close to the optimal solution within 40 iterations with an objective function value of approximately 0.9. Additionally, the algorithm yields a lower Mean Squared Error (MSE) of 0.285 across 10 different discount scenarios, indicating superior quality in financial statement evaluation. These findings validate the effectiveness of the proposed method in enhancing financial statement quality assessment.