Comprehensive Optimization Model Research Articles

A bi-level cooperating optimization for AC/DC power systems considering renewable energy integration

The integration of renewable energy on a large scale into the power system presents a significant challenge in ensuring the secure and cost-efficient operation of the system. Considering the uncertainty associated with wind and photovoltaic power, this paper develops a bi-level cooperating optimization strategy for AC-DC hybrid systems integrated with renewable energy generation. Firstly, to characterize the uncertainty of wind and photovoltaic power, the Latin hypercube sampling and the Kantorovich distance-based scenario reduction methods are employed to obtain the typical daily outputs of wind and photovoltaic power. Next, a bi-level multi-objective comprehensive optimization model is established to minimize the active power network loss and voltage deviation during system operation. To solve the bi-level optimization model, the proposed approach presents a multi-objective hybrid algorithm that combines the traditional particle swarm and whale algorithms, thereby enhancing the optimization capability of the algorithm through the incorporation of a whale enveloping search strategy into particles. Finally, the simulation results explain the effectiveness and practicability of the proposed strategy and methods, as well as the superiority of the proposed algorithm.

A sequential three-way risk sorting model with the cautionary principle under probabilistic linguistic environment

Risk assessment plays a crucial role in managing risks associated with systems, products and services. In practical risk assessment, the cautionary principle is widely adopted by individuals to prevent accidents, and the degree of caution varies with the risk level of potential hazardous activities (PHAs). Therefore, it is necessary to explore a sequential risk assessment model integrating the cautionary principle. To this end, we design an improved risk assessment model based on sequential three-way decision (STWD) model and cautionary principle. Firstly, a risk distance measure under probabilistic linguistic environment is proposed to effectively reflect varying degrees of caution when individuals analyzed PHAs of different risk levels. Then, a comprehensive risk weight optimization model is constructed on the consideration of the importance ranking and correlations of risk attributes. Subsequently, STWD and the as low as reasonably practicable (ALARP) principle are integrated to build the risk sorting model, and both the risk prioritization and classification of PHAs are obtained from the sequential risk sorting process. Finally, we verify the reliability of our proposed model through an example of a floating offshore wind turbine system. The findings validate the crucial role of cautionary principle in risk assessment.

Optimization of land use to accommodate nutritional transformation of food systems: a case study from the Beijing–Tianjin–Hebei region

The transformation and reconstruction of China’s food system not only faces many risks, such as the unceasing growth of food consumption on the demand side and the structural imbalance of dietary nutrition, but also must address serious challenges, such as constraints of resources, environment, and production capacity on the supply side. The optimal allocation of land use structure is an important method to realizing a transformation of sustainable food systems, achieving the goal of nutrition security, and guiding coordinated spatial development. This study takes the Beijing–Tianjin–Hebei region as an example, analyzing the development trends of the region’s dietary nutrition structure clarifies the objectives for improving dietary nutrition. This study uses comprehensive optimization model and dynamic land system model, exploring land use optimization schemes under different nutritional goals and development scenarios. The result show that the dietary structure in the Beijing–Tianjin–Hebei region is transitioning from “food based” to “intake balance” and gradually evolved to “intake diversity,” with the main objectives being to maintain stable calorie intake while moderately increasing protein intake and reducing fat intake. Achieving this goal will gradually increase demand for cultivated land and intensify spatial competition for land use. However, by optimizing land use allocation, it is possible to free up more spatial resources to balance economic development and ecological protection and reduce land use fragmentation, thereby significantly enhancing regional economic benefits and the value of ecosystem services based on improvements in dietary nutrition.

Coordinated reactive power planning in an industrial park integrated energy system using support vector regression

In this study, we develop and present a comprehensive multi-objective optimization model for reactive power planning in industrial park integrated energy systems (IPIES), addressing the challenges posed by extensive inductive loads. Our model prioritizes enhancing system resilience through three key objectives: minimizing total costs, improving static voltage stability margins, and enhancing voltage recovery capabilities. We employ support vector regression (SVR) to estimate voltage recovery metrics effectively, using reactive power capacity as input. The optimization process leverages the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) for efficient solution finding. Validation on the IEEE-39 bus system demonstrates the model’s effectiveness, with SVR significantly reducing computational demands while maintaining satisfactory accuracy.

Robust optimization modelling of passenger evacuation control in urban rail transit for uncertain and sudden passenger surge

ABSTRACT Effectively addressing the surge in passenger flow caused by emergencies represents a critical challenge in the emergency management of urban rail transit operations. This article introduces a comprehensive control strategy for passenger evacuation, employing robust optimization methods to tackle the uncertainties associated with sudden increases in passenger numbers. Initially, a comprehensive mathematical optimization model for train scheduling and passenger flow coordination is established to ensure efficient passenger transport while maximizing the reduction of operational costs for the operating company’s emergency response. Subsequently, the impact of uncertainty factors on the evacuation model is considered. The fluctuation in passenger flow is represented using a range of intervals, and a robust corresponding model is formulated by introducing an uncertainty budget coefficient. Furthermore, small-scale numerical examples are utilized to discuss passenger evacuation plans, conduct robustness analysis, and assess demand sensitivity. The practical case of the Beijing subway demonstrates that, in contrast to the most optimistic scenario, the robust passenger plan, accounting for a 1% fluctuation in passenger flow, exhibits a reduced cost increase from 10.96% to 9.13%, as compared to the most conservative situation.These findings contribute to enhancing the emergency response capabilities of operational management departments.

Research on Multiple Optimization of Engineering Management Models under the Concept of Environmental Protection

Abstract With the continuous development of social economy, engineering management technology is becoming increasingly mature, and multi-objective optimization technology is widely used in engineering management. Based on the traditional schedule, cost and quality model, the article introduces the environmental factor as the fourth optimization objective and constructs a comprehensive four-objective optimization model. The model starts from the concept of environmental protection, and proposes the modeling method of ecological indicators for deterministic and non-deterministic conditions. The article utilizes the multi-objective comprehensive optimization model to conduct an in-depth analysis of the engineering management condition of S project. After the engineering management optimization implementation, the optimization scheme showed that the planned duration was shortened to 324 days, which saved 12.9% of the construction time compared to the initial scheme and 1.81 million RMB in cost. In addition, the project achieved an average value of 0.9167 for quality reliability and a reduction in environmental impact to 296.48, which met the expected criteria in the S-engineering contract. The research in this paper not only provides a new type of multi-objective optimization method integrating the concept of environmental protection for project management, but also proves the effectiveness and practicability of this method in actual projects through example analysis.

A comprehensive bi-objective optimization model to design circular supply chain networks for sustainable electric vehicle batteries

<abstract> <p>As electric vehicles (EVs) continue to advance, there is a growing emphasis on sustainability, particularly in the area of effectively managing the lifecycle of EV batteries. In this study, an efficient and novel optimization model was proposed for designing a circular supply chain network for EV batteries. In doing so, a comprehensive, bi-objective, mixed-integer linear programming model was employed. It is worth noting that the current model outlined in this paper involved both forward and reverse flows, illustrating the process of converting used batteries into their constituent materials or repurposing them for various applications. In line with the circular economy concept, the current model also minimized the total costs and carbon emission to develop an inclusive optimization framework. The LP-metric method was applied to solve the presented bi-objective optimization model. We simulated six problems with different sizes using data and experts' knowledge of a lithium-ion battery manufacturing industry in Canada, and evaluated the performance of the proposed model by simulated data. The results of the sensitivity analysis process of the objective functions coefficients showed that there was a balance between the two objective functions, and the costs should be increased to achieve lower emissions. In addition, the demand sensitivity analysis revealed that the increase in demand directly affects the increase in costs and emissions.</p> </abstract>

Energy Consumption Modeling and Optimization of a Hobbing Machine Tool Considering Multi-Axis Coupling

Energy consumption of machine tools has always been a hot issue in the manufacturing sector due to the concern about climate change. To decrease the energy consumption of machine tools, this paper investigates the multi-axis coupling mechanism of a hobbing machine tool during machining from the design stage, and develops an energy consumption model under multi-axis coupling of the whole machine. Firstly, the multi-axis coupling mechanism in the actual machining process is studied. The hob speed and the feed speed of Z axis moving component will affect the hobbing force, and the component of hobbing force in each direction will affect the load and energy consumption of each axis. Meanwhile, the tracking error of each axis is reduced by a sliding mode controller. Next, a comprehensive energy consumption optimization model of a hobbing machine tool under multi-axis coupling is constructed. Furthermore, three optimization algorithms are implemented to the energy consumption model. Compared with the initial scheme, the simulation experiment results of the case-study indicate that the hobbing machine tool energy consumption is reduced by 1.19%, the steady-state error of each axis is reduced by 35.27%, 21.70%, and 36.34%, respectively, and the maximum deformation of the tool holder is reduced by 10.91%. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is dedicated to dealing with the issue of a hobbing machine tool energy consumption. Based on our previous work, the multi-axis coupling mechanism of the gear hobbing machine tool is investigated, and a comprehensive energy consumption model of the gear hobbing machine tool is proposed. The design variables of the energy consumption optimization model include the structural parameters of the Z axis moving component and the sliding mode controller parameters of the three axes (B, C, and Z axis). Three optimization algorithms are used to drive the optimization model. The simulation results verify the feasibility of the method. The control performance of the system is improved, and the mass of moving component is reduced.

Automatic Pricing and Replenishment Strategy for Vegetable Products Based on Time-Series Analysis and BP Neural Fitting

To reduce the waste of vegetables and guarantee revenues for supermarkets, this paper explores the construction of a comprehensive optimization model to help the supermarket select the specific suitable replenishment scheme and pricing strategy. This sets the bedrock for it to both gain the maximum revenue and timely supply various categories of vegetables that the market demands. The expectation for further relevant research to better complete the model is also mentioned. First, the attached data were preprocessed, and no missing values and outliers were detected. Consequently, the data of multiple forms was merged, and data related to categories of vegetables was counted. In response to Question 1, descriptive statistics were conducted on different individual items and categories of vegetables. Sales volumes of categories of vegetables were visualized through sales volume statistical graphs, profit line graphs, and quarterly sales change graphs. Following, the sales volumes were verified to have satisfied the conditions for calculating the Pearson Correlation Coefficient, and heat maps were drawn for correlation analysis. For individual items, hierarchical clustering was carried out with indicators, such as sales volume, unit price, number of purchases, and wastage rate. The basis of categorization of each category of vegetable was also explored. For Question 2, average pricing was used to replace cost-plus pricing first. Then, BP Neural Net Fitting was leveraged to analyze the relation between total sales volume and average pricing of different vegetable categories. The average wholesale price of the next seven days of each vegetable category was predicted with ARIMA Model, in order to gain the profit of different categories. Finally, a nonlinear objective planning model to achieve maximum benefit for the supermarket was constructed. Corresponding constraints were given to propose a reasonable total replenishment and pricing strategy for each vegetable category. In solving Question 3, constraints were added based on the nonlinear objective planning model in Question 2, and the prediction model was optimized. In the case of meeting market demand, the replenishment volume and pricing strategy for individual items on July 1, 2023, were proposed based on a combination of factors, as a way to maximize the benefits for the supermarket.

Double-layer optimization model for integrated energy system under multiple robustness.

Output instability is one of the important constraints limiting the large-scale application of renewable energy. The development of comprehensive energy systems can effectively improve energy utilization efficiency, but there is still a problem of randomness in renewable energy output. The paper conducts research on the uncertainty of distributed energy output and load, constructs a comprehensive energy system optimization model that takes into account the robustness of bilevel programming, and solves the model using the firefly algorithm. The calculation results show that optimizing uncertainty can significantly reduce the actual operating costs of the system, with a maximum reduction of 14.43%. When the distributed wind power interval is within [0190], a dynamic balance between cost and consumption rate can be achieved.

The optimization model of soil and water conservation ecological construction for power transmission and transformation projects in ecologically sensitive areas in Sichuan and Chongqing area in the new era is analyzed

In the construction and development of modern society, the overall characteristics of soil and water conservation ecological construction are not only reflected in the coordination of biological measures and engineering measures, but also in the sustainable development of economy and technology, which is the fundamental demand of the comprehensive treatment and optimization model of soil and water conservation for power transformation projects in ecologically sensitive areas of Sichuan and Chongqing in the new era. Therefore, after understanding the development requirements of ecological sensitive areas in Sichuan-Chongqing area, this paper mainly studies the optimization model of ecological construction of soil and water conservation according to the construction management content of power transmission and transformation projects, so as to provide technical support for urban construction and development in the new era.

Optimizing Carbon Sequestration in Forest Management Plans Using Advanced Algorithms: A Case Study of Greater Khingan Mountains

The Paris Agreement aims to combat climate change by reducing greenhouse gas emissions, with bioenergy identified as a potential solution. However, concerns remain about its impact on carbon stocks and the optimal timing for implementation. To address these challenges, we propose a comprehensive multi-objective optimization model for forest management that maximizes carbon sequestration and economic benefits. Our model integrates three key components: (1) a sophisticated carbon-sequestration model encompassing living plants, wood forest products, and soil and microbial carbon uptake, (2) dynamic factors such as forest fires and extreme weather events, and (3) an economic benefits model focused on wood-processing products. We optimized the forest-management strategy over ten years by leveraging the simulated annealing and Karush–Kuhn–Tucker (KKT) algorithms. Through simulations using data from China’s Greater Khingan Mountains region, we explored the optimal logging plans for maximizing carbon sequestration without external factors. Our results revealed that the optimized logging plans significantly enhance carbon sequestration compared to proportionally averaged logging plans. Next, we investigated the impact of external factors on forest management, specifically wildfires and extreme weather events. Our findings demonstrate that wildfires have a more-substantial detrimental effect on the absolute value of carbon sequestration and the extent of improvement achieved through model optimization. At the same time, extreme cold primarily affects the growth rate of carbon sequestration. We employed a linear-weighting approach and the Analytic Hierarchy Process (AHP) to address the trade-offs between carbon sequestration and economic benefits to transform the multi-objective optimization function into a single objective. The results showed that the optimized harvesting schedule can lead to improved economic benefits compared to uniformly harvesting trees. Moreover, the joint optimization approach enabled us to identify optimal solutions that balance carbon sequestration and economic benefits, offering sustainable forest management strategies. Our study provides valuable quantitative insights into forest management strategies that balance carbon sequestration and economic benefits, making it highly relevant for real-world applications.

NuBot: A Magnetic Adhesion Robot with Passive Suspension to Inspect the Steel Lining

The steel lining of huge facilities is a significant structure, which experiences extreme environments and needs to be inspected periodically after manufacture. However, due to the complexity (crisscross welds, curved surface, etc.) of their inside environments, high demands for stable adhesion and curvature adaptability are put forward. This paper presents a novel wheeled magnetic adhesion robot with passive suspension applied in nuclear power containment called NuBot, and mainly focuses on the following aspects: (1) proposing the wheeled locomotion suspension to adapt the robot to the uneven surface; (2) implementing the parameter optimization of NuBot. A comprehensive optimization model is established, and global optimal dimensions are properly chosen from performance atlases; (3) determining the normalization factor and actual dimensional parameters by constraints of the steel lining environment; (4) structure design of the overall robot and the magnetic wheels are completed. Experiments show that the robot can achieve precise locomotion on both strong and weak magnetic walls with various inclination angles, and can stably cross the 5 mm weld seam. Besides, its maximum payload capacity reaches 3.6 kg. Results show that the NuBot designed by the proposed systematic method has good comprehensive capabilities of surface-adaptability, adhesion stability, and payload. Besides, the robot can be applied in more ferromagnetic environments and the design method offers guidance for similar wheeled robots with passive suspension.

Reliability Optimization Method for Gas–Electric Integrated Energy Systems Considering Cyber–Physical Interactions

With the development in the field of energy and the growing demand for sustainable energy, gas–electric integrated energy systems are attracting attention as an emerging energy supply method. At the same time, with the deep application of information technology, the cyber–physical interactions of gas–electric integrated energy systems are increasingly enhanced. To this end, first, the reliability assessment indices of a gas–electric integrated energy system, which comprehensively considers the interactions between cyber–physical and different energy sources, are established in this paper to quantitatively assess the reliability level of the system under different fault and failure conditions. Second, to solve the reliability optimization problem, a comprehensive reliability enhancement optimization model is constructed in this paper, which targets the sum of the total penalties of the failure rate and average repair time modification. The impact of the cyber systems on the gas–electric integrated energy systems is transformed into a modification of the failure rate and the average repair time, and the model is solved by an adaptive Gaussian particle swarm optimization algorithm. Finally, the applicability and superiority of the adaptive Gaussian particle swarm optimization algorithm to the reliability optimization of the gas–electricity integrated energy system are verified by conducting simulation tests on the gas–electricity integrated energy system coupled with an 8-node distribution system and the 11-node natural gas system in Belgium. Furthermore, the effects of cyber systems and cyber-attacks on system reliability optimization are also analyzed to verify the effectiveness of the proposed method and the rationality of the newly defined reliability indices.

Scenario-Based Optimization of Supply Chain Performance under Demand Uncertainty

This study presents a comprehensive supply chain performance optimization model that addresses the trade-off between supply chain cost and customer service level in the distribution network. The model incorporates both deterministic and scenario-based approaches, allowing for a more realistic representation of supply chain operations. The model is applied to a case company operating in the pharmaceutical supply chain in Ethiopia. The goal is to improve the company’s supply chain performance by optimizing various factors such as establishment costs, handling costs, transportation costs, and demand satisfaction. The study considers both financial measures (supply chain cost) and non-financial measures (customer service level) to evaluate the performance of the supply chain. The results of the study demonstrate the effectiveness of the proposed model in identifying the optimal trade-off between supply chain costs and customer service levels. By comparing the results of the model with the current situation of the case company, it is determined that the company can achieve significant cost reductions of up to 25.26% while still meeting customer demands. The model also takes into account the uncertainty in demand, providing more realistic recommendations for distribution center locations, transportation planning, and order fulfillment. The implications of using this optimization model include the potential for cost savings, improved decision-making, enhanced customer satisfaction, and, ultimately, a more successful supply chain. However, the study has some limitations, including a need for further research on other objectives and considerations, such as environmental impacts and disruptions, which could be addressed in future research directions.

Study on the Comprehensive Optimization of Quantum Radar Stealth Based on the Waverider Warhead

Quantum radar is a novel detection method that combines radar and quantum technologies. It exceeds the detection threshold and poses a threat to conventional stealth targets. This work aims to derive the expression of the quantum radar cross-section of a new complex target. The calculation formula of QRCS was derived after introducing the relative photon parameters and vector dot product. Subsequently, a comprehensive optimization model of quantum stealth and lift–drag ratio based on a genetic algorithm was proposed for the waverider warhead. During the optimization process, we proposed an optimization method with the objective function of the QRCS pioneering design value and achieved better outcomes than the optimization method using the average value in terms of QRCS performance and lift–drag ratio in the important azimuths of the waverider. By changing the design variables of the waverider warhead and using this new optimization method, the QRCS of the waverider in the forward and lateral angles were minimized, remarkably improving the aerodynamic performance of the waverider. Similarly, the optimization results show that the proposed design value optimization method is feasible.

Research on decision-making of water diversion supply chain considering both social welfare and water quality utility

When water diversion projects become important part of the water network around the world, the effective operation and management of the projects play important roles in giving full play to the optimal allocation of water resources. For the operation and management of water transfer, the decision-making of water supply chain under the scenario of economic benefit, producer surplus, and water quality utility should be considered simultaneously. According to the idea of supply chain, this paper regards water transfer operation management as a water supply chain composed of water transfer companies, water supply companies, and consumers. From the perspective of social welfare and water quality utility, a comprehensive optimization and coordination decision model for water transfer is proposed. Taking the South-to-North Water Diversion Project as the research object, the cost-sharing contract is designed, and the Stackelberg game method is used to optimize the decision-making and coordination of the water supply chain. The results show that when the concern coefficient and the cost-sharing ratio are evaluated within a given feasible value region, the profits of both the water transfer company and the water supply company can be improved. The feasible value interval of the concern coefficient decreases with the increase in the cost-bearing proportion. When the concern coefficient increases, the profit of the water transfer company decreases, while profit of the water supply company, water quality, consumer surplus, water quality utility, and utility of the water transfer company increase gradually. The results provide valuable references for water transfer decision-making.

Meteorological-Data-Based Modeling for PV Performance Optimization

Developing a sustainable and reliable photovoltaic (PV) energy system requires a comprehensive analysis of solar profiles and an accurate prediction of solar energy performance at the study site. Installing the PV modules with optimal tilt and azimuth angles has a significant impact on the total irradiance delivered to the PV modules. This paper proposes a comprehensive optimization model to integrate total irradiance models with the PV temperature model to find the optimal year-round installation parameters of PV modules. A novel integration between installation parameters and the annual average solar energy is presented, to produce the maximum energy output. The results suggest an increase in energy yields of 4% compared to the conventional scheme, where tilt angle is equal to the latitude and the PV modules are facing south. This paper uses a real-time dataset for the NEOM region in Saudi Arabia to validate the superiority of the proposed model compared to the conventional scheme, but it can be implemented as a scheme wherever real-time data are available.

Electric-Gas Integrated Energy Dispatch Based on Multi-ob jective Gray Wolf Algorithm

With the development of the energy Internet, the integrated energy network of electricity-gas interconnection has become an important research object of energy optimization. To improve the energy absorption capacity and energy utilization efficiency of renewable energy in a regional integrated energy network, a comprehensive energy multi-objective optimization model containing the power to gas (P2G) is proposed in this paper. The model considers the two objectives of pollutant emission and system operating cost, and the model is solved by the Multi-objective Grey Wolf Optimizer (MOGWO). The example shows that a P2G device can improve energy efficiency

Multi-Objective Path Optimization of Highway-Railway Multimodal Transport Considering Carbon Emissions

With the increase in carbon emissions from railway transit, green transportation has attracted worldwide attention due to its low pollution and low consumption. In order to improve the transportation efficiency of multimodal transport and reduce carbon emissions, this paper makes a systematic study on the comprehensive optimization model and method of multiple transport tasks and transport modes considering carbon emissions. Firstly, an optimization model is established with transportation distance, transportation time, and carbon emission as transportation objectives. Secondly, an improved fuzzy adaptive genetic algorithm is designed to adaptively select crossover and mutation probabilities to optimize the path and transportation mode by using population variance. Finally, an example is designed, and the method proposed in this paper is compared with the ordinary genetic algorithm and adaptive genetic algorithm, which proves the proposed model and algorithm are effective. In conclusion, it is found that the present multi-objective optimization model based on the improved genetic algorithm can adjust multimodal transport plans and reduce carbon dioxide emissions, which provides a reference basis for logistics enterprises to carry out multimodal transport.