Ideal Point Method Research Articles

Evaluation system of vocational education construction in China based on linked TOPSIS analysis

In response to the task of vocational education reform in the new era, in order to cultivate innovative skilled talents, it is necessary to improve traditional evaluation methods to enhance their efficiency. This study proposes an evaluation model for vocational education construction that combines grey relational analysis and approximate ideal point method (TOPSIS). By selecting indicators and combining subjective analytic hierarchy process and objective entropy weight method for weight allocation, the weights of the indicators are determined using subjective analytic hierarchy process and objective entropy weight method. Through experimental analysis, the results showed that among the 11 analysis indicators, the weight of policy environment indicators was the highest at 0.124, while the weight of employment rate and contract signing rate indicators was the lowest at 0.050. The above results indicate that the TOPSIS comprehensive evaluation system based on grey correlation analysis has better application effects in practical scenarios and is more suitable for the development requirements of vocational education.

The Multi-Objective Shortest Path Problem with Multimodal Transportation for Emergency Logistics

The optimization of emergency logistical transportation is crucial for the timely dispatch of aid and support to affected areas. By incorporating practical constraints into emergency logistics, this study establishes a multi-objective shortest path mixed-integer programming model based on a multimodal transportation network. To solve multi-objective shortest path problems with multimodal transportation, we design an ideal point method and propose a procedure for constructing the complete Pareto frontier based on the k-shortest path multi-objective algorithm. We use modified Dijkstra and Floyd multimodal transportation shortest path algorithms to build a k-shortest path multi-objective algorithm. The effectiveness of the proposed multimodal transportation shortest path algorithm is verified using empirical experiments carried out on test sets of different scales and a comparison of the runtime using a commercial solver. The results show that the modified Dijkstra algorithm has a runtime that is 100 times faster on average than the modified Floyd algorithm, which highlights its greater applicability in large-scale multimodal transportation networks, demonstrating that the proposed method both has practical significance and can generate satisfactory solutions to the multi-objective shortest path problem with multimodal transportation in the context of emergency logistics.

Bi-objective ship speed optimization based on machine learning method and discrete optimization idea

With the introduction of increasingly stringent carbon dioxide (CO2) emission regulations in the shipping industry, shipping companies face significant pressure to reduce emissions while ensuring profitable ship operations. A practical bi-objective speed optimization (BSO) algorithm for container ships is developed based on the variable weight idea, the discrete optimization idea, and the ideal point method to meet the urgent needs of shipping companies to improve operating profits and reduce emissions. By constructing two BSO models of ship main engine fuel consumption-ship sailing time and ship operating costs (SOC)-ship CO2 emissions (SCE), the effectiveness of the BSO algorithm is confirmed through extensive optimization experiments. The results show that compared to the historical sailing results, the BSO reduces the SOC by 4.43 % without causing a significant increase in the SCE, and reduces the SCE by 0.25 %. Moreover, the results demonstrate the effectiveness and feasibility of the BSO in reducing the cost of ship operations while reducing the impact on the environment. The results provide valuable references for shipping companies in the trade-off analysis between economic and environmental benefits and optimization decision-making.

Multi-Objective Optimization with Active–Passive Technology Synergy for Rural Residences in Northern China

Due to the serious problems with energy efficiency, carbon emissions, and thermal comfort of rural residences in northern China, an optimization of active and passive heating technologies for rural residences is necessary. In this paper, an optimization for rural residences in northern China is conducted with four objectives: the whole life cycle carbon emission; the annual energy consumption through heating, ventilation, and air conditioning systems; the annual cost; and thermal comfort. In addition, the optimization model with active–passive heating technology synergy is resolved by NSGA-II genetic algorithm. The active and passive design variables, including the type of air source heat pump, orientation, the type and thickness of envelope insulation, the layer of window glass, the window-to-wall area ratio, as well as sunspace parameters are preferred to obtain the optimal solution. The results indicate that the optimal solution obtained by the ideal point method gives the most outstanding performance. Compared with the prototype, the optimized carbon emissions in severe cold and cold regions decreased by 56.1% and 54.6%, respectively. The annual energy consumption decreased by 59.7% and 62.2%. Finally, the roof insulation thickness is the most sensitive design variable in Pareto-optimal solution sets. This paper offers significant guidance in the application of the optimization method of active–passive technology synergy to the energy-saving design of buildings.

Integrated energy system evaluation and optimization based on integrated evaluation model and time-series optimization

Against the backdrop of global attention to climate change and environmental sustainability, the development timing and comprehensive cost of regional renewable energy power generation projects have become a focus of attention. By constructing effective models to evaluate them, it can help promote the healthy development of renewable energy projects. The research aims to quantitatively evaluate the development status of local renewable energy projects by constructing a comprehensive evaluation model, minimize information loss, and improve the accuracy of evaluation results. This study adopted a comprehensive evaluation model that combines Analytic Hierarchy Process (AHP) based on accelerated genetic algorithm, entropy weight method, and ideal point method. Firstly, the subjective weights of the development evaluation indicators for regional renewable energy power generation projects are calculated. Secondly, the entropy weight method is used to analyze the trend of each indicator and obtain objective weights. Finally, combined with the objective weights and the evaluation results calculated using the TOPSIS method, a comprehensive evaluation of renewable energy power generation projects in various regions is conducted. Through analysis, the core indicators of the development level of renewable energy power generation projects in various regions show specific performance, such as Hebei’s evaluation value of 0.4945 in the proportion of comprehensive energy development, and Inner Mongolia’s evaluation value of 0.4045 in the proportion of comprehensive energy installed capacity. Meanwhile, genetic optimization methods exhibit significant advantages in the calculation of optimization schemes compared to dynamic programming methods, possessing strong global search capabilities and high-precision solutions. This study provides a new research method and approach for the evaluation of regional renewable energy power generation projects, demonstrating the practical value and certain advantages of the research method.

Multi-objective optimization of residential building energy consumption, daylighting, and thermal comfort based on BO-XGBoost-NSGA-II

The energy consumption, daylighting, and thermal comfort of buildings directly affect the three key goals of residents. However, there is little research on the optimization of energy consumption, daylighting, and thermal comfort in residential buildings in China. Therefore, this study proposes an optimization framework that combines Bayesian optimization with extreme gradient boosting trees (BO-XGBoost) and non-dominated genetic algorithm-II (NSGA-II) to study the multi-objective optimization of residential building performance. This paper first uses Grasshopper to simulate and obtain a dataset through Latin hypercube sampling (LHS). BO-XGBoost is used to establish the regression relationship between building envelope design parameters and residential building performance. Then, the obtained regression model is used as the fitness function of NSGA-II to get the Pareto optimal solution set. Finally, the ideal point method is used to obtain the optimal combination of building envelope structure parameters for residential buildings. Taking a residential building in a hot summer and cold winter area as an example, the effectiveness of this method is verified. The results show that (1) BO-XGBoost has excellent predictive performance, with R2 values of 0.997, 0.960, and 0.994 for energy consumption, thermal comfort, and daylighting, respectively. (2) The proposed BO-XGBoost-NSGA-II can effectively achieve multi-objective optimization. Compared with the initial scheme of the case building, energy consumption is reduced by 44.1%, thermal comfort index is reduced by 10.9%, and daylighting performance is improved by 1.7%. Therefore, the proposed method can effectively optimize the performance goals of residential buildings and provide practical ideas for similar problems.

Modified ideal point method for estimating minimum environmental flow of rivers

Modified ideal point method for estimating minimum environmental flow of rivers

Multi-objective optimization of dew point indirect evaporative coolers for data centers

This study introduces a novel counter-flow dew point indirect evaporative cooler (DPIEC) tailored for data center cooling. A numerical model for DPIEC was developed in COMSOL and validated experimentally, followed by the formulation of regression models using response surface methodology. Eight critical design parameters (five operational and three geometrical) were linked to three performance evaluation indexes (outlet primary air temperature T1,out, coefficient of performance COP, and cooling capacity per unit volume Qv). The magnitude of parameter influence on the evaluation indexes was assessed. Employing the three regression models as objective functions, a genetic algorithm, implemented in MATLAB, was adopted to perform multi-objective optimization for DPIEC under four climatic conditions, yielding an array of optimal parameter combinations. The results underscored the robust predictive accuracy of the established regression models, with R2 and Adeq Precision exceeding 0.96 and 53.7, respectively. The ideal point method achieved design solutions of the DPIEC with T1,out below 21.6 °C, COP above 61.3, and Qv over 15.5 kW/m3. Compared to the original design, the optimized design featured reductions of over 13.84 %, elevations of over 23.50 %, and enhancements of over 104.10 % in these three indexes, respectively. In addition, the confirmation study of the Pareto front proved the reliability of the obtained DPIEC design reference.

A modification of double frontier ideal point method for Malmquist productivity index in the investigation of eco-innovation in transportation industry

A modification of double frontier ideal point method for Malmquist productivity index in the investigation of eco-innovation in transportation industry

Aptenodytes forsteri optimization algorithm for low-carbon logistics network under demand uncertainty.

As China's "double carbon" goal continues to advance, logistics as a key area of carbon emissions and low-carbon logistics center site selection are key links in the process. However, existing studies on logistics center location often ignore the impact of demand uncertainty, which leads to a waste of resources in the planning and construction processes. We take logistics cost and carbon emission as the objectives, and the multi-objective site selection model established based on stochastic programming theory takes demand uncertainty as a stochastic constraint. We transform the stochastic constraint model into a 0-1 mixed integer multi-objective planning model by utilizing the idea of equivalence transformation. The Aptenodytes Forsteri Optimization (AFO) algorithm is combined with the Ideal Point Method to solve the model, and the algorithm is compared with the Particle Swarm Optimization (PSO), Differential Evolutionary (DE), Tabu Search (TS), Sparrow Search (SS) algorithms, and the exact solver Linear Interactive and General Optimizer (LINGO). The examples verify the validity of the models and algorithms, with an average reduction of 6.2% and 3.6% in logistics costs and carbon emissions in the case of demand determination, and at the confidence level of 0.9 under demand uncertainty, both logistics costs and carbon emissions are decreased to varying degrees. This study provides a new research idea for the low-carbon logistics location problem under demand uncertainty, which helps to promote the transformation of the logistics industry to low-carbon and high-efficiency.

A multiple criteria decision-making approach for water allocation of environmental flows considering the value trade-offs - A case study of Fen River in China

In the water-scarce basin, the allocation of environmental flows needs to achieve the equilibrium between ecological protection and economic development. One of the challenges is the lack of quantitative analysis of the value of water in the economy and ecosystem, which could not effectively support the decision-making. This paper proposed a new multi-criteria decision-making approach that considers the value trade-offs between the environmental flows and the economic water use of rivers. The value of environmental flows was assessed using the modified equivalent factor method combined with the hydraulic method, which considers the influences of the hydraulic characteristics of rivers on value assessment. The value of the economic water use was estimated using the modified Cobb-Douglas production function. The ideal point method was applied to obtain the optimal solution of the multi-criteria decision with the objective of maximum values of the economic water use and maximum values of environmental flows (or the value of multiple recommended environmental flows). This new method was applied to determine the optimal environmental flows in the Fen River, the second-largest tributary of the Yellow River of China. The results indicated that the reasonable environmental flows were 51.1 % or 40.4 % (considering the water shortage for development) of the natural runoff of the Fen River. This work could provide a valuable reference for determining the water allocation of environmental flows in the water shortage area of China.

Multi-objective optimization of hollow fiber membrane-based water cooler for enhanced cooling performance and energy efficiency

Water-mediated evaporative cooling systems often suffer from cross-contamination, droplet carryover, and high maintenance costs. This study proposes a novel liquid cooling solution, the hollow fiber membrane-based water cooler (HFMWC), to address these issues. The primary innovation of this study lies in the optimization of key design parameters of the countercurrent HFMWC, aligning them with stringent engineering requirements. Two standard working conditions for wet cooling towers are considered. Numerical modeling and response surface methodology are used to develop regression models correlating six key parameters (water velocity, air/water ratio, fiber length, fiber inner diameter, membrane thickness, and packing fraction) with two performance evaluation indexes (outlet water temperature and consumptive electric power ratio). A genetic algorithm-based multi-objective optimization is employed to obtain Pareto fronts, representing optimal trade-offs between the two indexes. Using the ideal point method, relative optimal solutions are identified, resulting in water temperature drops of 7.56 °C and 13.07 °C, with consumptive electric power ratios of 0.0128 kW h/m3 and 0.0138 kW h/m3 for conditions Ⅰ and Ⅱ, respectively. Most solutions achieve the highest energy efficiency rating while satisfying the design temperature difference. The average deviations between optimized and simulated data for the two indexes are 0.88 % and 3.88 %, demonstrating the reliability of the derived optimal solutions for engineering design. These findings not only offer a promising way to overcome the limitations of traditional evaporative cooling systems, but also contribute to the broader field of sustainable and efficient thermal management technologies.

Exergoeconomic analysis and multi-objective optimization using NSGA-II in a novel dual-stage Selexol process of integrated gasification combined cycle

To alleviate the problem of global climate change and provide a new perspective of carbon recovery, a novel configuration of H2S concentrator adopting pressure drop method and self-stripping method in dual-stage Selexol process of integrated gasification combined cycle is developed in this study. A comprehensive thermodynamic and exergoeconomic analysis of the novel Selexol process is carried out. The total product specific cost and total exergoeconomic factor of the process are 48.54 $/hr and 7.84 %. The ideal condition of components in the context of exergoeconomic analysis is defined, and the concrete improvement directions and methods of components which are not in ideal condition are given. Towards the exergoeconomic analysis, the multi-objective optimization of the model is carried out by using the Non-dominant Sorting Genetic Algorithm-II (NSGA-II) in conjunction with Aspen Plus. Besides, the ideal optimal point method is applied to the Pareto frontier in this study to get the optimal operating conditions according to the three targets for actual operation. The multi-objective optimization results reveal that the optimal point between CO2 capture efficiency and total exergy efficiency is that CO2 capture efficiency is 93.13 % and the total exergy efficiency is 26.09 %. This study provides a reference for academic research and engineering design of novel CO2 capture systems.

Microorganism inspired hydrogels: Optimization by response surface methodology and genetic algorithm based on artificial neural network

To optimize the synthetic process of microorganism inspired multistage porous hydrogel, the mathematical analysis strategies was applied to select the optimal experimental parameters. One-factor-at-a-time (OFAT) design was used to optimize the preparation process of yeast fermentation multistage porous hydrogels. The analysis of variance (ANOVA) was applied to determine the significant influencing factors, and the results revealed that the mass ratio of yeast to glucose (Ryeast/glucose), gelation temperature of yeast fermentation (Tgelation) and reaction time (treaction) had a significant influence on responses. Box-Behnken design (BBD) based response surface methodology (RSM) was used to design experiments and build the relationship between the input parameters and output responses. Ideal point method was used to transform a multi-objective optimization problem into a single-objective optimization problem. Artificial neural network (ANN) coupled genetic algorithm (GA) were employed to further optimize and predict the optimal preparation conditions of yeast fermentation multistage porous hydrogels. The results showed ANN coupled GA was a more effective tool in the modelling and optimization of the preparation of yeast fermentation multistage porous hydrogels. The optimized preparation conditions are Ryeast/glucose 1.84, Tgelation 25.00 °C, treaction 239.97 min. These values are expected to give us the minimum density, the maximum swelling degree and compressive strength. The research content of this paper provides theoretical support and factual basis for process optimization with complex influencing factors.

Optimization algorithms for light pollution management based on TOPSIS-non-linear regularization model

Rapid urbanization and economic development have inevitably led to light pollution. However, currently the world has not yet formed a unified technical standard for light pollution, and light pollution cannot be effectively controlled when the environmental protection department is unable to operate. To effectively solve this problem, this paper establishes a combined weight ideal point method evaluation model based on TOPSIS evaluation method to obtain comprehensive index weights to evaluate the light pollution risk levels of four different land types in urban, suburban, rural and nature reserve areas in Beijing, China, and uses one-way ANOVA to test the differences among the four regions. Based on the Random Forest algorithm to determine the three variables with the top three feature importance weights, and based on the nonlinear optimization algorithm, using the SLSQP method, the optimal parameter combinations with the smallest cost are obtained after iteration, so as to put forward three feasible intervention strategies such as adjusting the design of the nightscape lighting, reducing the time of nonessential lighting, and rationally planning the layout of the city’s lighting, etc., to solve the light pollution problem, which effectively promote the urban nightscape lighting’s it effectively promotes the healthy and sustainable development of urban nightscape lighting.

Multiobjective optimization allocation of multi‐skilled workers considering the skill heterogeneity and time‐varying effects in unit brake production lines

AbstractThe optimal allocation of multi‐skilled workers in labor‐intensive industries can improve production capacity and reduce production costs. In actual production, the efficiency of workers will change with time due to their proficiency, fatigue, and other effects. In this article, we attempt to solve the problem of multi‐skilled workers allocation in unit brake production lines considering the heterogeneity of skills and time‐varying effects. A nonlinear mixed‐integer programming model is established, which fully considers the impact of worker efficiency due to proficiency, fatigue, and multi‐task rest recovery. The product production cycle and worker cost are the two objectives of the optimization solution. An enhanced NSGA‐II algorithm that combines the improved NSGA‐II algorithm and the variable neighborhood search (VNS) algorithm is used to solve the multiobjective optimization problem. Finally, the weighted ideal point method is used to obtain the Pareto optimal solution. The application case of a unit brake production is considered to evaluate the proposed model. The results indicate that the time cost and salary cost of workers are reduced by 8.03% and 18.91% compared with the original scheduling. The scheduling model considering learning, fatigue and recovery factors is more suitable for the actual production situation, ensuring the completion time and reducing the labor cost.

Multiobjective-Based Decision-Making for the Optimization of an Urban Passenger Traffic System Structure

Urbanization has aggravated the conflict between continuously increasing urban travel demands and limited supply. Moreover, the inability to expand urban roads due to previous land planning and utilization has resulted in significant traffic congestion, traffic safety issues, and environmental problems. To address these problems, this work attempted to develop a multiobjective model to optimize the passenger traffic system while considering carbon emissions, transport costs, and resource utilization. In addition, the ideal point method and entropy weight method were combined to obtain the optimal solution. Based on the operational data on traffic modes and travel data on passengers in Harbin, the northern capital of China, the proposed method was used to solve the case in Harbin. The results show that the proportion of buses increased by 1.05%, that of subways increased by 36.60%, that of taxis decreased by 11.86%, and that of private cars decreased by 25.78% after optimization. Furthermore, the analyses of the results show that the optimized passenger traffic system structure can promote the sustainable development of urban transport and demonstrate the practicality of the proposed method for solving multiobjective optimization problems. Relative to the ideal point method and genetic algorithm, the proposed method is more applicable for optimizing the passenger traffic structure in Harbin. In addition, this study explored the sensitivity of the optimization goals to the four motorized modes. The results show that subways and private cars are the key areas to prioritize in adjusting the urban passenger traffic system structure. Based on the analysis results, recommendations for the development of transportation in Harbin are given. This study provides a reference for decision-makers to formulate policies for the urban sustainable development of Harbin as well as for transportation development in other cities.

An integrated framework for residential layout designs: Combining parametric modeling, neural networks, and multi-objective optimization for outdoor activity space optimization

The quality of life of residents and community vitality is significantly enhanced by outdoor activity spaces within residential areas. Existing residential layout designs primarily focus on a restricted set of predefined layouts and most outdoor evaluation indices are limited to thermal comfort, thereby leading to an incomplete evaluation system. Furthermore, there is a scarcity of studies offering a comprehensive design process, from parameterized generation to performance evaluation. As such, there is a pressing need for an all-encompassing design framework that investigates a broad array of layout possibilities while providing multidimensional evaluations based on diverse metrics, thereby augmenting the accuracy and intelligence of residential layout designs.This research introduces a building layout generation framework that integrates parametric modeling (PD), neural network modeling (ANN), and multi-objective optimization (MOO) methods. The framework utilizes parametric design methods to construct a geometric model that adheres to building codes. Following this, it builds separate neural network models for three key metrics: sky view factor (SVF), sunshine duration, and noise, this approach is aimed at accelerating the evaluation process. This alternative model serves as the objective function for multi-objective optimization, identifying the optimal building layout that balances multiple metrics. Additionally, the framework utilizes the ideal point method to optimize the final Pareto solution set, thereby informing designers' and planners' program selections.Case studies of this framework indicate that the residential layouts generated can adhere to building codes while effectively balancing the three crucial metrics. Relative to the conventional modeling-evaluation process, this framework augments efficiency substantially. The design framework presented herein will offer comprehensive decision support for policy makers to optimize residential layout designs, further fostering the harmonious coexistence of people and cities, while advancing intelligent building design and sustainable urban development.

A Novel Method of Self-Healing Concrete to Improve Durability and Extend the Service Life of Civil Infrastructure

Concrete civil infrastructure often suffers severe damage to its internal structure due to insufficient durability in high and cold complex environments, affecting the service life of the infrastructure. Therefore, a novel method based on nanofillers for self-healing concrete is proposed to optimize the durability mix proportion of high-performance concrete in complex high and cold environments, which improves the strength recovery rate of concrete. Moreover, a concrete durability prediction model based on particle swarm optimization-least squares support vector machine (PSO-LSSVM) and improved NSGA-II (nondominated sorting genetic algorithm II) algorithm was proposed to quickly and accurately determine the optimization scheme of self-healing concrete mix proportion. First, the model employs PSO-LSSVM to achieve highly accurate predictions of relative dynamic elastic modulus and chloride ion permeability coefficient, which are key indicators of the concrete durability. Subsequently, the predicted regression functions for concrete durability are utilized as fitness functions, and the improved NSGA-II algorithm is employed to obtain the optimal mix ratio for durable concrete. Finally, the Pareto frontier solution set is processed using the ideal point method selection approach to determine the optimal concrete mix ratio scheme. To assess the self-healing ability of the proposed concrete, a concrete durability test study is conducted. Experimental research shows that the durability of the proposed self-healing concrete has been significantly improved. The optimized PSO-LSSVM model demonstrates excellent generalization capability. The coefficient of determination between the predicted and actual values in the test dataset is 0.9357, and the root-mean-square error is 0.10267. Building upon this, the enhanced concrete durability prediction model based on the NSGA-II algorithm proves to be highly effective in predicting the optimal concrete mix proportion scheme. The predicted values of chloride ion permeability coefficient and relative dynamic elastic modulus of the proposed model differ from the actual experimental values by 1.29% and 0.59%, significantly better than the other prediction models.

A Multi-Stage Decision Framework for Optimal Energy Efficiency Measures of Educational Buildings: A Case Study of Chongqing

Buildings consume large amounts of energy resources and emit considerable amounts of greenhouse gases, especially existing buildings that do not meet energy standards. Building retrofitting is considered one of the most promising and significant solutions to reduce energy consumption and greenhouse gas emissions. However, finding suitable energy efficiency measures for existing buildings is extremely difficult due to the existence of thousands of retrofit measures and the need to meet various objectives. In this paper, a multi-stage decision framework, including a multi-objective optimization model, and a ranking method are proposed to help decision-makers select the optimal energy efficiency measures. The multi-objective optimization model considers the economic and environmental objectives, expressed as the retrofit cost and energy consumption, respectively. The entropy weight ideal point ranking method, an evaluation and ranking method that combines the entropy weight method and ideal point method, is adopted to sort the Pareto front and make a final decision. Then, the proposed decision framework was implemented for the retrofit planning of an educational building in Chongqing, China. The results show that decision-makers can quickly identify near-optimal energy efficiency measures through multi-objective optimization and can select suitable energy efficiency measures using the ranking method. Moreover, energy consumption can be reduced by building retrofitting. The energy consumption of the case building was 64.20 kWh/m2 before retrofitting, and the value can be reduced by 6.79% through retrofitting. Furthermore, the reduction in building energy consumption was significantly improved by applying the decision framework. The highest value of energy consumption was 59.84 kWh/m2, while the lowest value was 27.11 kWh/m2 when implementing the multi-stage decision framework. Thus, this paper provides a useful decision framework for decision-makers to formulate suitable energy efficiency measures.