Grey Target Research Articles

Study on the Optimization of Site Selection for Emergency Supply Reserve Warehouses Based on Multi-Attribute Weighted Intelligent Gray Target Decision-Making Evaluation Model

To reasonably allocate emergency supply reserve warehouses and enhance the utilization efficiency of emergency supply reserves, this paper proposes a new optimization method for the site selection of emergency supply reserve warehouses based on gray system theory—the multi-attribute weighted intelligent gray target decision-making evaluation model. This method innovatively incorporates the density of road networks, population density, economic density, and post-disaster response capability as key indicators. It then utilizes the multi-attribute weighted intelligent gray target decision-making evaluation model to evaluate the synthetic effect. Based on the evaluation results, optimization recommendations for the site selection of emergency supply reserve warehouses are provided. To validate the credibility of the proposed method, a comparative analysis is then conducted using the EEM-TOPSIS and TOPSIS–Gray Correlation Degree methods, resulting in largely consistent evaluation results. The study demonstrates that the multi-attribute weighted intelligent gray target decision-making evaluation model accounts for both hitting-the-target and missing-the-target scenarios for effect values and vectors of objectives. This approach effectively addresses the limitation of traditional multi-attribute evaluation methods, which can only rank evaluation schemes without effectively distinguishing between superior and inferior ones. This method also proves to be more user-friendly compared to others.

Performance Study of Asphalt Mixtures Reinforced with Gradated Basalt Fibers of Mixed Lengths

The length of basalt fibers affects the performance of asphalt mixtures. To explore the influence of different lengths of basalt fibers on the performance of asphalt mixtures, this study selected basalt fibers with lengths of 6 mm, 9 mm, and 12 mm to design gradations that were incorporated into asphalt mixtures to prepare specimens. High-temperature rutting tests, immersion Marshall tests, freeze-thaw splitting tests, and low-temperature splitting tests were conducted, resulting in 11 test mix designs and 12 test indicators. Then, a multi-objective grey target decision method was used to optimize the optimal combination ratio of basalt fiber lengths. The results indicate that compared to asphalt mixtures with single-length basalt fibers, incorporating well-combined basalt fibers significantly enhances the high-temperature, low-temperature, and water stability performance of asphalt mixtures. According to the grey target decision method, this study determined that a basalt fiber combination ratio of 3:4:3 for lengths of 6 mm, 9 mm, and 12 mm provides the best overall performance of asphalt mixtures. Additionally, when designing asphalt mixtures with graded basalt fibers, the inclusion of 9 mm fibers should be the primary control point. These research findings provide important guidance for the enhanced application of basalt fibers in road engineering.

A grey target performance evaluation model for aeroengine pressure test bench

PurposeThis paper aims to optimize the air pressure regulation scheme of the aeroengine pressure test bench.Design/methodology/approachBased on the requirements of pressure regulation process and the operating mechanism of aeroengine pressure test bench, a grey performance evaluation index system is constructed. The combination of principal component analysis and grey theory is employed to assign weights to grey indexes. The grey target evaluation model is introduced to evaluate the performance of historical regulation processes, and the evaluation results are analyzed to derive optimization mechanism for pressure regulating schemes.FindingsA case study based on monitoring data from nearly 300 regulation processes verifies the feasibility of the proposed method. On the one hand, the improved principal component analysis method can achieve rational weighting for grey indexes. On the other hand, the method comparison intuitively shows that the proposed method performs better.Originality/valueThe pressure test bench is a fundamental technical equipment in the aviation industry, serving the development and testing of aircraft engines. Due to the complex system composition, the pressure and flow adjustment of the test bench heavily rely on manual experience, leading to issues such as slow adjustment speed and insufficient accuracy. This paper proposes a performance evaluation method for the regulation process of pressure test bench, which can draw knowledge from historical regulation processes, provide guidance for the pressure regulation of test benches, and ultimately achieve the goal of reducing equipment operating costs.

Urban flood-bearing vulnerability evaluation based on the moment estimate weighting and improved gray target model.

Increasingly severe flooding seriously threatens urban safety. A scientific urban flood-bearing vulnerability assessment model is significant to improve urban risk management capacity. The gray target model (GTM) has advantages in urban flood-bearing vulnerability assessment. However, indicator correlation and single bull's-eye are commonly neglected, leading to defective evaluation results. By integrating the four base weights, an improved weighting method based on the moment estimate was proposed. Then, the marginal distance was used to quantify the indicator correlation, and the TOPSIS model was introduced to define the relative bull's-eye distance. Thus, an improved gray target evaluation method was established. Finally, an urban flood-bearing vulnerability evaluation model was presented based on the moment estimate weighting-improved GTM. In this study, Zhengzhou City, China, was taken as an example. The spatial and temporal changing characteristics of the flood-bearing vulnerability of Zhengzhou from 2006 to 2020 were investigated. The results show that: (1) On the temporal scale, the disaster-bearing vulnerability of Zhengzhou City showed an upward trend during the 15 years; (2) On the spatial scale, Guancheng District of Zhengzhou City had the relatively highest vulnerability to urban flooding. This study is expected to provide a scientific reference for urban flood risk management.

Preferential evaluation of coal filling mining scheme under building pressure based on improved grey target decision-making model of game theory

The problem of coal compression under buildings is common in underground mining of coal mines in China. The selection of traditional mining programme is subjective and lacks scientific rationality. In order to solve this problem, this paper studies the evaluation index system and model applicable to the selection of coal filling mining scheme under buildings. A multi-objective evaluation index system integrating economic, technical and adaptive factors is constructed. And an integrated optimization model is established, which is based on the traditional grey target model, combining the game theory optimal combination of weights with the hierarchical analysis method, entropy weight method, Critic method to determine the weights, and then introducing the TOPSIS model and the Mars distance to establish an improved grey target decision-making model. The validity of the evaluation index system and model is verified by taking the example of coal mining under pressure of buildings in five mining areas of a coal mine, which provides technical support for decision makers. This study helps to scientifically and reasonably carry out the preferred mining scheme of coal filling under building pressure.

Quality evaluation of surrounding rocks in a mine shaft based on the game gray target model

Accurate evaluation of surrounding rock quality grade can help guarantee the safety of tunnel design and construction; hence, it has great significance in the construction of mine shafts. Accordingly, the uniaxial saturated compressive strength of the rock block Rc, rock quality index RQD, rock softening coefficient kR, integrity coefficient of the rock mass kv, depth H, unit weight of the rock mass γ, coefficient of quantification of the angle between the principal structural plane and shaft axis k2, and weight of the groundwater k1 are first selected as the indexes of assessment to introduce the game gray target model. Then, the gray target model of the surrounding rocks in a mine shaft project is established. The weight coefficient of each index is next calculated using the combination weighting method based on game theory, and the synthetic target center distance of each sample is determined using the gray target model. Finally, the quality level of the asphalt pavement is determined. The suggested model can be used to mine a small data sample to the maximum extent possible, thereby minimizing the information shortage caused by the small sample to a certain extent, to evaluate the final quality grade of the surrounding rocks quantitatively. Thus, the proposed approach provides a new scheme for the future quality assessments of surrounding rocks.

A Multiobjective Super Conflict Grey Target Negotiation Consensus Approach

A Multiobjective Super Conflict Grey Target Negotiation Consensus Approach

Fuzzy Relationship between Kansei Images: A Grey Decision-Making Method for Product Form

Current design decision-making methods ignore the fuzzy relationship between Kansei images, and the use of constant weights reduces the accuracy of cognitive evaluation results. To solve these problems, this paper proposes a grey decision-making method for product form driven by the fuzzy relationship between Kansei images. First, according to the initial weight of the Kansei images, variable weight theory is used to determine the Kansei image variable weights of the samples, and the variable weight comprehensive evaluation results for each sample are obtained. Then, based on the correlation and angle of the Kansei images, a cobweb diagram is drawn to represent the fuzzy relationship between the Kansei images of each sample. Combined with the cobweb grey target decision-making model (CGTDM) for multiple Kansei images, decision coefficients are calculated. The decision coefficients are compared and ranked to determine the relatively optimal design reference sample. Finally, the constructed model is compared with the CGTDM for multiple Kansei images and TOPSIS. The results show that the difference coefficient of the proposed method is the largest, and it can reflect the decision-making thinking of the designers and improve the discrimination among the decision-making results to a certain extent.

Symmetric Kullback–Leibler distance based generalized grey target decision method for mixed attributes

PurposeThe reported Kullback–Leibler (K–L) distance-based generalized grey target decision method (GGTDM) for mixed attributes is an asymmetric decision-making basis (DMB) that does not have the symmetric characteristic of distance in common sense, which may affect the decision-making result. To overcome the deficiency of the asymmetric K–L distance, the symmetric K–L distance is investigated to act as the DMB of GGTDM for mixed attributes.Design/methodology/approachThe decision-making steps of the proposed approach are as follows: First, all mixed attribute values are transformed into binary connection numbers, and the target centre indices of all attributes are determined. Second, all the binary connection numbers (including the target centre indices) are divided into deterministic and uncertain terms and converted into two-tuple (determinacy and uncertainty) numbers. Third, the comprehensive weighted symmetric K–L distance can be computed, as can the alternative index of normalized two-tuple (deterministic degree and uncertainty degree) number and that of the target centre. Finally, the decision-making is made by the comprehensive weighted symmetric K–L distance according to the rule that the smaller the value, the better the alternative.FindingsThe case study verifies the proposed approach with its sufficient theoretical basis for decision-making and reflects the preferences of decision-makers to address the uncertainty of an uncertain number.Originality/valueThis work compares the single-direction-based K–L distance to the symmetric one and uses the symmetric K–L distance as the DMB of GGTDM. At the same time, different coefficients are assigned to an uncertain number’s deterministic term and uncertain term in the calculation process, as this reflects the preference of the decision-maker.

Low-carbon development of urban power grids in China: Quality assessment, obstacle analysis, and potential release

Urban power grid planning plays a pivotal role in the process of reducing carbon dioxide emissions from power grids. Traditional grey target models and analytic hierarchy processes fail to reflect the diversity of resource endowments in urban areas and cannot provide targeted solutions for carbon reduction strategies in urban power grids, representing a critical issue that urgently needs addressing. This paper introduces an enhanced grey target model based on the ‘source-grid-load’ system, establishes a quality evaluation index system for low-carbon development, and employs the Analytic Hierarchy Process - entropy weight method to account for the differentiated impacts of urban power grid partition resources. It conducts a comprehensive assessment of overall potential through multi-dimensional relative target centrality and identifies and examines key factors in low-carbon development potential using single-dimensional relative target centrality. The findings indicate that the refined grey target model, focusing on relative target centrality, effectively identifies areas with significant low-carbon development opportunities and assesses their developmental scope across various metrics, thereby providing strategic insights for carbon reduction planning in urban power grids under multi-objective decision-making conditions. This method will decrease CO2 emission by 21.38 % in 2030.

Multi-objective optimal scheduling considering low-carbon operation of air conditioner load with dynamic carbon emission factors

As global temperatures rise and climate change becomes more severely. People realize that air conditioning systems as a controllable resource and play an increasingly important role in reducing carbon emissions. In the past, the operation optimization of air conditioning systems was mainly oriented to user comfort and electricity costs ignoring the long-term impact on the environment. This article aims to establish a multi-objective model of air-conditioning load to ensure user temperature comfort performance and reduce the total cost (i.e., electricity cost and carbon emission cost) simultaneously. Multi Sand Cat Swarm Optimization (MSCSO) algorithm combined with gray target decision-making (GTD) is used to explore optimal solution. Meanwhile four competitive strategies are applied to validate the effectiveness of the proposed method, i.e., genetic algorithm (GA), MSCSO-comfort objective, MSCSO-total electricity cost objective and unoptimization. The simulation results show that the MSCSO-GTD based objective method can significantly reduce total costs while taking into account appropriate indoor temperature comfort.

Confidence relative off-targets distance-based multi-dimensional transparency evaluation of distribution station area

With the large-scale integration of distributed renewable energy into low-voltage distribution station areas, rapidly growing services put forward new requirements on transparent monitoring. In order to accurately and objectively quantify the transparency of various distribution station areas and measure the impact of different dimensional indexes on the transparency evaluation of distribution station areas, this paper proposes a confidence relative off-target distance-based multi-dimensional transparency evaluation method. First, a multi-dimensional transparency evaluation index system with electrical and communication integration is constructed. Second, an improved gray target model combining both positive and negative target vectors is established to realize bi-directional quantitative analysis. Then, the relative off-target distance is calculated based on the endowment coefficient by leveraging both subjective and objective weights. Finally, the confidence of the relative off-target distance is calculated based on fuzzy entropy to improve the reliability of transparency evaluation. The simulation results show that this method can effectively distinguish the transparency gap between different distribution station areas, identify the single dimensional index with the greatest contribution or potential, and verify the effectiveness of the proposed method applied to the transparency evaluation of distribution station areas.

A novel machinery RUL prediction method based on exponential model and cross-domain health indicator considering first-to-end prediction time

Remaining useful life (RUL) prediction is of great significance in the mechanical transmission system as it provides a basis for the predictive maintenance of bearings and gears. Recently, intelligent data-driven methods of tag-based end-to-end and based-health indicator multi-step iterative for RUL prediction have been developed. However, they still mostly suffer from some limitations: 1) The reasonable failure threshold of mechanical system is rarely considered. 2) First-to-end prediction time (FEPT) is ignored during the construction of degradation trend. To address the above drawbacks, a novel machinery RUL prediction method based on Exponential model and cross-domain health indicator (CDHI) considering FEPT is proposed. The grey target decision (GTD) is first applied to the performance evaluation of mechanical system and a weighted GTD is proposed to construct sensitive health indicators and sensitive failure indicators for determining FPT and EPT respectively. Then, to generate the CDHI considering FEPT under different working conditions, a transfer quadratic function-based ConvLSTM framework is proposed based on the degradation trend of quadratic function and maximum mean discrepancy. Finally, the Exponential model is used to perform RUL regression prediction based on the generated CDHIs. The proposed RUL prediction method has been demonstrated by five datasets and compared with several state-of-the-art approaches. Extensive experiments show that the proposed method outperforms other approaches in terms of superiority, robustness and generalization.

Construction and Confirmation of Digital Literacy Assessment Framework for Leading Cadres

Abstract This paper establishes the assessment framework of digital literacy of leading cadres based on digital competency and uses gray correlation analysis to solve the correlation degree and get the weights of assessment indexes. Based on the multi-factor gray target decision-making model to realize the comprehensive evaluation of digital literacy of leading cadres and introduce the extreme learning machine to optimize the optimal solution of its bull’s-eye distance. The digital literacy assessment framework of leading cadres given in this paper was validated by taking leading cadres in the government service center of G province as an example. The results show that the Cronbach’s alpha coefficient value of the digital literacy assessment framework for leading cadres constructed in this paper is 0.906, and the KMO value is 0.956, with good structure and reliability. Digital communication and collaboration, digital problem processing, digital security and ethics in the digital literacy assessment framework of leading cadres have an interpretive degree of 72.86%, and the multi-factor gray-target ELM model can be used to comprehensively evaluate the digital literacy of leading cadres, with a comprehensive evaluation score of 73.12 points. The digital literacy assessment framework for leading cadres gives a certain evaluation basis for the construction of digital government and provides a reference for improving the digital capacity of leading cadres.

An experimental investigation and optimization of the properties of concrete containing cellulose fiber based on system theory

This study aims to provide an approach for the optimization and prediction of the properties of cellulose fiber-reinforced concrete using system theory. First, mechanical properties, drying shrinkage, and anticrack properties of mortar reinforced with cellulose fibers (CTF960, CTF850, and CTF800) at the contents of 0.6, 0.9, 1.2, 1.5, and 1.8 kg/m3 were evaluated by a three-point bending test, direct tensile test, drying shrinkage test, and a slab method with stress risers. The optimal fiber category and dosage range were initially selected based on the performance of the mortar using the comprehensive index method. Next, the Box–Behnken design (BBD) method was used to conduct a mix design and establish prediction models for the mechanical properties, resistance to freeze–thaw cycles, and chloride ion penetration of the cellulose fiber-reinforced concrete. Finally, the optimal mix scheme was selected using the grey target decision based on the entropy weight method. The experimental results showed that CTF960 most significantly improved the performance of the mortar. When the CTF960 ranged from 0.9 to 1.5 kg/m3, the best comprehensive performance of the mortar was acquired. In addition, CTF960 with appropriate content enhances the mechanical properties and durability of concrete. According to the grey target decision results, concrete with a CTF960 content of 1.2 kg/m3, sand ratio of 0.4, and superplasticizer content of 2% exhibited optimal mechanical properties and durability.

Dynamic multi-attribute grey target group decision model based on quantum-like Bayesian networks

PurposeGrey target decision-making serves as a pivotal analytical tool for addressing dynamic multi-attribute group decision-making amidst uncertain information. However, the setting of bull's eye is frequently subjective, and each stage is considered independent of the others. Interference effects between each stage can easily influence one another. To address these challenges effectively, this paper employs quantum probability theory to construct quantum-like Bayesian networks, addressing interference effects in dynamic multi-attribute group decision-making.Design/methodology/approachFirstly, the bull's eye matrix of the scheme stage is derived based on the principle of group negotiation and maximum satisfaction deviation. Secondly, a nonlinear programming model for stage weight is constructed by using an improved Orness measure constraint to determine the stage weight. Finally, the quantum-like Bayesian network is constructed to explore the interference effect between stages. In this process, the decision of each stage is regarded as a wave function which occurs synchronously, with mutual interference impacting the aggregate result. Finally, the effectiveness and rationality of the model are verified through a public health emergency.FindingsThe research shows that there are interference effects between each stage. Both the dynamic grey target group decision model and the dynamic multi-attribute group decision model based on quantum-like Bayesian network proposed in this paper are scientific and effective. They enhance the flexibility and stability of actual decision-making and provide significant practical value.Originality/valueTo address issues like stage interference effects, subjective bull's eye settings and the absence of participative behavior in decision-making groups, this paper develops a grey target decision model grounded in group negotiation and maximum satisfaction deviation. Furthermore, by integrating the quantum-like Bayesian network model, this paper offers a novel perspective for addressing information fusion and subjective cognitive biases during decision-making.

Multi-Dimensional Collaborative Operation Model and Evaluation of Cascade Reservoirs in the Middle Reaches of the Yellow River

Reservoir operation optimization is a technical measure for flood control and is beneficial owing to its reasonable and reliable control and application of existing water conservancy and hydropower hubs, while ensuring dam safety and flood control, as well as the normal operation of power supply and water supply. Considering the beneficial functions of reservoirs, namely flood control and ecological protection, this paper firstly established a two-objective optimal operation model for the reservoir group in the middle reaches of the Yellow River. We aim to maximize the average output of the cascade reservoir group and minimize the average change in ecological flow during the operation period under efficient sediment transport conditions, with the coordination degree of water and sediment as the constraints of reservoir discharge flows. The paper aims to construct an evaluation index system for reservoir operation schemes, apply a combined approach of objective and subjective evaluations, and introduce the gray target and cumulative prospect theories. By uniformly quantifying the established scheme evaluation index system, screening the reservoir operation schemes with the fuzzy evaluation method, and selecting the recommended scheme for each typical year, this paper provides a new scientific formulation of the operation schemes of reservoirs in the middle reaches of the Yellow River. The selected schemes are compared with actual data, demonstrating the effectiveness of joint reservoir operation and for multidimensional benefits in terms of power generation, ecology, and flood control.

Risk Decision for a Port Shore Power Supply System Based on Cumulative Prospect Theory and an Improved Gray Target

Considering carbon neutral development strategies, ecological ports and green growth concepts have become the future development trend of ports. As an important technology for green port construction, the stable operation of port shore power systems is particularly important. However, as a new field of transmission system, the development of port shore power is also facing many new risks and challenges. For example, the shore power system has security risks as well as technical and economic difficulties. There are also problems such as the lack of regulations on shore power systems. To ensure the safe and stable operation of a port grid system, this paper proposes a risk decision method for a transmission system in a market environment based on interval number and cumulative prospect theory. Based on the constructed risk evaluation index system of a port shore power supply system, a risk decision model based on cumulative prospect theory and an improved gray target risk model (CPT-IGT) are constructed considering the uncertainty and ambiguity of the index attributes and weight information as well as the risk attitude held by decision-makers; these models are developed with improved interval gray number and cumulative prospect theory. Finally, a case study applied to the risk decision of a port shore power supply system verified the reasonableness and effectiveness of the method. The results show that the proposed method has some advantages in dealing with language terms, representing language uncertainty, and reflecting risk decisions in different environments. The research can provide a theoretical basis for the risk assessment of a port power system in the future and provide suggestions for the green growth of the port.

An improved quantum particle swarm algorithm for solving multi-objective fuzzy flexible job shop scheduling problem

Due to the dynamic nature of work conditions in the manufacturing plant, it is difficult to obtain accurate information on process processing time and energy consumption, affecting the implementation of scheduling solutions. The fuzzy flexible job shop scheduling problem with uncertain production parameters has not yet been well studied. In this paper, a scheduling optimization model with the objectives of maximum completion time, production cost and delivery satisfaction loss is developed using fuzzy triangular numbers to characterize the time parameters, and an improved quantum particle swarm algorithm is proposed to solve it. The innovations of this paper lie in designing a neighborhood search strategy based on machine code variation for deep search; using cross-maintaining the diversity of elite individuals, and combining it with a simulated annealing strategy for local search. Based on giving full play to the global search capability of the quantum particle swarm algorithm, the comprehensive search capability of the algorithm is enhanced by improving the average optimal position of particles. In addition, a gray target decision model is introduced to make the optimal decision on the scheduling scheme by comprehensively considering the fuzzy production cost. Finally, simulation experiments are conducted for test and engineering cases and compared with various advanced algorithms. The experimental results show that the proposed algorithm significantly outperforms the compared ones regarding convergence speed and precision in optimal-searching. The method provides a more reliable solution to the problem and has some application value.

A novel collision warning system based on the visual road environment schema: An examination from vehicle and driver characteristics

Drivers pay unequal attention to different road environmental elements and visual fields, which greatly influences their driving behavior. However, existing collision warning systems ignore these visual characteristics of drivers, which limits the performance of collision warning systems. Therefore, this study proposes a novel collision warning system based on the visual road environment schema, in order to enhance the support for avoiding potential dangers in objects and areas that are easily overlooked by the drivers’ vision. To capture the above visual characteristics of drivers, the visual road environment schema that consists of the semantic layer, the scene depth layer, the sensitive layer, and the visual field layer is established by using several different deep neural networks, which realizes the recognition, quantization, and analysis of the road environment from the drivers’ visual perspective. The effectiveness of the novel collision warning system is verified by the driving simulation experiment from six indicators, including warning distance, maximum lateral acceleration, maximum longitudinal deceleration, minimum collision time, reaction time, and heart rate. Additionally, a grey target decision-making model is built to comprehensively evaluate the system. The results show that compared with the traditional collision warning system, the novel collision warning system proposed in this study performs significantly better and can discover potential dangers earlier, give timely warnings, enhance the vehicles’ lateral stability and driving comfort, shorten reaction time, and relieve the drivers’ nervousness. By integrating the drivers’ visual characteristics into the collision warning system, this study could help to optimize the existing collision warning system and promote the mutual understanding between intelligent vehicles and human drivers.