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A new omni-channel retailing mode, prepositioned warehouses, which developed rapidly in China during the COVID-19 pandemic, was introduced, and a three-level logistics network optimisation model for agricultural products was presented. This study takes into account the characteristics of the omni-channel retail model, which combines the two delivery methods of vehicle delivery and consumer in-store purchase, adds the freshness penalty incurred by fresh agricultural products in the storage process of the prepositioned warehouse, and establishes a mathematical optimisation model of the three-stage logistics network of the prepositioned warehouse that meets the characteristics of omni-channel retailing on the basis of the classical EOQ inventory model. In this work, the shortest delivery time and the smallest total cost are set as the optimisation objectives, and the multi-objective problem is transformed into a single objective through normalisation and linear weighting methods to obtain a mixed-integer optimisation model. This paper solves the problems of site selection for prepositioned warehouses, merchandise replenishment strategies, and optimisation of delivery routes based on data from real cases. Additionally, through sensitivity analysis, the management inspiration of the enterprise is obtained. [ABSTRACT FROM AUTHOR]

In this paper, production system (PS) of shale oil is optimized according to production data and indoor experiments, including core and fluid tests. Results showed that: ① Pressure drop rate at wellhead is a reasonable reference for the determination of post-fracture shut-in duration (PFSID). When pressure at a wellhead of horizontal well is relatively stable and the pressure drop less than 0.1 MPa per day for three consecutive days, PFSID ends; ② Flowback intensity of fracturing fluid affects the effectiveness of proppant underground, thus flowback intensity can be determined by the critical flow rate and safety factor of each proppant; ③ Flowback intensity should be varied during different development stages, which could be divided into four according to production gas and oil ratio(GOR) of a shale oil horizontal well: low, medium–high, high and high-low production GOR. During the stage of low production GOR, ratio of flow pressure and saturation pressure should be maintained greater than 1.0, and the initial daily liquid productivity for a hundred-meter oil-bearing lateral length in a horizontal well is 2.4 ~ 2.9 m3/d; and during the medium–high production GOR, high production GOR and high-low production GOR stages, the responding initial daily liquid productivity should be maintained between 0.8 ~ 1.0 or less than 0.8 respectively. [ABSTRACT FROM AUTHOR]

The randomness and instability of wind power bring challenges to power grid dispatching. Accurate prediction of wind power is significant to ensure the stable development of power grid. In this paper, a new ultra-short-term wind power forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long-short-term memory (LSTM) network optimized by improved whale optimization algorithm (IWOA) is proposed. Firstly, CEEMDAN is applied to decompose the power history data into several intrinsic mode functions (IMFs) and a residual (RS) to reduce the complexity and unsteadiness of the original data. Then the partial autocorrelation method is used to analyze and select the input variables of each IMF and the residual. Finally, the IWOA-LSTM prediction model is established, and the parameters of LSTM are optimized by using the improved whale optimization algorithm. Each IMF and the residual are predicted respectively. The prediction results are superimposed to obtain the final wind power prediction value. The hybrid model is applied to the ultra-short-term wind power prediction of a wind farm in northern China. The prediction results of comparison experiments with other 11 models prove the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The changes in China's stock market are inseparable from the country's economic development and macroeconomic regulation and control and have far-reaching significance in promoting China's national economic growth. Compared with the Western developed capital market, China's current stock market's main smart investment strategy still has certain defects. Based on the SVM model, this paper establishes a predictive model that combines kernel parameters and parameter optimization to model. The mesh search method, genetic algorithm, and particle swarm optimization algorithm are used to optimize the parameters of the SVM under various kernel functions such as radial basis kernel function. The algorithm and particle swarm optimization algorithm optimize the parameters of the SVM to strengthen the applicability of the model in practice. The empirical results show that under the three-parameter optimization algorithms, the prediction results are higher than the random prediction accuracy, which indicates that it is effective to optimize the model by adjusting the parameters of the SVM. Among them, the SVM using the genetic algorithm parameter optimization under the radial basis kernel function shows the better prediction effect, which is the closest to the real value in the stock market forecast. The particle swarm algorithm supports the vector machine to predict the effect is slightly lower than the grid. Search method. In addition, through comparison experiments, the guess accuracy of BP neural network is worse than that of the support vector machine model before the adjustment. Finally, this paper uses the well-trained model to plan the stock smart investment plan. [ABSTRACT FROM AUTHOR]

As a kind of particulate matter which is very detrimental to human health, the prediction of PM 2.5 has attracted increasing attention in air pollution forecasting and early warning. In this paper, density peaks clustering (DPC) is employed to select the individual models for combination forecasting, and a novel high-precision combination forecasting model named DPC-based combined model is proposed to predict PM 2.5 concentrations. Firstly, CEEMD is introduced to decompose the original P M 2.5 data into three intrinsic mode functions ( I M F s ) with different features. Then, 125 individual models are obtained by applying SVR and GRNN to these three I M F s , and the intelligent optimization algorithms are adopted to optimize the parameters of SVR. Finally, the 125 individual models are clustered by DPC based on five model evaluation indexes to select the individual model for combination forecasting. The experimental results of four Chinese cities demonstrate that the proposed model outperforms the optimal individual model and can greatly improve the prediction accuracy. Taking Dalian as an example, the MAE of the proposed model is 2.2 by clustering the 125 individual models into two groups, and the result reveals that the accuracy is increased by 65.73% compared with the minimum MAE (6.42) of the individual models. [ABSTRACT FROM AUTHOR]

With economic development and the acceleration of urbanization, China's energy demand has gradually increased and brought a lot of energy-related CO2 emissions. Energy-related CO2 emissions are affected by a variety of factors. Quantifying the correlation between energy-related CO2 and driving factors and constructing the driving factor system are conducive to predict the future energy-related CO2 emissions and analyze the impact of driving factors. In this paper, the improved grey relational analysis (IGRA) was proposed to screen the influencing factors of energy-related CO2 emissions considering the sample difference, and the factor analysis (FA) was used to reduce dimensionality of the influencing factors. Then, a carbon dioxide emission forecasting model based on the bacterial foraging optimization algorithm (BFO) and the least square support vector machine (LSSVM) was proposed. Empirical analysis results of Hebei show that the LSSVM optimized BFO significantly improves the accuracy of energy-related CO2 emissions forecasting, and IGRA-FA-BFOLSSVM model is significantly better than BP, PSOBP, SVM, and LSSVM models. The mean absolute percentage error (MAPE) of the proposed model is 0.374%. The forecasting results of the supplementary case show that the model has better generalization ability. In addition, education and technological progress have proven to be important drivers of energy-related CO2 emissions. Simultaneously, the research results can also offer more breakthrough points for policy makers to control carbon emissions. [ABSTRACT FROM AUTHOR]

This report describes a study involving the design and optimization of Earth-Mars transfer trajectories with launch constraints by taking the Chinese Mars mission Tianwen-1 as a typical case. The trajectory characteristics of the mission are investigated, and the results indicate that the launch and transfer trajectories do not match at the injection point owing to the constraints on the launch site in China, the allowed range of the launch azimuth, and the launch vehicle's limited capability. Two strategies are presented to overcome these challenges. One involves performing a single deep space maneuver during the cruise, which was the option implemented in the Tianwen-1 mission. An alternative strategy involves skillfully utilizing a lunar gravity assist to efficiently change the velocity of the probe. Herein, the orbital elements, the velocity increments of the maneuvers, and the launch windows are analyzed for each strategy. The simulations were optimized by applying the particle swarm optimization algorithm under the patched conic model and by a local optimization algorithm under a high-fidelity model; the results indicate that both strategies are efficient for the investigated mission, and the velocity increment differences arising from the two models are small. The advantages and disadvantages of the two strategies are summarized. In general, the first strategy is more robust in design, but the second approach reduces the velocity increment. The method comprising the two strategies proposed in this paper has certain relevance for other similar space missions, especially in terms of dealing with launch constraints. [ABSTRACT FROM AUTHOR]

The urban green wave traffic control system is one of the effective means to solve the problems of urban traffic jams and reduce vehicle delay. Aiming at the problems of traditional green wave traffic control in optimization computation, this paper introduces the adaptive mechanism and crossover, mutation operators to artificial fish swarm algorithm (AFSA) in order to adjust the evolution group. Through setting bulletin board measure and retention strategy, the individual status of the optimal artificial fish is recorded. In that way, the adaptive genetic AFSA is established. Finally, the paper applies this algorithm to green wave traffic control in five continuous intersections on Jianning Road, Lanzhou, China, gains one traffic control program better than traditional method through calculation, and verifies the feasibility and effectiveness of adaptive genetic-AFSA to optimize green wave traffic control system. [ABSTRACT FROM AUTHOR]

Energy saving and emission reduction are the most concerned issues in the world. Objective and accurate prediction of carbon emissions can provide reference and early warning for the implementation of the government's environmental strategy. Based on the traditional grey Verhulst model, this paper analyzes the main causes of its errors, introduces the extrapolation method to optimize the background value, and uses particle swarm optimization algorithm to optimize its parameters. In order to evaluate the performance of the models, six commonly statistical evaluation indicators are used to compare the new model with other optimization models and grey universal models, and the effect of the new model is basically better than other models. Finally, it is applied to the prediction of carbon dioxide emission of three kinds of coal in China. The results show that the increase of carbon dioxide related to raw coal slows down in 2016–2020, while the increase of carbon dioxide related to clean coal and other washed coal is 12.7097% and 19.2024%, respectively. Therefore, in order to prevent a strong rebound in carbon emissions, China should increase efforts to save energy and reduce emissions, and reduce energy consumption, especially coal consumption. [ABSTRACT FROM AUTHOR]

The accuracy of hydraulic turbine model has a direct influence on controller design and system stability. This paper presents an improved whale optimization algorithm (IWOA) and its application in parameter identification of hydraulic turbine at no-load. In IWOA, two strategies including the increase in global exploration probability and combination of immune operator are introduced to avoid local optimum. Besides, in the identification process, the adaptive modification method is developed to solve the estimated parameter range uncertainty problem. Finally, in the example of Unit 4 in Huanglongtan Hydropower Plant, China, the results of different methods are compared. Considering the indicators of cost, iteration and total computation time, the results show that IWOA has faster convergence and higher precision than WOA. [ABSTRACT FROM AUTHOR]

Impacted by groundwater, faults, lithology, and other factors, the slope of the Shengli no. 1 open-pit coalmine has been in a state of instability. Among these factors, groundwater in the quaternary aquifer is considered primary. Thus, an optimum design for dewatering well pumping rates is presented in this paper. A two-dimensional groundwater simulation model is built to characterize the groundwater flow of the study area. A steady-state model was applied to the observed data (head and discharge) to verify and calibrate the groundwater model. The pilot point method, with a regularization option provided by parameter estimation, was used to identify the hydraulic conductivity field. Afterward, a groundwater optimization model is integrated with the calibrated simulation model to realize groundwater dewatering optimization in the studied open-pit coalmine, and an optimization method called modified Pareto dominance-based real-coded genetic algorithm is adopted. Taking into account the safety of the mine, slope and dewatering wells, seepage discharge is added to objective function and the maximum aquifer saturated thickness is set as the constraint condition in the optimization model. The results indicate that the dewatering optimization procedure developed in this paper can serve as a useful template and framework for solving mining related water problems. [ABSTRACT FROM AUTHOR]

A common problem that the world faces is the waste of energy. In water pump stations, the situation is not different. Employees still use the traditional, manual, and empirical operation of the water pumps. This process gradually generates unwanted losses of energy and money. To avoid such profligacy, this paper presents two Adaptive and one Improved Multi-population based nature-inspired optimization algorithms for water pump station scheduling. The main goal here is to obtain the optimal operational scheduling of each group of pumps, wasting the minimum amount of energy. Therefore, since the objective function relies on the shaft power consumption of all the pumps running together, our aim becomes feasible. We implemented and tested the algorithms in the main water pump station of Shanghai, in China. Based on traditional multi-population based nature-inspired optimization algorithms, such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), this work adapts and improves the models to fit the complex constraints and characteristics of the system. It also compares and analyses the performance of each method used in this case study, considering the obtained results. The method which demonstrated outperformance was chosen as the best solution for the present problem. [ABSTRACT FROM AUTHOR]

The study of multiobjective optimization (MOO) in China began at the beginning of the seventies, when some researchers in Beijing and Shanghai adopted techniques in this field to deal with practical problems arising from industrial designs and management sciences for achieving good results. Subsequently, theoretical studies began. A seminar on MOO was organized by members of the Institute of Mathematics of the Chinese Academy of Sciences in 1977. Meanwhile, studies of the theory and applications of MOO were developed in many universities. Related seminars were held and courses were offered to graduate students at these universities. At the same time, many managers and industrial designers had tried to use the techniques of this branch of OR to solve practical problems arising in a variety of fields. In 1981, 1984, 1987 and 1989, national symposiums on MOO were held, each of which had approximately 100 participants; about 300 papers and reports were presented. Up to the present time, about 200 papers on MOO by Chinese scholars have been published in China or abroad. The sphere of application becomes broader and the scale of problems being solved becomes larger. In China today, there are groups of experienced researchers working in this field, in particular at the Institute of System Sciences of the Chinese Academy of Sciences, the Jiangxi University, the Jilin Technical University, the People's University, and the Shanghai Jiao Tong University. In this paper, we will give a sketch of the results obtained by our colleagues since 1979. The paper consists of seven parts. [ABSTRACT FROM AUTHOR]

We revisit in this article the Two-Fund Separation Theorem as a simple technique for the Mean–Variance optimization of large portfolios. The proposed approach is fast and scalable and provides equivalent results of commonly used ML techniques but, with computing time differences counted in hours (1 min vs. several hours). In the empirical application, we consider three geographic areas (China, US, and French stock markets) and show that the Two-Fund Separation Theorem holds exactly when no constraints are imposed and is approximately true with (realistic) positive constraints on weights. This technique is shown to be of interest to both scholars and practitioners involved in portfolio optimization tasks. [ABSTRACT FROM AUTHOR]

Severe water shortage is unacceptable for water-supply reservoir operation. For avoiding single periods of catastrophic water shortage, this paper proposes a multi-reservoir operating policy for water supply by combining parametric rule with hedging rule. In this method, the roles of parametric rule and hedging rule can be played at the same time, which are reducing the number of decision variables and adopting an active reduction of water supply during droughts in advance. In order to maintain the diversity of the non-dominated solutions for multi-objective optimization problem and make them get closer to the optimal trade-off surfaces, the multi-population mechanism is incorporated into the non-dominated sorting particle swarm optimization (NSPSO) algorithm in this study to develop an improved NSPSO algorithm (I-NSPSO). The performance of the I-NSPSO on two benchmark test functions shows that it has a good ability in finding the Pareto optimal set. The water-supply multi-reservoir system located at Taize River basin in China is employed as a case study to verify the effect of the proposed operating policy and the efficiency of the I-NSPSO. The operation results indicate that the proposed operating policy is suitable to handle the multi-reservoir operation problem, especially for the periods of droughts. And the I-NSPSO also shows a good performance in multi-objective optimization of the proposed operating policy. [ABSTRACT FROM AUTHOR]

In conjunction with socioeconomic development in watersheds, increasingly challenging problems, such as scarcity of water resources and environmental deterioration, have arisen. Watershed management is a useful tool for dealing with these issues and maintaining sustainable development at the watershed scale. The complex and uncertain characteristics of watershed systems have a great impact on decisions about countermeasures and other techniques that will be applied in the future. An optimization method based on scenario analysis is proposed in this paper as a means of handling watershed management under uncertainty. This method integrates system analysis, forecast methods, and scenario analysis, as well as the contributions of stakeholders and experts, into a comprehensive framework. The proposed method comprises four steps: system analyses, a listing of potential engineering techniques and countermeasures, scenario analyses, and the optimal selection of countermeasures and engineering techniques. The proposed method was applied to the case of the Lake Qionghai watershed in southwestern China, and the results are reported in this paper. This case study demonstrates that the proposed method can be used to deal efficiently with uncertainties at the watershed level. Moreover, this method takes into consideration the interests of different groups, which is crucial for successful watershed management. In particular, social, economic, environmental, and resource systems are all considered in order to improve the applicability of the method. In short, the optimization method based on scenario analysis proposed here is a valuable tool for watershed management. [ABSTRACT FROM AUTHOR]

Condition Based Maintenance (CBM) scheduling for power equipment is based on some types of data in power system, such as the health condition of components, the load level of the substation and Life Cycle Cost (LCC), etc. Due to the lack of necessary data, traditional CBM strategy optimization models are usually established without enough data, and the optimization results are usually difficult to obtain the enough accuracy. With the development of Chinese power industry, the data volume in power system is increasing dramatically in recent years, and the power industry is undoubtedly entering the era of Big Data. To handle and store these increasing data, more and more data management systems have been developed for different application. For example, Energy Management System, Production Management System and Management Information System have been widely applied. The multi-source data in these systems gives an opportunity to improve the accuracy and rationality for CBM optimization results. Thus, it is of great significance to make full use of these data in maintenance strategy optimization process. This paper presents a new approach to optimize the CBM strategy for components in the substation. After analyzing the interconnection relations of different types of components, the substation is divided into different maintenance units, the components in the same unit can be maintained together. To quantitatively evaluate the reliability of the components before and after repaired, two failure rate calculation models based on Health Index (HI) and age reduction factor are established respectively, and all the alternative maintenance strategies for the abnormal components are proposed based on the location and severity of the faults or defects of the components. According to the theory of LCC, the CBM optimization model is established when determines the minimum total cost as the optimization goal during the maintenance period. The total cost consists of the reparation cost, the interruption cost and the maintenance cost. Finally, an application example in a 220 kV substation is proposed, and these multi-source data are fully applied in the optimization process. The calculation results indicate that these types of data have benefit to improve the accuracy and rationality of the optimization results, and the strategy after optimized can obviously improve both the power supply reliability and the economy of the substation. [ABSTRACT FROM AUTHOR]

Notwithstanding its complexity in terms of numerical implementation and limitations in coping with problems involving extreme deformation, the finite element method (FEM) offers the advantage of solving complicated mathematical problems with diverse boundary conditions. Recently, a version of the particle finite element method (PFEM) was proposed for analyzing large-deformation problems. In this version of the PFEM, the finite element formulation, which was recast as a standard optimization problem and resolved efficiently using advanced optimization engines, was adopted for incremental analysis whilst the idea of particle approaches was employed to tackle mesh issues resulting from the large deformations. In this paper, the numerical implementation of this version of PFEM is detailed, revealing some key numerical aspects that are distinct from the conventional FEM, such as the solution strategy, imposition of displacement boundary conditions, and treatment of contacts. Additionally, the correctness and robustness of this version of PFEM in conducting failure and post-failure analyses of landslides are demonstrated via a stability analysis of a typical slope and a case study on the 2008 Tangjiashan landslide, China. Comparative studies between the results of the PFEM simulations and available data are performed qualitatively as well as quantitatively. [ABSTRACT FROM AUTHOR]

A modeling framework by linking air quality simulation with system optimization was presented in this paper to develop cost-effective urban air quality management strategies in Fengnan district of China. The relation between the total allowable emission and wind speed as well as the relation between the total allowable emission and air-quality-guideline satisfaction were quantified based on the simulation results of the Gaussian-box modeling system. The area-source emission reduction objective in each functional zone of the study city during the heating and non-heating seasons was calculated based on such relations. A linear programming model was then developed to optimize the emission abatement which was subject to a number of dust and SO2 control measures. The economic objective of the air quality management strategy was to minimize the total emission control system cost while the environmental objective can still be satisfied. The environmental objective was reflected by the emission reduction objective of TSP, PM10 and SO2 corresponding to an air-quality-guideline satisfaction percentage of 80%. Consequently, the modeling system comprehensively took into account the information of emission reduction objectives, emission abatement alternatives, emission reduction cost, and related resources constraints. An optimal emission abatement strategy and the related cost were obtained for various pollution control measures. The results would provide sound bases for decision makers in terms of effective urban air quality management and ensuring healthy economic development in the study city. [ABSTRACT FROM AUTHOR]

Age estimation based on the mineralized morphology of teeth is one of the important elements of forensic anthropology. To explore the most suitable age estimation protocol for adolescents in the South China population, 1477 panoramic radiograph images of people aged 2–18 years in the South were collected and staged by the Demirjian mineralization staging method. The dental ages were estimated using the parameters of the Demirjian and Willems. Mathematical optimization and machine learning optimization were also performed in the data processing process in an attempt to obtain a more accurate model. The results show that the Willems method was more accurate in the dental age estimation of the southern China population and the model can be further optimized by reassigning the model through a nonintercept regression method. The machine learning model presented excellent results in terms of the efficacy comparison results with the traditional mathematical model, and the machine learning model under the boosting framework, such as gradient boosting decision tree (GBDT), significantly reduced the error in dental age estimation compared to the traditional mathematical method. This machine learning processing method based on traditional estimation data can effectively reduce the error of dental age estimation while saving arithmetic power. This study demonstrates the effectiveness of the GBDT algorithm in optimizing forensic age estimation models and provides a reference for other regions to use this parameter for age estimation model establishment, and the lightweight nature of machine learning offers the possibility of widespread forensic anthropological age estimation. [ABSTRACT FROM AUTHOR]

Based on the analysis of the factors influencing the dynamic control of reservoir normal elevation in flood season (RNEFS), the evaluation index system on optimizing dynamic RNEFS schemes is established firstly. The flood disaster losses evaluation model of reservoir downstream is established with MIKE software manufactured by the DHI Company, Denmark. Then, the Biliuhe Reservoir is taken as an example, and the flood disaster risk losses of Chengzitan town in its downstream area is calculated and linked with the flood control risk, the mean annual generated energy, the utilization rate of water resources and the reliability of water-supply guaranteed rate after running up the RNEFS. Lastly, the RNEFS schemes are optimized by variable fuzzy optimization model. And the satisfying scheme obtained will provide an important reference and basis for dynamically controlling RNEFS during real-time operation of a reservoir. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are increasingly being utilized as a global optimal method in the inversion and modeling of magnetic data. We proposed the Bat Algorithm Optimization (BAO) technique that is based on bat echolocation performance to find the global optimum solution. The best-estimated source parameters that correspond to the objective function minimum value are obtained after achieving the global optimum (best) solution. The suggested BAO technique does not require any prior knowledge; rather, it is a global search method that provides an effective tool for scanning the space of data to appraise sources parameters. The BAO technique is applied to magnetic data in the class of dipping and vertical dikes along 2D profiles to estimate the dimensional source parameters that include the depth to top, origin location, amplitude coefficient, index angle of magnetization, and width of the dipping dikes. The BAO technique has been used for single and multiple dikes structures. The accuracy and stability of the BAO technique are achieved on different synthetic examples of free and noisy data for single and multiple cases. Furthermore, the presented BAO technique was effectively utilized in three field examples from China and Egypt for iron ore deposits and metavolcanics basalt rock investigations. Overall, the BAO technique recovered inversion outcomes are in good agreement with borehole, geology, and published literature results. [ABSTRACT FROM AUTHOR]

A Coarse-Grained Parallel Genetic Algorithm (CGPGA) is utilized to search for near-optimal solutions for land use allocation optimization problems in the context of multiple objectives and constraints. Plans are obtained based on the trade-off among three spatial objectives including ecological benefit, accessibility and compatibility. The Multi-objective Optimization of Land Use model integrates these objectives with the fitness function assessed by reference point method (goal programming). The CGPGA, as the first coupling in land use allocation optimization problems, is tested through the experiments with one processor, two processors and four processors to pursue near-optimal land use allocation scenarios and the comparison to these experiments based on Generic Genetic Algorithm (GGA), which clearly shows the robustness of the model we proposed as well as its better performance. Furthermore, the successful convergent (near-convergent) case study utilizing the CGPGA in Tongzhou Newtown, Beijing, China evinces the capability and potential of CGPGA in solving land use allocation optimization problems with better efficiency and effectiveness than GGA. [ABSTRACT FROM AUTHOR]

Optimization back analysis is the most common approach to displacement back analysis for underground engineering. However, this is a non-convex problem that requires the use of nature-inspired global optimization algorithms. Therefore, the present study will investigate on the suitability of six state-of-the-art nature-inspired algorithms for elastic back analysis and elastic–plastic back analysis. These algorithms include improved genetic algorithm, immunized evolutionary programming, particle swarm optimization, continuous ant colony optimization, artificial bee colony and black hole algorithm. Numerical results indicate that immunized evolutionary programming is overall the best algorithm followed by the black hole algorithm; while, the improved genetic algorithm is the worst optimizer. Meanwhile, using elastic back analysis, the sensitivity analysis of the main input parameters for these nature-inspired optimization algorithms has been conducted. At last, the comparative results have been verified by using in one real underground roadway in Huainan coal mine of China. [ABSTRACT FROM AUTHOR]

This work analyzed the potential impact on the soybean outflow Logistics from Brazilian Midwest to China caused by the Panama Canal expansion, completed in 2016. Scenarios were built out to analyze the use of this route in compare to the traditional route, which skirts the Cape of Good Hope. A mathematical optimization model based on Linear Programming was constructed using the transportation problem, considering the main soybean exporting ports, Brazilian internal logistic infrastructure and new projects to optimize logistics costs. In 2017, there was a low influence of Panama Canal on the soybean transportation between Brazil and China. However, the growth of exported soybean projections aims to the huge potential for demand to export through the North ports, due to their geographical locations, closer to the producing areas, capacity constraints in Brazilians Southern ports and new investments in Brazilian infrastructure, increasing the competitiveness of the route via the Panama Canal. [ABSTRACT FROM AUTHOR]

In order to make hydropower station's development and operation harmonious with ecological protection, the optimal operation of hydropower stations to meet the needs of ecological protection is studied. Firstly, the ecological protection function of river course is defined according to the minimum ecological runoff and suitable ecological runoff. Then, a multi-objective optimal running model of reservoir which can maximize the capacity of ecological protection and generation is proposed. Finally, an improved multi-objective particle swarm optimization algorithm (MOPSO), which can construct a neighborhood for each particle and choose the neighborhood optimal solution by adopting self-organizing mapping (SOM) method, is proposed to solve the model. The model is applied to the Shui-Kou Hydropower Station in Minjiang, China. The results show that the model can get the optimal schedule with balanced consideration of ecological benefits and power generation benefits, which has not a great impact on the economic benefits of reservoirs while achieving the goal of ecological environment. The research results can provide theoretical basis and concrete scheme reference for reservoir operation. [ABSTRACT FROM AUTHOR]

In this research, an integrated simulation-optimization modeling approach (ISOMA) was developed for supporting agricultural N2O emission mitigation at the watershed scale. This approach can successfully combine soil N2O emission simulation and the consequential mitigation management within a general modeling framework. Also, uncertainties associated with the key soil parameter can be effectively reflected and addressed through adoption of Monte Carlo analysis for the simulation results. The Monte Carlo simulated results were then used to generate fuzzy membership functions that can be consequentially used for emission mitigation management, reflecting the combined uncertainties for N2O emission simulation and mitigation management. The developed ISOMA was then applied to a reservoir watershed in Miyun county of Beijing municipality. In the studying watershed, the simulation model was calibrated and verified. Then, N2O emission from multiple agricultural land-use patterns were predicted. The amounts of N2O emission of four land use patterns (i.e., cash tree, orchard, cropland, and natural forest) were (536.9, 590.8, 653.1), (237.7, 254.4, 275.9), (79.5, 100.7, 105.1), (33.0, 47.3, 61.1) kg CO2 eq ha−1 year−1, respectively. Two scenarios (i.e., G1 and G2) were set up according to development priorities of local economy and society. Meanwhile, multiple credibility levels were considered according to the risk of N2O emission. The land use patterns could be adjusted according to solutions of ISOMA. The developed methods could help regional manager choose various production patterns with cost-effective agriculture N2O emission management schemes in the Miyun reservoir watershed. The manager also can obtain deeply insights into the tradeoffs between agricultural benefits and system reliabilities. [ABSTRACT FROM AUTHOR]

An optimization approach is used for the operation of groundwater artificial recharge systems in an alluvial fan in Beijing, China. The optimization model incorporates a transient groundwater flow model, which allows for simulation of the groundwater response to artificial recharge. The facilities’ operation with regard to recharge rates is formulated as a nonlinear programming problem to maximize the volume of surface water recharged into the aquifers under specific constraints. This optimization problem is solved by the parallel genetic algorithm (PGA) based on OpenMP, which could substantially reduce the computation time. To solve the PGA with constraints, the multiplicative penalty method is applied. In addition, the facilities’ locations are implicitly determined on the basis of the results of the recharge-rate optimizations. Two scenarios are optimized and the optimal results indicate that the amount of water recharged into the aquifers will increase without exceeding the upper limits of the groundwater levels. Optimal operation of this artificial recharge system can also contribute to the more effective recovery of the groundwater storage capacity. [ABSTRACT FROM AUTHOR]

In order to smooth ore production during the transition from open pit to underground mining at Yanqianshan Iron Mine, China, it is necessary to select an appropriate mining method to operate simultaneously open-pit mining and underground mining for inner-slope (hanging-wall) ore-body. Based on practical geologic setting of the hanging-wall ore-body, a comprehensive evaluation model for selecting the underground mining method to extract handing-wall ore-body was proposed, which is constituted by 3 sub-systems of safety factors, production factors and technical-economic factors. This evaluation model could be analyzed by the mutation progression method, which based on catastrophe theory and fuzzy mathematics. In addition, the FLAC is employed to simulate the mining process of hanging-wall ore-body, and finally the optimal mining scheme is determined. By using mutation progression analysis and numerical modeling, some useful conclusions for hanging-wall ore-body mining have been drawn, which will be beneficial to mining safety during transitional period from open-pit to underground mining. [ABSTRACT FROM AUTHOR]

Soil erosion has become a pressing environmental concern worldwide. In addition to such natural factors as slope, rainfall, vegetation cover, and soil characteristics, land-use changes-a direct reflection of human activities-also exert a huge influence on soil erosion. In recent years, such dramatic changes, in conjunction with the increasing trend toward urbanization worldwide, have led to severe soil erosion. Against this backdrop, geographic information system-assisted research on the effects of land-use changes on soil erosion has become increasingly common, producing a number of meaningful results. In most of these studies, however, even when the spatial and temporal effects of land-use changes are evaluated, knowledge of how the resulting data can be used to formulate sound land-use plans is generally lacking. At the same time, land-use decisions are driven by social, environmental, and economic factors and thus cannot be made solely with the goal of controlling soil erosion. To address these issues, a genetic algorithm (GA)-based multi-objective optimization (MOO) approach has been proposed to find a balance among various land-use objectives, including soil erosion control, to achieve sound land-use plans. GA-based MOO offers decision-makers and land-use planners a set of Pareto-optimal solutions from which to choose. Shenzhen, a fast-developing Chinese city that has long suffered from severe soil erosion, is selected as a case study area to validate the efficacy of the GA-based MOO approach for controlling soil erosion. Based on the MOO results, three multiple land-use objectives are proposed for Shenzhen: (1) to minimize soil erosion, (2) to minimize the incompatibility of neighboring land-use types, and (3) to minimize the cost of changes to the status quo. In addition to these land-use objectives, several constraints are also defined: (1) the provision of sufficient built-up land to accommodate a growing population, (2) restrictions on the development of land with a steep slope, and (3) the protection of agricultural land. Three Pareto-optimal solutions are presented and analyzed for comparison. GA-based MOO is found able to solve the multi-objective land-use problem in Shenzhen by making a tradeoff among competing objectives. The outcome is alternative choices for decision-makers and planners. [ABSTRACT FROM AUTHOR]

Geological coalbed methane (CBM) reserves buried at a depth of about 2000 m in China amount are estimated at 36.81 trillion m, ranking China as third in the world. Resources buried at depths greater than 1000 m account for 61.2% of total reserves. Compared with currently developed shallow coal seams, the deep beds have much more complex mechanical engineering characteristics: high in situ stress, high temperature, high karst water pressure, considerable mining disturbance, etc. Therefore hydraulic fracturing operations for deep coalbed methane (DCBM) are associated with problems that are not typical for shallow beds. Accordingly, in order to develop deep bed fracturing technology, we need to study types of proppants, fracturing fluids, fracturing supporting technology, etc. based on results of hydraulic fracturing of shallow coal seams. [ABSTRACT FROM AUTHOR]

We present a method for freight train gauge-exceeding detection based on three-dimensional (3D) stereo vision measurement. To reach high measurement accuracy under large-scale situation, the factors which influence the 3D measurement error are analyzed in detail. Algorithm to accurately extract the laser stripe feature projected by the measurement system is described. With the obtained stripe features, the 3D structure of the freight train can be reconstructed with nonlinear optimization procedure. Specially designed targets are used to identify the global coordinate system for gauge-exceeding detection. A prototype has been developed and the reliability and accuracy have been demonstrated by external field experiment. [ABSTRACT FROM AUTHOR]

Operating rule curves have been widely applied to reservoir operation, due to their ease of implementation. However, these curves are generally used for single reservoirs and have rarely been applied to cascade reservoirs. This study was conducted to derive joint operating rule curves for cascade hydropower reservoirs. Steps in the proposed methodology include: (1) determining the optimal release schedule using dynamic programming to solve a deterministic long-term operation model, (2) identifying the forms of operating rule curves suitable for cascade hydropower reservoirs based on the optimal release schedule, (3) constructing a simulation-based optimization model and then using the non-dominated sorting genetic algorithm-II (NSGA-II) to identify the key points of the operating rule curves, (4) testing and verifying the efficiency of the generated joint operating rule curves using synthetic inflow series. China's Qing River cascade hydropower reservoirs (the Shuibuya, Geheyan and Gaobazhou reservoirs) were selected for a case study. When compared with the conventional operating rule curves, the annual power generation can be increased by 2.62% (from 7.27 to 7.46 billion kWh) using the observed inflow from 1951 to 2005, as well as by about 1.77% and 2.52% using the synthetic inflows generated from two alternative hydrologic simulation methods. Linear operating rules were also implemented to simulate coordinated operation of the Qing River cascade hydropower reservoirs. The joint operating rule curves were more efficient and reliable than conventional operating rule curves and linear operating rules, indicating that the proposed method can greatly improve hydropower generation and work stability. [ABSTRACT FROM AUTHOR]

comprehensive indicator model for risk assessment and a multiple-level theoretical indicator system of the water quality-quantity-ecosystem (WQQE) for the Haihe River basin were constructed in this research. A fuzzy optimization model was used to assess risks for the four water systems of the Haihe River basin, and their risk order from high to lower risk was southern Haihe River system (SH), northern Haihe River system (NH), Tuhaimajiahe River system (TH) and Luanjiyanhai River system (LJ). The highest risk value (SH) was 0.8737. In terms of the WQQE, the secondary parameters for assessment of the four water system risks were 0.3579, 0.7226, 0.9547, and 0.5428 respectively. The results indicated that the main control factors for pollution for LJ, TH, SH and NH differed from each other and involved pollutant level, development of water resources, water flow and quality, ecosystem health and the hydrologic structure. [ABSTRACT FROM AUTHOR]

The water supply to Chinese cities is increasingly degrading from pollution due to watershed activities. Consequently, water source protection requires urgent action using optimal land-use management efforts. An inexact linear programming model for optimal land-use management of surface water source area was developed. The model was proposed to balance the economic benefits of land-use development and water source protection. The maximum net economic benefit (NEB) was chosen as the objective of land-use management. The total environmental capacity (TEC) of rivers and the minimum water supply (MWS) were considered key constraints. Other constraints included forest coverage, government requirements concerning the proportions of various land-use types, soil loss, slope lands, and technical constraints. A case study was conducted for the Songhuaba Watershed, a reservoir supplying water to Kunming City, the third largest city in southwestern China. A 15-year (2006 to 2020) optimal model for land-use management was developed to better protect this water source and to gain maximum benefits from development. Ten constraints were involved in the optimal model, and results indicated that NEB ranged between 893 and 1,459 million US$. The proposed model will allow local authorities to better understand and address complex land-use systems and to develop optimal land-use management strategies for balancing source water protection and local economic development. [ABSTRACT FROM AUTHOR]

Improving the proportion of natural gas consumption of the manufacturing industry would make significant contributions to the low-carbon and sustainable development of China, which is one of the largest manufacturers in the world. However, it is very difficult to catch the trend of natural gas consumption of the concerning manufacturing industry as not enough trustable data can be collected. To fill this gap, a novel time-delayed fractional grey model is developed to forecast the natural gas consumption concerning time-delayed effect. Theoretical analysis shows it has more general formulation, unbiasedness and higher flexibility than the existing similar model. Being optimized by the Particle Swarm Optimization algorithm, the proposed model presents higher accuracy in four validation cases. Finally, it is used to forecast the natural gas consumption of the manufacturing industry of China, and the results show that the proposed model significantly outperforms the other seven existing grey models. [ABSTRACT FROM AUTHOR]