The deal structure of loan guarantee market refers to the distribution patterns of participants' behaviors, which depends on the implementation of guarantee contracts signed by the guarantor, creditor and debtor. While risk-free rate is a policy tool to regulate and control financial market, researches have not systematically examined the mechanism and effect of risk-free rate on deal structure of loan guarantee market. This paper develops a guarantee equilibrium model based on guarantor's capital structure, and finds that the change of risk-free rate will affect the deal structure of loan guarantee market. The model developed here suggests that as risk-free rate increases, the guarantor with high solvency probability (high quality guarantor) will increase their pricing, which will squeeze the benefit that debtor gained from purchasing guarantee; however, there is no impact on the guarantor with low solvency probability (low quality guarantor). As a result, high quality guarantors will exit from the market, and the reduction of high quality guarantors will thus increase the risk of the whole guarantee market. The validity of the results in this paper is well supported by the data of Chinese listed companies in 2007-2016, and therefore provides both theoretical and practical evidence to prevent the formation and evolution of risk in Chinese loan guarantee market.
Using the efficient frontier and capital market model of portfolio theory, this paper calculates the operational efficiency of commercial banks' credit business based on the non-performing loan ratio and NIM (net interest margin) of commercial banks; Using the loss of non-performing loan ratio on commercial banks' credit business income by using panel data of 16 listed commercial banks in China from 2011 to 2016. Through the comparison of the above results, we find that the operational efficiency of commercial banks' credit business based on portfolio theory proposed in this paper can better reveal the relationship between risk and return of commercial banks' credit business. In the future, commercial banks should consider and use portfolio theory in loan business.
High-quality earnings is the cornerstone of the healthy and sustainable development of the company and the financial crisis is a comprehensive performance of financial quality. Based on the theory of harmony, this paper analyzes the mechanism of financial crisis from the aspects of earnings quality and the balance of its each dimension and establishes the financial crisis' evaluation index system based on earnings quality pyramid. Considering the mutual influence between the evaluation indicators and the attribute value transfer of the constant weight comprehensive model, this paper establishes the decision-making and trial evaluation based analytic network process (DANP) double layer penalty variable weight time series financial early-warning model to judge the financial condition. Through the empirical data of listed companies, the critical interval of the financial early warning comprehensive value is[0.6494,0.6547]. Its prediction accuracy rate reaches 91.53%. The smaller the value, the greater the likelihood of the financial crisis. It has an incremental contribution to the study of the mechanism of financial crisis in theory and it helps companies supervise their financial situation in practice.
Based on the traditional two-sided market theory and the characteristics of P2P lending platforms, this paper proposes a two-stage game model to analyse different business models, especially profit models of P2P lending platforms. With the purpose of showing the continuous operating condition and developing direction of P2P lending platforms under the financial environment in China, this paper takes China's new policies about P2P into consideration, takes account of impact of policy and finds the following results:1) under the scenario in which P2P platform charges "subscription fee + transaction fee" or "subscription fee only", network externalities reduces the P2P platforms' profit when lenders are multihoming; 2) adopting the profit model of "subscription fee + transaction fee" makes a higher profit than the profit model of "subscription fee only"; 3) the prohibition in existing policies of P2P lending platforms on principal guaranteed model will protect the interests of lenders. Finally, this paper puts forward policy suggestions of the P2P lending platforms in China.
The two-slope online leasing problem is a natural generalization of the classic online leasing problem. Due to the fact that the two-slope online leasing problem of continuous-time has already been studied, we focus on the two-slope online leasing problem of discrete time in this study. Our discussion has taken the deterministic strategy and the randomized strategy into consideration. We demonstrated an optimal deterministic strategy, which could achieve competitive factor of 2-[1+(s-1)a]/s. As for the randomized strategy, we proposed the strategy of risk balanced its absolute optimality through competitive analysis. Finally, analysis and discussions are carried out for the two types of strategies, mainly based on their competitive performances. It is found that, the two-slope analysis could improve the competitive ratio of the classic online leasing problems. In addition, by taking the discreteness into consideration rather than the continuity, the effectiveness of solving online leasing problem could be much improved.
Considering the transfer of the customers' behavior, this paper has builded a stochastic dynamic programming model to study dynamic booking capacity control in the car rental system. When the customers' driving behavior can be monitored by intelligent equipment, we have researched how the price subsidy policy affected the capacity booking control process and customers' behavior. Because of the higher dimension of the model, we have proposed two dynamic programming decomposition approaches. One decomposition has approached dynamic programming by daily (ADPD), the other has approached dynamic programming by periodicity (ADPP). Finally, numerical simulations have verified the effectiveness of the proposed algorithms. The finding gives the principal of the capacity booking control, and the conclusions are as follows:1) The expectation profit of ADPP gets closer to maximum expectation profit; 2) When the customers' behavior has not been changed, the expected total profit will not increase with the increase of subsidy; 3) When the subsidy strategy increases the possibility of choosing good behavior, the increase of subsidy will increase the expected total profit of the enterprise. The results will provide a support for capacity booking control in the car rental system.
The demand data in omni-channel retail is big, different, and relevent. It will change the network structure of the traditional supply chain. In this paper, a discrete Latin hypercube sampling immune genetic algorithm is constructed to optimize the omni-channel supply chain network. Through the data processing by discrete Latin hypercube sampling, we can reduce the sample size while preserving the data characteristics. On this basis, we adopt the immune genetic algorithm to solve the multi-objective and multi-mechanism supply chain network optimization problem. This method will ensure fast convergence and avoid local extreme. The example indicates the effectiveness of the algorithm, and its feasibility in practice.
The fixed cost allocation of multinational corporations is directly related to the interests of multinational corporations, subsidiaries and their tax authorities. Therefore, how to reasonably allocate the fixed costs of multinational corporations has attracted the attentions from the related parties. Based on the characteristics of multinational corporations, this paper proposes a fixed cost allocation method for multinational corporations that considers the competition and cooperation relationship among subsidiaries. First, based on the arm's length principle, which has been widely accepted in practice, the cost allocation constraint set is built to ensure the practicability of the method. The constraint set is the same as the efficient cost allocation set in data envelopment analysis (DEA). Second, under the constraint of the arm's length principle, the corresponding multinational corporation cost allocation model is designed according to the concept of maximizing collective interests firstly and then maximizing individual interests. Finally, using the data of a multinational oil company in 2013 to verify our method. It is found that the DMU scale effect is reflected in the cost allocation result, which is beneficial to persuade DMUs to accept the allocation plan. Besides, it is also found that the alliance strategy can not guarantee that all DMUs can obtain better cost allocation results than the neutral strategy, but the alliance strategy is beneficial to improve the cost allocation results in most cases. In addition, we also find that the insensitive DMUs and sensitive DMUs are more suitable for adopting the neutral strategy and the competitive strategy, respectively. In a word, this study provides a feasible way to solve multinational corporations' cost allocation problems, and can provide effective decision support for decision makers.
To address the uncertainty of surgery duration, this paper investigates surgery planning scheduling problem with multiple servers, which proposes chance constraints of operating rooms overtime to guarantee the surgery durations of patients is no more than the time limit of operating rooms with a high probability. A stochastic chance-constrained program is proposed to determine which operating rooms to operate, and surgeries to operating rooms allocation. Based on a finite support set of the surgery duration, this paper introduces 0-1 variables to formulate the chance constraints, and derives 0-1 integer linear program counterpart. To improve the efficiency of the model, this paper presents two classes of valid inequalities and uses the longest path algorithm to separate the second class of valid inequalities, which are implemented in a branch-and-cut framework. Computational experiments based on real-life data from hospital in Beijing are conducted to verify the algorithm performance and determine the optimal planning scheme, so as to take full utilization of healthcare resources, i.e. operating rooms.
With the rapid development of Internet of Things technologies, the sensing systems which can inspect the state of key components even the whole system in time, have been installed on the most leased equipments. To minimize the maintenance cost for the lessor, a more reasonable maintenance scheme should be designed according to the health state of equipment, which can reduce unnecessary inspection and maintenance actions for the lessee. The scheme can also make sure of the reliability of leased equipment in operation as well with a few interruption. In this paper, condition-based inspection and maintenance policy is proposed for leased equipment by using the sequential inspection method. In order to minimize the expected maintenance cost, a mathematic model is derived to determine the optimal inspection cycle and preventive maintenance threshold. Except for the basic costs of inspection and maintenance, the costs of fixed penalty and resulting overtime repair penalty for leased equipment are further considered in the model. To get the solution, the inspection probability and the probability distribution function of states with maintenance intervention are derived for each unit time. Numerical experimental results verified the correction and validation of the presented condition-based maintenance strategy and the corresponding models.
Reasonable selection of black-start schemes is significant to the fast restoration of power system. Previous studies mostly focus on complete evaluation information. In this article, a comprehensive evaluation model based on symbolic data is proposed, which can address the issue of assessing black-start schemes under the condition of incomplete evaluation information. First, evaluation grade is established, and original evaluation matrix is transformed into symbolic data matrix. Then the symbolic data matrix is packaged, evaluation frequency matrix is gained, and weights of indexes are computed. Finally, weighted evaluation matrix is established based on the symbolic data matrix and weight vector, and negative ideal point approach is used to sort the black-start schemes. Experiments are carried out on the data of black-start path of Tianjin power grid, the results show that the proposed approach is effective.
A new matching method is proposed to solve the one-to-many two-sided matching problem of the selective operation patients and surgeons. First, we describe the problem of two-sided matching between selective operation patients and surgeons. Considering the surgeons' surgical quotas and surgical skills, the definition of one-to-many matching of elective surgery patients and surgeons is given. Then, according to the preference information of surgeons for different kinds of surgeries, the preference information of patients for surgeon and the aspiration level of patients, the definitions of surgeon-patient individual rational matching scheme, the surgeon-patient stable blocking pair meeting aspiration level, the surgeon-patient stable matching scheme meeting aspiration level are given respectively. Furthermore, the relationship between the surgeon-patient individual rational matching and the surgeon-patient stable matching under different aspiration level is analyzed and proved. Furthermore, a multi-objective optimization model for the surgeon-patient stable matching scheme meeting aspiration level is constructed. And a heuristic algorithm is designed to solve the model based on the elitist nondominated sorting genetic algorithm (NSGA-Ⅱ). At last, a practical example verifies the feasibility and effectiveness of the proposed method.
To take the maintenance cost and scraping income of empty containers into account, the containers are divided into different stages of lifecycle by the time they have been put into use. Based on the given container shipping route alternatives and loaded container OD, a mixed integer programming model is built to optimize the liner shipping network, the moving paths for the loaded containers, the sites for putting the new ordered containers into use, the sites for craping the old containers and which lifecycle stage of containers to choose for container transportation. The area which consists of China, Japan and South Korea, Southeast Asia, Europe, and the United States is used to do the case study. The calculation results show that 93% of the new containers are put into use in China, the ratio of containers scrapped in Europe and the United States reaches 64%, while 74% of the empty containers used for shipping cargos from China to the United States are at or near the ends of their lifecycles.
In this paper, we propose a path-based user equilibrium assignment model with ridesharing to study the impact of ridesharing activities on traffic assignment problem. In the proposed ridesharing user equilibrium model, travelers not only choose routes from origin to destination, but also decide transportation modes to minimize travelers' generalized path travel cost. The proposed model is more realistic than the existing ones on account of two additional assumptions:1) each passenger is carried by only one ridesharing driver and each driver share a ride with one rider; 2) due to ridesharing activities, both ridesharing drivers and passengers receive an extra ridesharing rewards, passengers gain ridesharing cost discount. We analyze the impacts of key parameters on equilibrium results in the Braess network. The numerical results show that ridesharing rewards and cost discount are effective measures to motivate travelers to participate in ridesharing activities.
This paper studies the location problem of battery charging and swapping facilities based on electric vehicle under battery charging mode and swapping mode. Firstly, this study builds a path planning and vehicle scheduling model without charging or battery swapping and location models with the objective of minimizing the sum of electricity cost, fixed cost, opportunity cost, and penalty cost under both charging mode and battery swapping mode. Then an improved genetic algorithm is developed to solve the path planning model and the location models of charging or swapping facilities. Finally, this paper compares and analyzes the location decision of charging or swapping facilities and the relevant distribution cost under charging mode and battery swapping mode. Results of this study show that when there is no delay caused by the charging in distribution, the delivery cost under the charging mode is lower; but when there are delays caused by the charging, improving charging speed or using battery swapping mode can lower the delivery cost. In addition, the service cost of public charging stations will significantly affect the decision-making of logistics companies on whether to build their own charging facilities or use public charging stations.
This paper constructs a knowledge transmission model on the coupled network formed by WeChat group and offline communication. The model considers the effect of the change in the number of exchange of knowledge in the WeChat group on the knowledge spreading rate in the offline subnetwork. The threshold conditions for distinguishing whether the knowledge propagates in the coupled network are derived. Further, it is verified that the transmission threshold is always a finite number. Finally, the numerical simulations of the propagation process on the coupled network are conducted based on the actual data. Results show that the transmission threshold in the offline subnet layer of the coupled network is greater than or equal to the transmission threshold in a single offline network, and less than or equal to the transmission threshold in the coupled network. The transmission threshold and the final propagation scale are larger in the model of the online communication rate changing with the number of exchange of the knowledge in the WeChat group compared with the coupled network model with a positive constant transmission rate in one layer. Study also shows that the network structure has a significant impact on knowledge transmission. If the offline sub-network is scale-free, then the transmission threshold and the final size of knowledge spreading in the coupled network will be larger compared to that is homogeneous network even if the number of knowledge owners at the initial time is small. If the offline sub-network is scale-free, then the spreading speed is faster compared to that is homogeneous network.
The irrational emotion of the parties concerned can cause the deviation of decision-making and behavior during the emergency management of unexpected mass incidents. Many experiments show that when the occurrence probability is lower, player is inclined to the pessimistic emotion. However, when the occurrence probability becomes very higher, player begins to turn the optimistic emotion. Considering the above logistic emotional utility of both players, we build a simultaneous-move game model of unexpected mass incidents with the logistic emotional utility, where the two players are the dominant group and the vulnerable group, respectively. Lastly, we make the numerical analysis based on the case of Dalian PX incident. The results show that when only the social strong group has logistic emotion, its pessimistic emotion leads to the occurrence of mass incidents. The unilateral logistic emotion of the vulnerable group is conducive to the reconciliation of the situation, especially its optimistic emotion makes the situation reach a harmonious bilateral cooperation. However, both sides having obvious emotional fluctuation aggrade the uncertainty of the situation. Therefore, local government strengthens information collection, situation assessment and emergency drills increasing the handling experience prompting the situation to converge to a stable equilibrium.
The recommendation technology based on trust relationship is applied in the field of mobile e-commerce, and the use of social trust network to realize personalized recommendation has been widely studied. The inaccurate measurement of social trust has great influence on the accuracy of recommendation system. The paper is concerned with the problem that trust mechanism is ignored the asymmetric effect brought by the distrust relationship, and then restricts the accurate measurement of social trust. A novel algorithm of recommendation is proposed which is a real-value constrained Boltzmann machine recommendation algorithm based on trust-distrust relationship (TDA-RBM), establish restricted Boltzmann machine for each user, using normalized real value analysis and training process of RBM. Construct trust-distrust supervision mechanism, trust, distrust and the trust transfer are introduced, and the trust as weights for the TDA-RBM prediction score. Finally, we experimented on Epinions datasets and the comparison experiment proves that the TDA-RBM algorithm can effectively improve the accuracy of recommendation, which shows the effectiveness and excellency of the algorithm.
The energy congestion effects refer to the abnormal economic phenomenon that output decreases with the increase of energy input. In order to investigate energy congestion effects in manufacturing sector and improve its low-carbon operational capability. Energy congestion effects are decomposed into desirable energy congestion and undesirable energy congestion according to the heterogeneity of output. The RAM-DEA model is used to establish the desirable energy congestion and undesirable energy congestion models under natural disposability and managerial disposability respectively. An empirical study on 28 sub-industries of Chinese manufacturing sector is conducted. Empirical results show that:Undesirable energy congestion have occurred in the manufacturing sector and the degree of undesirable energy congestion is increasing since 2000. Especially in the year of 2014, the amount of 962.6 million tons of coal equivalent are wasted. Most of China's manufacturing industries have poor performance of energy efficiency and there are huge gaps among sub-industries. If energy inefficiency is decomposed into congestion inefficiency and pure technical inefficiency, the traditional manufacturing industries are mainly suffered from congestion inefficiency, and the advanced manufacturing industries are mainly suffered from pure technical inefficiency, while the traditional chemical industries and energy intensive industries are suffered from both congestion and pure technical inefficiency. Additionally, the occurrence of desirable energy congestion had been improved in terms of both frequency and quantity from 2000 to 2014, which indicates that more and more China's manufacturing industries are undergoing a low-carbon technology innovation. The research conclusions have important policy implications for energy-saving and emission reduction, industrial structure optimization, as well as risk warning mechanism establishment for the over-capacity in the manufacturing sector.
The medium-term power load forecasting is often disturbed by a variety of external factors (such as temperature, holidays and wind power) and uncertainties. Also, the factors affecting the medium-term power load forecasting are complex and changeable, and it is difficult to predict accurately. In the big data environment, how to obtain valuable information quickly in a variety of large number of influence factors has become the key to the power load forecasting problems. A method of density forecasting based on LASSO quantile regression was proposed in this paper. First, the important influence factors were selected from the various external factors affecting the power load forecasting, and the LASSO quantile regression model was established. Then, by using the triangular kernel function, LASSO quantile regression was combined with the method of kernel density estimation for the medium-term power load probability density forecasting. Taking the historical load and external influence factors (including temperature, holidays and wind power) of a sub-provincial city in eastern China as an example, the probability density prediction of medium-term power load was carried out. The average absolute error obtained was respectively 3.53% and 3.69% in the median and the mode, which was better than the results without considering the external factors and without variable selection. In order to further verify the superiority of the method, the method was compared with the nonlinear quantile regression (NLQR) and the quantile regression neural network based on triangle kernel (QRNNT) probability density forecasting methods. The results illustrate that this method can better solve the high-dimensional data problem in power load forecasting, and obtain more accurate results of power load forecasting.
The direct economical losses and the tolls of earthquakes are main impacts of earthquake insurance. This paper collected the loss data of earthquake during the years 1950-2015 in China and modeled the direct economical losses and the tolls of earthquake using Copula-mixed distribution model. First, the truncated Gumble distribution and generalized Pareto distribution are mixed to fit the direct economical losses, and the truncated negative binominal distribution and generalized Pareto distribution are mixed to fit the tolls of earthquake. Second, copulas are used to model the dependence between the direct economical losses and the tolls of earthquake. Finally, Monte Carlo simulation method is used to calculate VaR and ES. The models proposed in the paper may be applied to establish earthquake insurance system in China.
The improvement of water resources monitoring data quality is an important content of the national water resources monitoring capacity building project. Hence, according to the actual statistical situation of national water resources monitoring data, the method of wavelet transform modulus maxima was applied to the noise reduction of water monitoring data and its singular value identification, and then the singular value was removed so that a new time-series monitoring data sequence could be corrected. This data sequence was used as the training samples of the least squares support vector machine model optimized by article swarm optimization (PSO-LSSVM), and singular value would be corrected by the fitting function of PSO-LSSVM model. All of above methods were tested through an empirical case of water monitoring data. Results showed that the original information of water monitoring data could be kept as much as possible using the method of wavelet transform modulus maxima, because this method improved the separation of high frequency and low frequency information, so it could reduce noise effectively and observe the inherent changes in water monitoring data. Meanwhile, the singular values were excavated in water resources monitoring data based on the method of wavelet transform modulus maxima, and also its application effect was better than traditional statistical method. The sample fitting accuracy of PSO-LSSVM model was higher than LSSVM and curve fitting, so the singular value was reconstructed by PSO-LSSVM model, and these reconstructed data were consistent with the objective law of actual water demand.
The autocorrelation among measurement errors has been usually ignored in the traditional accelerated degradation modeling procedure. For this problem, a step-stress accelerated degradation model is proposed by simultaneously considering a first-order autoregressive (AR(1)) measurement error series for reliability analysis. The Wiener process is utilized to describe the performance degradation procedure, and an AR(1) model is adopted for modeling the measurement error term. In addition, the relation function between the drift parameter and the accelerated stress is also constructed. Meanwhile, a parameter in the accelerated relation function is randomized to characteristic the individual variation. Then, under the concept of the first hitting time, closed-forms of the probability density function and the distribution function are derived. Moreover, the maximum likelihood estimation method is used for estimating unknown parameters in the proposed model. Finally, a real application involving the GaAs laser is conducted to illustrate the validity and efficiency of the proposed model. Results show that compared with the reference methods, the proposed model shows a better fitting goodness and an enhanced accuracy, and so that it can provide a strong support for further maintenance decision making.
In order to deal with the problem of sudden leakage during pressure monitoring of long-distance pipelines and being difficult to give early warning and accurate leakage location, a pipeline leakage fault detection and location model is proposed by using ensemble modified independent component analysis algorithm (EMICA) and kernel ridge regression algorithm (KRR). Firstly, a fault detection model based on EMICA algorithm is established, which extracts and separates Gaussian and non-Gaussian signals from pressure data and construct related statistics to achieve fault signal separation and principal component selection. Then, based on the fault signals obtained by the EMICA model, a fault diagnosis model by using the KRR algorithm is further constructed, and the data is fitted to obtain the amplitude of the pressure change of the fault signal, and the leakage signal selection and the location of the leakage fault are achieved. Finally, numerical simulation experiments of the TE (Tennessee-Eastman) process were performed to verify the performance of the proposed algorithm. The simulation results show that the EMICA-KRR algorithm has better signal separation capability, and can accurately identify the leakage fault signal and accurately locate the failure position of the pipe segment, which overcomes the shortcomings of the traditional methods such as inefficiency and delay.
With sufficient consideration about temperature load, mechanical load, expansion cone hardness with hard coating and casing hardness, the computational equation was deduced to calculate yield collapse pressure between expansion cone and casing by fractal theory and contact mechanics. Digital analyses show that yield collapse pressure between expansion cone and casing adds with the increasing final temperature, fractal roughness, linear expansion coefficient, Brinell hardness of expansion cone with hard coating, intermediate principal stress coefficient and casing wall thickness. Yield collapse pressure between expansion cone and casing decreases with the increase in fractal dimension from 1. Yield collapse pressure between expansion cone and casing enhances with the augment in fractal dimension close to 2. Yield collapse pressure between expansion cone and casing reduces due to the increase in tensile-to-compressive strength ratio. Relative errors between calculable values for yield collapse pressure and experimental measurement ones lie within the range from -8.9253% to -0.9901%.