In early 2013, Ministry of Railways (MOR) is dismantled into administrative and business arms. To tackle with economic performance setbacks and huge financial debt, Chinese Railway Group Corporation (CRGC), who is in charge of business operations, determined to make product innovation for express freight
 so as to provide the market with value-added logistical services and increase its revenue. This paper aims to analyze the necessity and feasibility of developing innovative express product for CRGC. Under the condition of heavy accumulated debt pressure and continuous economic losses, it is imperative for Chinese railway to increase revenue through the introduction of novel express freight services. Based on complete transportation product theory, this paper summarizes the temporal-and-spatial characteristics of modern logistics, analyzes the prospect of supplying express freight services, and applies Express Freight Network Design Model to identify key factors for express freight product supply. In order to improve service quality of express freight product, it is necessary to optimize transportation organization, put forward market-oriented customized products, and improve informationization standard.

Benevolent and aggressive cross-efficiency evaluation methods are the improvement of the cross-efficiency evaluation method. They merely maximize or minimize the efficiency of the composite unit constructed by evaluated decision-making units while maintaining the optimal efficiency of the decision-making unit under evaluation. The two methods completely ignore the self-evaluation efficiency of evaluated unit and the good relationship among decision-making units.
 To solve the above drawbacks, the authors consider the efficiency score of the decision-making unit as an interval number and propose a more reasonable   interval number. On the basis of the interval efficiency,  the authors  provide the benevolent and aggressive DEA cross-efficiency evaluation models based on the good relationship among all decision-making units. Finally, a numerical example is provided to illustrate the proposed method.

This paper presents the dynamic fuzzy clustering method to solve the multi-producers to multi-customers large-scale distribution problem. The proposed method includes three phases: Static clustering, order processing, and dynamic clustering. Based on the distances among customers, $k$-means method is used to generate the static clusters. The service priorities of each producer serving the static customer groups are ranked according to the distance performance. In the case of fluctuant customer orders, order processing can divide customer orders into several consecutive periods. After the above two phases, the fuzzy clustering technique is applied to further conduct dynamic clustering based on the customer order attributes. Similarly, the service priorities of generated   dynamic customer groups will be ranked according to the time attributes of orders. Finally, by the real case, the authors obtain the conclusion that using the proposed method, the total cost of the producer is reduced by about 35$\%$, and the vehicle loading rates are almost above 95$\%$.

This paper  studies the closed-loop supply chain decision problem in which the original manufacturer allows the third-party remanufacturer to remanufacture under patent protection. In the two cycle mode, the authors discuss the pricing decision problem of the original manufacturer, the retailer, and the third-party remanufacturer in centralized and decentralized decision-making cases.  The authors obtain the optimal sale price and wholesale price of new products and remanufactured products in two cases, and the optimal recycling rate of used products in centralized decision-making case. Finally,  the authors carry out
the corresponding numerical simulation of the pricing decision.

This paper  studies the control variate method for pricing interest rate derivatives driven by the LIBOR market model. Several control variates are constructed based on distinctive approximations for the LIBOR market model. Numerical results show the great efficiency of our methods. The idea in this paper can
 also be extended to price  other interest  rate derivatives under the LIBOR market model, such as $Swaptions$, $Caps$, some path dependent interest rate derivatives, and so forth.

In the situation of indirect network effect, this paper mainly studies the optimal extended warranty price. First, the characteristics of the extended warranty price are discussed. Next, the optimal extended warranty price model is advanced. From the model, the authors can draw that the influence factors mainly include the producer  and consumers' risk preferences, the incompatible degree of the producer's maintenance technology, the extended warranty period, the producer  and consumers' per-occasion maintenance cost. Finally, the authors carry the simulation method to indicate their influence relation. The results have theoretical significance for the automobile producer to ensure the optimal extended warranty price.

This paper considers several parallel machine scheduling problems with controllable processing times, in which the goal is to minimize the makespan. Preemption is allowed. The processing times of the jobs can be compressed by some extra resources. Three resource use models are considered. If the jobs are released at the same time, the problems under all the three models can be solved in a polynomial time. The authors give the polynomial algorithm. When the jobs are not released at the same time, if all the resources are given at time zero, or the remaining resources in the front stages can be used to the next stages, the offline problems can be solved in a polynomial time, but the online problems have no optimal algorithm. If the jobs have different release dates, and the remaining resources in the front stages can not be used in the next stages, both the offline and online problems can be solved in a polynomial time.

With China's economic slowdown and port low-level repeated construction,  China ports are facing the problem of overheated investment. In this paper, using the port supply-demand balance and comprehensive cost as the two main optimization goals, the ecological carrying capacity and the port profit-loss as the constraints, the port multi-period investment decision optimization model is built up by means of dynamic programming. Sustainable development ability about port supply-demand matching and ecological carrying capacity are considered into the decision-making model, which is used to solve the existing literature disadvantage of port investment planning decision based on operation efficiency and benefit. It helps to prevent the port resources waste and ecological environment destruction, and has the reality instruction significance.

Heavy ordered weighted averaging (OWA) operator is important for characterizing the decision maker's attitudinal character in multi-attribute decision making (MADM) problem with part or total ignorance. This paper  develops a new method based on heavy OWA operator  to solve the MADM problem in which the
attributes are characterized by some trapezoidal intuitionistic fuzzy numbers (TrIFNs). TrIFN, as a special kind of intuitionistic fuzzy set defined on the real numbers, is useful for characterizing the ill-known quantity in reality. Firstly, the operation laws and the cut sets concept for TrIFNs are introduced. Then  the authors  define the membership and non-membership average indexes. A new ranking method is developed on the basis of the two indexes. In the proposed decision model, the multi-attribute TrIFN values of the candidates are aggregated by the Heavy OWA operator, and ranked by their membership and non-membership average indexes. Lastly, the authors illustrate the proposed method by a numerical example which implies the practicality and effectiveness of the method.

This paper quantitatively investigates the effect of transportation system on trade flows of four major port cities in China. Due to the significant country-pair heterogeneities in both intercept and slope terms, this paper introduced a random-coefficients model for parameters estimation. The empirical findings imply that the impact of the explanatory variables included in the gravity equation could be inaccurately estimated if the pair-wise heterogeneity biases in both intercept and slope terms are not accounted for during the econometric estimation of the model. In particular, in the presence of this heterogeneity, parameter estimates tend to be underestimated for country-pairs with higher trade volume and overestimated for those country-pairs with lower trade volume. In addition, the empirical results suggest that the improvement transportation system in port cities of China offers greater scope for its trade competitiveness.

From the perspective of the mediation effect, this paper investigates whether institutional investors adjust their portfolios according to the listed companies earnings surprise. We find that the portfolio adjustments by institutional investors exert the mediation effect on the relationship between earnings surprise and stock price volatility. Institutional investors actively manage their portfolios in the rising market, which induces the stock price volatility; while they less adjust their portfolio in the falling market, the volatility declines. This paper helps understand the role of institutional investors in the fluctuation of stock prices, and provides a new basis for decision making of regulatory administration.

This paper investigates the detection for structural breaks in GARCH models based on Bayesian method. The authors firstly introduce the background and significance of this problem, then present the current situation and recent developments in this field. Because the rates of return have heavy tails, the authors present GARCH models. In this paper, the authors innovatively suppose that the error term follows standard student t distribution with degree of freedom υ instead of standard normal distribution. The authors give the specific description of estimation using Bayesian method, including a single structural break situation and multiple structural breaks situation when the number of breaks is unknown. In an application, the authors empirically research the volatility of stock market in China. The authors estimate GARCH models with structural breaks for the Shanghai A-share index and Shenzhen A-share index rate of return over the period of January 4, 2000-September 30, 2011. The authors explain the breaks together with the nearby big political and economic events. Empirical results show that the detecting method used in this paper is feasible.

This paper makes a theoretical and empirical study on the impact of economic growth and financial development on the environment in China. Through the establishment of econometric models, some conclusions have been found as follows: Firstly, there's Environmental Kuznets Curve in China in the long and short term; Secondly, China's financial interrelations ratio and financial efficiency can alleviate environmental pollution, and in the long term financial interrelations ratio has a stronger effect, instead, in the short term financial efficiency has a stronger effect; Moreover, in the long term financial interrelations ratio and financial efficiency have a positive moderating effect that can weaken the impact of economic growth on the environment, whereas financial interrelations ratio's moderating effect is stronger; Finally, this article makes conclusion and inspiration for the improvement of China's environmental quality.

The real-time pricing plays an important role in demand-side management for smart grid. In this paper, we study real-time pricing strategy of electricity retailers by means of game theory in smart grid. The retailers are in the game situation where there is one leader with multi-followers. We propose a real-time electricity demand function and analyze the interactions between the retailers, then obtain its equilibrium solution. The analysis and simulation results of the equilibrium solution show the e ectiveness of the proposed method.

A time-division duplex (TDD) two-way channel exploits reciprocity to estimate the forward channel gain from the reverse link. Many previous works explore outage probabilities in the TDD system, based on the reciprocity property. However, a frequency-division duplex (FDD) system has no reciprocity property. In this letter, we investigate the impact of CSI estimation error on the performance of non-orthogonal and orthogonal analog network coding protocols in an FDD two-way system, where channel gains are independent of each other. Considering imperfect CSI, the closed-form expressions of outage probabilities by two protocols are derived in the high signal-to-noise ratio (SNR) regime, respectively. It is shown that the derived outage probabilities match results of Monte Carlo simulations in di erent communication scenarios. It is interesting that ANC in the FDD two-way channel is proved to outperform that in the TDD channel by the computer simulation.

In order to overcome the problem of low convergence precision and easily relapsing into local extremum in fruit fly optimization algorithm (FOA), this paper adds the idea of differential evolution to fruit fly optimization algorithm so as to optimizing and a algorithm of fruit fly optimization based on differential evolution is proposed (FOADE). Adding the operating of mutation, crossover and selection of differential evolution to FOA after each iteration, which can jump out local extremum and continue to optimize. Compared to FOA, the experimental results show that FOADE has the advantages of better global searching ability, faster convergence and more precise convergence.

This paper studies a single server M/G/1 stochastic clearing queue operating in a 3-phase environment, where the time length of the rst and third phase are assumed to follow exponential distributions, and the time length of the second phase is a constant value. At the completion of phase 1, the system moves to phase 2, and after a xed time length, the system turns to phase 3. At the end of phase 3, all present customers in the system are forced to leave the system, then the system moves to phase 1 and restarts a new service cycle. Using the supplementary variable technique, we obtain the distribution for the stationary queue at an arbitrary epoch. We also derive the sojourn time distribution and the length of the server's working time in a cycle.

The law of start-up,acceleration profile and leave-out for queuing vehicles at signal intersections is a fundamental question for the queuing theory,traffic simulation and etc in traffic engineering.This paper investigates the vehicular flow at two protected left-turns by video cameras installed at signal intersections,and checks the accuracy of speed processing software.Based on the statistical analysis and the method of curve fitting,the research team establishes the model of start-up and speed-time profile for the queuing vehicles in the protected left-turns at signal intersections.Then the leave-out time of vehicles is calculated based on the established formulas in these models,which is consistent with the observed leave-out time of vehicles by the video.The developed models proved to be accurate.The paper concludes that the queuing vehicles follow by a linear start after the green light,and the speed-time profile indicates an "S" shape from vehicles starting to reach the stable saturation flow.The speed and the queue sequence of first vehicle of saturation flow are different at different intersections,but the law of start-up of vehicles is the same:The same reaction time for the first vehicle and the same linear start-time interval for the adjacent vehicle in the queue.The relationship of leave-out time and the queue sequence of vehicle is nonlinear before the saturation flow.According to the models developed in this paper,the queue sequence of first vehicle of saturation flow and the law of leave-out time of vehicles can be calculated accurately,which is the fundamental for the queuing theory and the traffic simulation.

The standard semi-supervised support vector machine(S³VM) is an unconstrained optimization problem of non-convex and non-smooth,so many smooth methods are applied for smoothing S³VM.In this paper,a new smooth semi-supervised support vector machine(SS³VM) model,which is based on the biquadratic spline function,is proposed.And,a hybrid Genetic Algorithm(GA)/SS³VM approach is presented to optimize the parameters of the model.The numerical experiments are performed to test the efficiency of the model.Experimental results show that generally our optimal SS³VM model outperforms other optimal SS³VM models mentioned in this paper.

Considering the fact that China is the world factory,in which the trade of intermediate goods has a relatively high share and especially that the trade of intermediate goods with ASEAN is even higher,it is not suitable to use GDP as the economic mass proxies in the gravity model to estimate trade ows traditionally.This paper,by using the data between China and 10 member states of ASEAN along with other 12 main trading partners of China from 1999 to 2013,constructs China's bilateral export equation based on the gravity model using new economic mass proxies according to Baldwin and Taglioni,and then calculates the trade potential index of China's export to the member states of ASEAN by using this equation.The empirical results show that,China's export trade equation based on new economic mass proxy has stronger explanatory power compared to the standard gravity model by using GDP as economic mass proxy.Therefore,the calculating results of trade potential of China's export to ASEAN are more convincing.

Based on association rules,this article proposed a method for intelligent recommendation of power supply mode,which helps decision-makers in the selection of many schemes.Firstly,a history database which includes the forecasting models and correlative factors was first built and association rule mining was conducted;then combined with the correlative factors in the designated area,the criteria matching in the rules mined were carried out with CBR technique;finally automatic recommendation of the power supply modes was achieved under the given conditions.By application of an example,it is demonstrated that the proposed method can not only automatically analyze the applicability of power supply modes and the intrinsic relationship between correlative factors but also provide,to some extent,theoretical basis for selection of power supply modes and practical utility for urban distribution network planning.

University-industry collaboration networks consist of heterogeneous subjects;in fact,the interactions among these subjects lead to the emergence of the complex characteristics of university industry collaboration networks and even to the emergence of the networks themselves.Thus,understanding the behavior that generates university-industry collaboration networks is essential to their construction and optimization.To more fully comprehend this generating behavior,we considered a university-industry collaboration network in the ocean energy industry in China as a case study.Employing the motif analysis method,we analyze the network's generating behavior from four perspectives:motif characteristics,motif classification,motif combination and motif clustering.Furthermore,we explore agents' modes of adaptive collaboration-and the causes of such collaborations-and summarize the characteristics and modes of networks' generating behavior.The results indicate that universities and industries tend to form highly stable and close cooperative relationships.The constraints of the network's generating behavior-which influences the subjects' interactions through relational and structural embeddedness-include asymmetric information,resource and cost constraints,risk aversion and geographical constraints.Our research presents the benefits of using the motif analysis method to study the network's generating behavior and its implications in constructing and optimizing a university-industry collaboration network.

With the transformation of economic growth modes,green growth is becoming the main source of global economic growth for the future.Enterprises are the main carriers of the economic growth in a country or a region,and enterprises' green growth efficiency determines the regional sustainable growth ability.On this basis,this paper investigates the problem of enterprises' green growth efficiency in China and identifies the input and output indexes of the green growth using the DEMA-TEL method.Then,through the super efficiency DEA model,this paper measures the enterprise green growth efficiency of 31 regions in China in the years 2003,2007 and 2012.In accordance with the four parts,including the East,the Central,the West and the Northeast,this paper completes a spatial and temporal variation analysis.The results show that there is an obvious regional difference in enterprise green growth efficiency in China during the above 3 years.Ranking the average efficiency from high to low,the order is as follows:the West,Central region,the Northeast and the East.Enterprise green growth efficiency shows a rising trend in the Northeast,and other regions show a downward trend.The enterprise green growth efficiency in the East is higher than the national average,and the efficiency in the other regions is less than the national average.

The spatial weights matrix is usually specified to be time invariant.However,when it are constructed with economic/socioeconomic distance,trade/demographic/climatic characteristics,these characteristics might be changing over time,and then the spatial weights matrix substantially varies over time.This paper focuses on power of Moran's I test for spatial dependence in panel data models with where spatial weights matrices can be time varying(TV-Moran).Compared with Moran's I test with time invariant spatial weights matrices(TI-Moran),the empirical power of TV-Moran test for spatial dependence are evaluated.Our extensive Monte Carlo simulation results indicate that Moran's I test with misspecified time invariant spatial weights matrices is questionable;Instead,TV-Moran test has shown superiority in higher power,especially for cases with negative spatial correlation parameters and the large time dimension.

This paper discusses linear quadratic Nash game of stochastic singular time-delay systems governed by Itô's differential equation.Sufficient condition for the existence of Nash strategies is given by means of linear matrix inequality for the first time.Moreover,in order to demonstrate the usefulness of the proposed theory,stochastic H₂/H_∞ control with multiple decision makers is discussed as an immediate application.

Domino effect is a fairly common phenomenon in process industry accidents, which makes many process industry accidents serious and the consequent losses enhanced. Domino effect of the major accidents in chemical cluster is emphasized. Many researchers have studied domino effect in chemical clusters from different perspectives. In the review, we summarize the research from three aspects:The statistical analysis of domino accidents in chemical process industry, the evaluation of domino accidents and the prevention of domino accidents in chemical clusters by game theory. From the analysis, we can find the characteristic of domino accidents such as the time and the location, the origin and causes of domino accidents. The methods of assessing domino effects such as quantitative risk assessment (QRA), Bayesian networks (BN) and Monte Carlo simulation (MCS) are analyzed.The prevention of domino accidents in chemical clusters using game theory is seldom, and there is still much space for improvement in enterprises' efforts to prevent risk of domino accidents.