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.

As one of the financial industry, the insurance industry is now facing a vast market and significant growth opportunities. The insurance company will generate a lot transaction data each day, thus forming a huge database. Recommending insurance products for customers accurately and efficiently can help to improve the competitiveness of insurance company. Data mining technologies such as association rules have been applied to the recommendation of insurance products. However, large policyholders' data will be calculated when it being processed with associate rule algorithm. It not only requires higher cost of time and space, but also can lead to the final rules lack of accuracy and differentiation. In this paper, a recommendation model for insurance products based on consumer segmentation is constructed, which first divides consumer group into different classes and then processed with associate rule algorithm. The empirical results show that our proposed method not only makes the consumption of association rules analysis reduced, it has also got more effective product recommendation results.

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.

Grey systems theory, probability theory, fuzzy systems
theory and rough set theory are known as four uncertain systems
scientific methodologies. After thirty years of development, grey
systems theory has formed its own disciplinary framework, whose
content includes grey philosophy, grey mathematics, processing and
technology systems of grey information, as well as grey systems
engineering. However, as a developing area of scientific
activities, a lot of theoretical contents are still imperfect and
awaiting further studies. The purpose of this paper is to acquaint
the readers with the development history, the main content and the
major challenges of grey system theory. It is our hope that this
paper could play the role of attracting the attention of the
international academic community to the theory
so that the development of this theory could be further promoted.

With the development of the electronic commerce, the electronic word-of-mouth (eWOM) has become important reference information for consumer shopping. EWOM has attracted considerable interest from researchers in the past decade. In this paper, a research review is conducted and an integrated framework is proposed on the effect of eWOM. The effect of eWOM are influenced by its characteristics, communicators, and other factors. The characteristics of eWOM include the source, the volume and the valence. The communicators of eWOM refer to the sender, the receiver and the relationship between them. In addition, dispersion and consistency, persistence and observability, anonymity and deception, and community engagement are related factors for the effect of eWOM.

The generalized autoregressive conditional heteroskedasticity (GARCH) type models are used to investigate the volatility of Bangladesh stock market. The findings of the study demonstrate that the index volatility characteristics changes over time. The article shows that the data are divided into three sub-periods: pre crisis, crisis, and post crisis. Accordingly, the results of the findings indicate changes in the GARCH-type models parameter, risk premium and persistence of volatility in different periods. A significant “low-yield associated with high-risk” phenomenon is detected in the crisis period and the “leverage effect” occurs in each periods. The investors are irrational which is based on assumption of risk and return characteristics of assets. Consequently, the market is not as mature as developed market. It is found in the article that the threshold generalized autoregressive conditional heteroskedasticity (TGARCH) model is more accurate for the model accuracy. Additionally, statistic error measurements indicate that GARCH model is more efficient than others and it has also more forecasting ability.

A data-centric approach is proposed to facilitate the design and analysis of challenging complex systems and address the problems of currently existing model-based systems engineering (MBSE) methodologies. More specifically, based on three core steps of current MBSE methodologies, a high-level data meta-model, depicting the semantic relationships of high-level data concepts, is first presented to guide the data modeling for systems engineering (SE). Next, with respect to the six high-level data concepts,the data elements are collected as the modeling primitives to construct static and/or executable models, which can also act as a common and consistent data dictionary for SE. Then, the mapping associations amongst core data elements are established to associate the model elements in different steps and achieve the requirement traceability matrix. Finally, the feasibility of the proposed approach is demonstrated with an illustrative example.

In this era of a data-driven society, useful data (Big Data) is often unintentionally ignored due to lack of convenient tools and expensive software. For example, web log files can be used to identify explicit information of browsing patterns when users access web sites. Some hidden information, however, cannot be directly derived from the log files. We may need external resources to discover more knowledge from browsing patterns. The purpose of this study is to investigate the application of web usage mining based on web log files. The outcome of this study sets further directions of this investigation on what and how implicit information embedded in log files can be efficiently and effectively extracted. Further work involves combining the use of social media data to improve business decision quality.

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.

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.

Supply chain management is becoming the core competitiveness in enterprise competition, which is an important way to increase the
enterprise's profitability. With the development of the Internet of Things, supply chain management is gradually achieved by information
technologies. This paper studies an information-based real-time products management method in supply chain management system through
Java language, XML and other information technologies. Firstly, we introduce the supply chain management, then using XML document
and B/S structure to realize the electronic management for products during product circulation process. The experimental results
show that this system is stable and practicable, and could efficiently collect the product flow information which could provide
valuable information for the materials and manufacture enterprises.

This paper examines the relationship between stock market(KSE-100), money market(M2 and 180 days T-bill rate), and foreign exchange market(ER:PKR/USD) in Pakistan by using monthly data covering the period from 2000:M1 to 2015:M12. The study investigates long-run equilibrium relationship between these three financial markets by employing Johansen and Juselius^[1] cointegration tests. Long-run and short-run causality relationship between stock market and other macroeconomic variables is also established by employing vector error correction model(VECM) and pairwise granger causality tests. The results of multivariate cointegration test(trace test) indicate a one cointegrating vector, and the significant normalized cointegrating coefficients are evident of long run equilibrium relationship between all the selected variables. Negative and significant ECT(?1) for all variables during full sample period witness the presence of long-run causality connection among variables, while during the military regime and democratic regime, significant difference of long-run causal connections are identified across the regimes. Moreover, the results of granger causality test also indicate that there are significant variations in the causality relationship among variables across the regimes. Therefore, it is essential for forecasting, planning and policy making to consider the importance of political governance system while analyzing the historical cointegration among financial market and make the necessary adjustments accordingly.

The analytic hierarchy process (AHP) is used widely for analyzing decisions made in various real-world applications. Its basic idea is to construct a hierarchy of concepts encountered in a given decision problem and to choose the best alternative according to pairwise comparison matrices given by the decision maker. Under the assumption of fully rational economics, a reasonable decision should be consistent. It becomes an important issue on how to analyze and ensure the consistency of comparison matrices together with the judgments of the decision maker. The main objectives of the present paper are threefold. First, we review the basic idea and methods used to define the consistency and the transitivity of multiplicative reciprocal matrices, additive reciprocal matrices and comparison matrices with fuzzy interval and triangular fuzzy numbers. The existing controversy behind the applications of fuzzy set theory to the AHP in the literature is presented. Second, the consistency of the collective comparison matrices in group decision making based on AHP and fuzzy AHP is further analyzed. We point out that the weak consistency of preference relations with fuzzy numbers in fuzzy AHP and group decision making should be investigated comprehensively. Third, under the consideration of the vagueness in the process of evaluating the judgements, a new concept of fuzzy consistency of comparison matrices in the AHP is given.

In recent years, it has been a hot pot to explore the effectiveness of basic medical insurance. However, due to different sample characteristics, time series length, models and so on used by authors, there exists big difference among relevant papers. In this paper, we try to apply meta regression analysis (MRA, a prevalent statistic literature review method) to explore its influence on family expenditure, and 90 sample observations were extracted from 49 important literatures. We find: 1) All the index construction, sample characteristic, model selection, control factors will influence the conclusions of medical insurance effectiveness; 2) The implement of basic medical insurance will increase household consumption other than the new rural cooperative systems; 3) The implement of basic health insurance-has not really reduce family's medical expense. Thus, we induce that adverse selection exists in China's medical insurance.

This paper explores the macroeconomic determinants of non-performing loans (NPL) in 19 Asian countries (low to high income economies) using the Generalized Method of Moments estimation approach based on the economic data for the period between 1998 and 2015. The categorization of the economies is based on the average gross national income per capita as set by the World Bank. Specifically, the paper aims to evaluate if the determinants of NPL vary with the income levels of the countries. The results indicate that the NPL is strongly influenced by the inflation rate. The effect is, however, negative in the high-income and the middle-income countries and positive in the low-income countries. The GDP per capita has a dynamic negative relationship with the NPL in the high-income and the low-income countries. The remittance has a significant positive association in the high-income and a significant negative association in the low-income countries. Similarly, the unemployment rate has a positive effect on NPL in the middle-income and the low-income countries. With the rise in the official exchange rate, the NPL level increases in the low-income countries. The overall estimation results suggest that the NPL in Asian banking system depend on some key macroeconomic variables, such as unemployment rate, inflation rate, official exchange rate, remittance received and gross domestic product per capita, and these associations vary with the income level of the countries. Therefore, economic level of a country should be carefully considered while formulating credit policy to minimize credit risks in the banking system.

To cope with the increasing complexity and constantly changing capability requirements of system-of-systems, an architectural approach for capability mapping and gap analysis is proposed. Firstly, a more specific architecture framework is presented. Secondly, on the basis of the analysis of the semantic relationships between constituent architecture data elements, a capability mapping approach is proposed to manage the relations between capabilities and systems based on the mapping matrixes representing associations of core entities. Then, a four-step capability mapping process for the mapping from capabilities to systems is suggested. Moreover, the capability gap analysis on how to identify and fill the capability gaps is also studied. Finally, an example of specific Net-Centric Warfare mission of Precision Engagement is used to illustrate the feasibility of the approach.

Rapid expansion of big-box store in developing country caused typical archetypal change in market structure: Success to the Successful, because big-box stores armed with modernized infrastructure and management capability are absorbing the once customers of the traditional market like a black hole. Facing rapid change in market structure and surmounting pleas from traditional market merchants, government took an inevitable intervention with law regulating the big-box store's business and improving traditional market's competence building. Not so long, however, did government confront policy resistance from both sides: Still ongoing polarization of both side's sales. This study articulates behavior over time of market structure with causal loop diagrams of which causalities are extracted from literatures. This study provides significant contribution to policy makers and traditional markets' merchants in other developing countries like India and China, as well as Korea.

The aim of this paper is to measure the effect of sensational seeking, visual merchandising and collectivism on impulsive buying behavior. Valid sample size for this study was 300 comprising of all age groups. Mall intercept method which is a kind of convenience sampling was used for collecting the data. The data was collected by preselected enumerators. Scale used for this study had established reliabilities. After ascertaining the normality of data a typical multiple steps, procedure was adopted for this study. The conceptual framework tested through Structural equation modeling and was found to be relevant in understanding the impact of predictor variables on impulsive buying behavior. A strong and positive relationship was found between sensational seeking, and no relationships were found between, collectivism and impulsive buying, and visual merchandising and impulsive buying. One of the contributions of this study is that it has explored the relationships of collectivism, and sensational seeking with impulsive buying which have not been explored that extensively.

The game among the corporate controlling shareholder, the shareholder in power balance and the manager can lead to severe agency
problems. This paper regards the shareholder in power balance as another principal and applies the latest results about double-principal
agent theory in the research of manager tunneling, supervision cost and corporate performance, trying to solve the inconsistency of the above
corporate governance issue researched by domestic and foreign scholars. The main conclusions are as the followings. The correlation
among them depends on which one has a dominant position, the free rider effect of supervision or the positive externality effect
on cash flow right. Therefore, the key to excite the positive effect of the corporate governance mechanism such as the check-and-balance
of stock ownership is the degree of cooperation between shareholders.

The paper focuses on modeling longitudinal customer behavior and develops a dynamic programming (DP) to show how customer transaction database may be used to guide marketing decisions such as pricing and the design of customer reward programs. Dynamic programming is not as a tool to marketing decisions making in this research but rather as a description of consumer behavior. The results show that the method provides a means for evaluating the effectiveness of marketing strategy, for example, customer reward programs. Moreover, the findings from the model estimation indicate that reward program can actually increase the customer's purchase level and stimulate the repeat purchase behavior.

Factor analysis is widely used in psychology, sociology and economics, as an analytically tractable method of reducing the
dimensionality of the data in multivariate statistical analysis. The classical factor analysis model in which the unobserved factor
scores and errors are assumed to follow the normal distributions is often criticized because of its lack of robustness.
This paper introduces a new robust factor analysis model for dichotomous data by using robust distributions such as multivariate
$t$-distribution. After comparing the fitting results of the normal factor analysis model and the robust factor analysis model
for dichotomous data, it can been seen that the robust factor analysis model can get more accurate analysis results in some cases,
which indicates this model expands the application range and practical value of the factor analysis model.

Major societal problems affect the social stability. It is necessary to understand the public opinion toward those issues to avoid social conflicts. Nowadays the social media become the major platform to track what the public is concerned about and which may be of the societal risk. However, it is very tough to capture the public attention in short time due to huge flow of user-generated contents. In this paper, we approach this problem by expanding the method of generating storyline with the result displayed by a multi-view graph. One real-world example is illustrated and evaluation is given to show the effectiveness of the proposed method.

This article establishes a useful analytical framework
for complete carbon emission of the industries in China and then
makes comparison on carbon emission among these industries based
on the latest data derived from China's Input-Output Table and
Energy Statistics in 2007. It is found that some industries are
``invisible high-carbon" sectors by the definitions of
directly-embodied coefficient and perfectly-embodied coefficient
and that others have made contributions to carbon leakage by
measure of importing and exporting carbon emission volume.
Finally, this article provides suggestions to industrial
strategies, trade policies and the comprehensive economic
management policy in order
to effectively achieve energy conservation and emission reduction.

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.

A particle filter based method to price American option under partial observation framework is introduced. Assuming the underlying price process is driven by unobservable latent factors, the pricing methodology should contain inference on latent factors in addition to the original least-squares Monte Carlo approach of
Longstaff and Schwartz. Sequential Monte Carlo is a widely applied technique to provide such inference. Applications on stochastic volatility models has been introduced by Rambharat, who assume that volatility is a latent stochastic process, and capture information about it using particle filter based ``summary
vectors''. This paper investigates this particle filter based pricing methodology, with an extension to a stochastic volatility jump model, stochastic volatility correlated jump model (SVCJ), and auxiliary particle filter (APF) introduced first by Pitt and Shephard. In the APF algorithm of SVCJ model, it also provides a modification version to enhance the performance in the resampling step. A detailed implementation and numerical examples of the algorithm are provided. The algorithm is also applied to empirical data.

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 article makes a comparative research on the effect
of leader supporting, interpersonal relationship, knowledge
sharing mechanisms and organizational incentive on the formal and
informal knowledge sharing in the project context. Through the
establishment of structural equation model, some conclusions have
been reached as follows: Firstly, leader supporting positively
influences both formal and informal knowledge sharing, whereas the
interpersonal relationship partly mediates between leader
supporting and informal knowledge sharing; Moreover, the
interpersonal relationship has a significant effect on both formal
and informal knowledge sharing; Sharing mechanisms can
significantly promote formal knowledge sharing, but there is no
direct impact on informal side. Instead, the interpersonal
relationship plays a fully mediating role between sharing
mechanism and informal sharing; Finally, organizational incentive
has a significant promotion on formal
sharing, but negatively influences the informal sharing.

This paper studies a dual-channel supply chain in which a manufacturer sells products to a retailer as well as to customers who are sensitive to both channel price and the retail service. Three game models (Manufacturer Stackelberg, Retailer Stackelberg and Vertical Nash) are built according to members' different bargaining power in a dual channels system. The authors show that customers can receive lower channel price and higher retail service level when channel members have equal bargaining power, however, when the retailer occupies the market leadership, consumers always receive the least welfare because of the higher channel price and lower retail service. Interestingly, the retailer can take advantage of market leadership to make more profits, while the manufacturer is more willing to give up its power and act as a Stackelberg follower. Furthermore, Manufacturer Stackelberg and Vertical Nash is a strictly dominated strategy for the retailer and the manufacturer respectively.

Evolutionary computations are kinds of random searching algorithms derived from natural selection and biological genetic evolution behavior. Evaluating the performance of an algorithm is a fundamental task to track and find the way to improve the algorithm, while visualization technique may play an important act during the process. Based on current existing algorithm performance evaluation criteria and methods, a python-based programming tracking strategy, which employs 2-D graphical library of python matplotlib for online algorithm performance evaluation, is proposed in this paper. Tracking and displaying the performance of genetic algorithm (GA) and the particle swarm optimization (PSO) optimizing two typical numerical benchmark problems are employed for verification and validation.
Results show that the tracking strategy based on Python language for online performance evaluation of evolutionary algorithms is valid, and can be used to help researchers on algorithms' performance evaluation and finding ways to improve it.

To strengthen operational process analysis and normalize information requirements description in systemic operations based on information systems, a new operational architecture modeling method is proposed from the perspective of information flow analysis. An operational architecture modeling framework based on information flow analysis is established by referring to American department of defense architecture framework (DoDAF V2.0). Concepts of entities, relationships, attributes and mapping rules are defined. Operational activity model, operational nod model and information alternation model are constructed. Finally, aerial assault operation is taken as an example to demonstrate the modeling process. Simulation results show that operational process analysis is more refined and information requirement descriptions are more visible, normal and clear, which validate the feasibility and validity of the method and models.

Taking the special nonlinear characteristics of the
domestic and international gold price into account, this paper
systematically analyzed its nonlinearity by the methods of BDS
test, R/S analysis and improved largest Lyapunov exponent. We find
three main results: (1) ARMA-GARCH model could adequately explain
the linear and nonlinear dependence of gold price series; (2)
long-memory does not exist anymore in price series explained by
ARMA-GARCH model; (3) chaos phenomenon which is sensitive to the
initial value does not exist either in the residuals of regression
model. Therefore, we believe that the nonlinearity of gold price
is mainly characterized in conditional heteroscedasticity rather
than chaos.

This paper researches the pricing strategy and government intervention
mechanism for green supply chain in monopoly market, while considering strategic
customer behavior. According to optimization theory, it establishes the target
functions of retailer and strategic customers and investigates the interactions
between retailer and strategic customers in accordance with Stackelberg's game
theory, so as to confirm the optimal discount level for green products. In addition,
it discusses the regulatory effect of government intervention, including the guiding
price and fiscal subsides, on the sales of green products. The research shows that
the retailer can make the profit maximization by adjusting the discount level;
the governments can regulate the sales of products by implementing the guiding
price and fiscal subsides to the retailer.

The paper did some work to analyze the longevity risk of impaired lives with general chronic diseases. The work was based on the age-specific, time-specific and disease-specific data set which is from the underwriting data base, the Chinese healthy yearbook and the medical institution. After some analysis on the final data, the results showed that there was an obvious upward trend in morbidity and downward trend mortality. All the results can be used in the pricing process of insurance products for impaired lives. The paper also showed the pricing results of innovative life insurance products for impaired lives with diabetes. The conclusion means a good market chance for any insurance companies.

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.

The rise of Big Data brings the financial innovation opportunities as well as challenges. This paper reviews different fields of big-data-based financial innovations as well as the scientific discoveries and theoretical breakthroughs of risk analysis with respect to these financial innovations. Based on the current research status, several key problems are put forward and their relative solutions are discussed. The three mean aspects are listed as the pricing and risk measuring for data-driven financial innovation products or services; the changes that data-driven financial innovation would bring to finance industry, which involve operation, resources allocation and ecosystem; and questions and solutions of systemic risk management based on Big Data analytics. Finally, predictions towards the hotspots frontier and developing trends for further data-driven financial innovation are proposed.

Economic development has contributed to the rapid expansion of China's steel industry during the past two decades, which has resulted in numerous problems including increased energy consumption and excessive environmental pollution. This study examines changes in crude steel production, steel scrap consumption, energy consumption, CO₂ emissions and steel stocks per capita from 2000 to 2014. Scenario analysis based on QGT equation is provided to accurately assess China's steel demand. Under three different scenarios, the peak of steel production and the variation trend of energy consumption, CO₂ emissions, steel stocks per capita and steel scrap are analyzed from 2010 to 2030. Based on Chinese situation, the most reasonable variation trend of China's steel production is proposed, which will increase from 626.7 Mt in 2010 to approximately 914 Mt in 2020, then gradually decrease to about 870 Mt in 2030. Steel stocks per capita will increase from 3.8 t/cap in 2010 to 8.09 t/cap in 2020 (the inferior limit of completing industrialization), then reach 11.46 t/cap in 2030 and stabilize. The peaks of energy consumption and CO₂ emissions in steel industry are expected to reach 505.37 Mtce and 1444.1 Mt in 2020, respectively. The scrap ratio is expected to reach 0.36 by 2030, when steel scrap resources will be relatively sufficient. This paper can provide corresponding theoretical basis for the government to make decision-making of macro-control.

In order to improve the forecasting accuracy, a hybrid error-correction approach by integrating support vector machine (SVM), empirical mode decomposition (EMD) and the improved cuckoo search algorithm (ICS) was introduced in this study. By using two indexes as examples, the empirical study shows our proposed approach by means of synchronously predict the prediction error which used to correct the preliminary predicted values has better prediction precision than other five competing approaches, furthermore, the improved strategies for cuckoo search algorithm has better performance than other three evolutionary algorithms
in parameters selection.

For an integrated supply chain with an online direct channel and a traditional retail channel competing with each other, solutions are be identified as to the two channels' ordering policies, product pricing strategies, and optimal product output, when product costs in the two channels are disrupted in different ways. Findings are as follows: 1) For the integrated supply chain, when unanticipated events lead to product cost increase, market size will shrink, and the system profit is harmed. In contrast, when unanticipated events lead to reduced product cost, market size will expand, and system profit increases; 2) Production strategies applicable to normal situations have certain robustness, and should be maintained when product cost disruption caused by unanticipated events is relatively small; 3) When product cost disruption caused by unanticipated events is relatively large, product sales price should be first adjusted, and aligned with the way that product cost is disrupted. Meanwhile, order quantity and product output should also be properly adjusted. That is, order quantity and output need to be reduced when product cost increases; order quantity and output need to be increased when product cost is reduced. In the end, this paper employs numerical examples to testify the findings. Research conclusions help to further enrich and extend the theoretic basis of “supply chain disruption management”, and are helpful for researchers' further study.