With the deepening of economic globalization and the increasingly rapid development of Internet technology, business model has become a hot research focus and an exciting frontier. As the foundation of business success and the core competitiveness, the business model in service industry has aroused wide concern and been faced with enormous challenges. This paper aims at investigating the research progress about business model in service industry by using the methods of systems engineering. To start with, this paper analyzes the relevant literature with bibliometrics based on the CiteSpace software and the Web of Science database. In addition, due to the service industry involving extensive domains, we subdivide service industry into five sub-areas, and review the relevant literature and research comprehensively. In order to drawing more extensive attention about this area, this article presents and studies the development and evolution process of business model focusing on service industry, and proposes some suggestions about the research directions in the future.
There are a large number of literatures which have tested the relationships between economic growth and financial integration, but the middle channels have been neglected greatly. The expanded Nelson-Phelps model shows that the technological spillover effect of financial integration depends on the economic development. Based on 1975-2010 years panel data of 80 countries, the relationships between financial integration and TFP have been tested. The results show that financial integration of the total factor productivity in developed countries is uncertain, but for emerging and developing countries have significant role in promoting TFP, and mainly through FDI and debt capital. These conclusions for different countries financial integration policy arrangements have important policy implications.
Based on the daily return data of Chinese actively managed open-ended funds during the period 2002-2013, this paper adopts the false discovery rate (FDR) to examine the impact of luck on fund performance from the dimensions of style timing, investment objectives, fund characteristics, and performance persistence. The main conclusions include: The luck has an asymmetric effect on fund performance, namely, the effect of luck on good funds is greater than that on bad funds; Few good funds exhibit genuine stock-picking skill after the adjustment of luck, while most funds can not obtain abnormal returns with management fee, trade cost, and other expenses being deducted; Style timing has a significant effect on true performance, which means that investors should consider fund timing behavior when they use past performance to choose funds; According to the differentiated effects of investment objectives and fund characteristics on true performance, investors can efficiently choose the truly skilled star funds; Fund performance controlled by the FDR is not persistent.
Based on bounded rationality, adaptive expectations and disequilibrium market, the dynamic model of game among developers, local government, financial institutions and buyers in regional real estate market is built. Using game theory, dynamical systems theory and numerical simulation and DFC method, the authors study the dynamic evolution of the game. The results show that the asymptotically stable Nash equilibrium of the game can be reached by dynamic game among 4 main bodies of the market with bounded rationality on condition that regional real estate market supply and demand are unbalanced. The rising of rigid demand rate of the buyers, land remediation cost rate, urbanization rate, degree of financial dependence on the land and housing rental rate will all promote the equilibrium value of the supply, demand and prices in the real estate industry. And the rising of the property maintain tax rate, secondary transaction tax rate and real estate investment opportunity cost rate will all make the real estate industry supply, demand and prices downturn. However, the rising of comprehensive tax rate that the real estate developers burden will promote the equilibrium value of demand and prices. The authors suggest that the Central Government's real estate market regulation policies making should focus on the urbanization, land financial, the property maintain tax, secondary transaction tax and fee, investment opportunity cost and indemnificatory housing construction, rather than directly to focus on supply, demand and developers' comprehensive tax burden rate. The Central Government should adjust expectations of the main bodies of the market, and take advantage of the market self-regulation to achieve the healthy sustainable development of real estate market.
Uneven distribution of population, economy, environment and other factors lead to high disparities in real estate market supply and demand and further cause spatial agglomeration and heterogeneity of commercial housing prices. This paper took an empirical analysis on this phenomenon with statistical data of 287 cities between the year of 2002 and 2012. The result shows that real estate prices among cities are significantly spatial-autocorrelated in China, which is more and more obvious over time. And this paper finds that, spatial econometric model is more explanatory in analysis of these phenomena. What's more, the result reveals income disparities, imbalance between supply and demand, and population migration might be the primary factors leading to heterogeneity in real estate prices.
Under fierce market competition, in order to attract more new and old customers, a special promotion strategy of off-price merchandise is adopted by many supermarkets and marketplaces. Considering that off-price merchandise is allowed to be stockout and the waiting will of customers is related to selling price and waiting time respectively during out of stock, two kinds of different backlogging rate have been presented, and an economic ordering quantity model of off-price merchandise with demand related and different waiting will of customers has been generated. Considering the price of the off-price merchandise as exogenous variable and decision variable, the existent condition of the unique optimal solution and correlative management inspirations are discussed. The effect of key parameters on pricing, ordering policy and average profit of distributors is analyzed by numerical simulation. It shows that instock time has a lower limit and stockout time has a upper limit when the price of off-price merchandise satisfies a certain condition; the average total profit is a decreasing function of the proportion of low-income customers when the difference between selling price and purchase price of off-price merchandise is greater than a critical value; the price of off-price merchandise is a decreasing function of the proportion of high-income customers when demand related factor is greater than a critical value, but there exits a lower limit; the price of off-price merchandise can be close to even less than its purchase price when the difference between selling price and purchase price of the related merchandise is greater than a critical value; the price of off-price merchandise should be lower and the time of instock should be shortened and the time of stockout should be extended when the proportion of low-income customers and the deteriorating rate are larger; the price of off-price merchandise should be lower and the time of instock should be extended and the time of stockout should be shortened when the demand related factor, difference between selling price and purchase price of the related merchandise and the expiration date of the off-price merchandise are larger.
Foreign direct investment (FDI) is one of the important factors that affect China's economic development. Therefore, the prediction of which is the basic of its development and decision-making. Based on elaborating the significant role in the growth of China's economy and the current situation of utilizing foreign investment, with the data of 2000-2013, the article attempts to build grey-Markov model (GMM) and time series model to forecast the trend of China's utilization of foreign direct investment (FDI), and then compares the two different precision to get a better predicting model. The research results suggest that: traditional grey model needs to be optimized, although it is qualified; based on the grey model, to build a Markov forecasting model can help correct the result, improve grey relational degree and narrow the gap with real value; to build a first-order autoregressive time series model (AR(1)) forecasts the data; by comparing the accuracy of grey-Markov model (GMM) and that of time series model, the prediction accuracy of grey-Markov model (GMM) is higher, and its fitting effect is better. In order to further strengthen the credibility of the results, the paper selects the data of Beijing and Chongqing from 1990 to 2013, establishes the grey-Markov model (GMM) and time series prediction model and finds that the fitting effect of grey-Markov model (GMM) is superior to the time series prediction model. In short, for the prediction result of Chinese foreign capital utilization level, grey-Markov model (GMM) is more credible, which has a certain reference value to improve the system mechanism for the utilization of foreign direct investment (FDI).
E-waste permits regulation is an effective measure which can encourage firms to recover positively e-wastes. A closed-loop supply chain network including several member firms is developed. The equilibrium conditions of member firms in the network are analyzed in case of compliance and that of noncompliance. The variational inequality framework is given. The game models with compliance and noncompliance are developed. The behavior responses of the firms are analyzed. Under this basis, a solution algorithm for the model is proposed. Finally, numerical examples are proposed and solve two game models. The equilibrium results of the models are compared and analyzed. We find that environmental standards imposed by the authorities are met if the initial license allocation meets the environmental target, in case of compliance. The member firms will choose excess emissions, driven by profit, if the supervision strengthen of government is not enough. The member firms will choose to decrease excess emissions if government increase unit penalty cost. In the case of noncompliance, an appropriate penalty scheme can guarantee that there will be no noncompliant behavior.
In a mixed model assembly line, when demands for different products vary, the workload of stations will change and sometimes work overload may occur. Hiring utility workers to help ordinary workers in case of work overload is a common practice in just-in-time (JIT) production systems, and will affect the constraints and objective of assembly line balancing problem, but no study has ever discussed it. This paper aims to design a mixed model assembly line with utility workers to satisfy uncertain demands in all possible scenarios. The decision maker needs to determine the number of utility workers and ordinary workers, and their tasks allocation in order to minimize the total labor cost. This problem is formulated as a mixed integer programming and is proved NP-complete. A method to estimate the lower bound on labor cost is presented, based on which a heuristic and a branch, bound and remember algorithm are proposed. The numerical experiments on 500 instances show that the proposed algorithms are effective and efficient.
How to choose the direction vector in a DDF is a theoretical puzzle. The paper abandons the traditional exogenous directions, and gives an endogenous method by marginal profit maximization, which not only can be used for efficiency evaluation, also can guide the efficiency improvement. In order to understand the practical significance of this method, the paper also studies the optimal directions of the 30 provinces in China to meet the requirements of industrial growth and emission-reduction. Results show that energy, capital and labor inputs will be greatly reduced when the emission-reduction task gets to a certain extent, emission-reduction task thus should be modest; after endogenizing directions, various provinces have different preference between industrial growth and air quality. Additionally, emission-reduction task under the direction of this paper is more easier than that under the traditional direction.
Achievement reward system widely used in innovative enterprises. Drawing on incentive fitness theory, we examined how achievement reward system may relate to individual creativity in different achievement contexts. Multi-source data were collected from 274 members within 60 R&D teams. Our research findings figured out that mastery climate and egalitarian reward structure positively impact on radical creativity, performance climate and hierarchical reward structure positively impact on incremental creativity. It also reveals 3-way interactions between achievement climate, reward level, and reward structure such that high egalitarian reward structure for high mastery climate to strengthen the association between reward level and radical creativity, on the contrary, high hierarchical reward structure for high performance climate to strengthen the association between reward level and incremental creativity. Implications for practice and future research are discussed.
Enterprises are driven to carry out environmental innovation thanksing to consumers' preferences for environmental products. Meanwhile, affected by other members and external stimulation, consumers' preferences are experiencing complicated dynamic evolution process called "transition" and "mutation". We constructed a model based on computational experimental method to simulate the evolution process of consumers' preferences and the environmental innovation process of enterprises in different scenarios, to reveal the micro cause of environmental innovation behavior, and analyze the environmental performance and financial performance of each environmental innovation strategy in difference scenarios. The results show that the mutations of individual environmental preference act as a "bridge" among the environmental preference evolution of different groups, and they play an important role in promoting the whole environment preference change. On the other hand, when the level of the whole environment preference is low, in order to protect the innovation initiative of enterprises, government should reduce enterprises' innovation risk by incentives or subsidies. Meanwhile, with the help of environmental protection organizations and media, actively promote the "mutation" of consumers' environmental preferences by strengthen education and publicity.
Based on bottleneck model, this paper aims at exploring the welfare effect of the reward-equal-charge (REC) travel credit scheme on commuters with heterogeneity. Under the REC travel credit scheme, the commuters who pass the bottleneck within the peak-time window to either pay certain units of mobility credits. Those who avoid the peak-time window by traveling outside the peak time window could be rewarded credits. A market will be created to decide the credit price and commuters can buy or sell the credits according to their own travel need. The results show that the REC scheme distributes the benefits across the heterogeneous population due to decreasing commuters' equilibrium cost. The REC scheme can guarantee less than 10% efficiency loss compared to the optimal travel credit scheme regardless of the behavioral assumption. Given its simplicity, equity and efficiency, the REC scheme seems to be an ideal choice in practice.
Aiming at the possible multiple solutions and slow convergence rate in the game cross efficiency method, researchers proposed three improved approaches for game cross efficiency evaluation including restrictions on DEA-efficient units and non-DEA-efficient units, or meeting the same normalization constraint. Compared with the traditional game cross efficiency evaluation method, the improved approaches can be utilized for assessing the performances of the DMUs on a common basis, can improve the convergence rate of algorithm, and can eliminate the non uniqueness in the traditional game cross efficiency method. The result shows that the constraint of all the decision making units can be eliminated by improving the discrimination of the decision units, the application of normalization constraint can improve the convergence rate of the iterative algorithm. The proposed new approaches are illustrated with rail transit enterprise of 9 city operation performance, the results also show that the improved approaches are better in discrimination and convergence rate, and we obtained a more credible evaluation results.
The world liner shipping is divided into 18 shipping regions based on shipping services among 593 ports referring to 17 major liner companies, connections among these shipping regions are counted and the global liner shipping network is built. Hierarchical structure in the network is analyzed through a combination analysis of dominant flows and significant flows. The results indicate the global liner shipping network has a hierarchical structure with 4 layers, where regions in different layers have connections with each other and play different roles in the network. Specifically, regions in the first layer are in the core positions, while regions in the second layer and some regions in the third layer have strong intermediary function. To this end, the core regions (i.e. East Asia and Northwest Europe) are further explored: the comparative shipping distances between the two regions and the others are measured from a perspective of service frequency, and also inter-shipping region relationships are analyzed.
R&D projects are complex systems composed by process architecture, organization architecture and product architecture. Firstly, we analyze the features, categories and causes of complexity in R&D projects, and argue that the design structure matrix (DSM) is a quantitative tool to analyze complexity effectively. Further, this paper summarizes the progress in modeling and optimization for complex R&D projects based on DSM and elaborates it in the following frame: from modeling iteration and overlapping in process architecture to sequencing optimization and simulation of process DSM; from measuring dependencies between teams in organization architecture to identifying clustering criterion of organization DSM; from estimating dependencies between components in product architecture to modularity design in product DSM, as well as the evolution and development of DSM clustering algorithm; from modeling and application in single domain DSM extending to multi-domain matrix (MDM). Finally, this paper proposes an integrated analysis frame that is exploring complexity of system architecture, measuring dependencies between elements in DSM and optimizing/clustering of DSM. Directions of future research in this field are presented.
To solve the actual measurement difficult problem of innovation performance transmitted by new information in the process of random walk in the micro decision-making innovation diffusion model, an individual innovative relative index to describe adoption time sequence was built. According to the horizontal partitioning method of five adopter types aiming at the relationship of adopters and adoption time proposed by Rogers, the adoption types of individual adopted successively were defined, which provided the basis for different types of decision makers to take different measures. With the practical application cases of the innovation diffusion process of security system development, the potential people were divided into three categories by their longitudinal management roles. The adoption time characteristics of each management role was analyzed comparatively, and the application methods and applicable conditions of the individual innovative relative index were explained.
There is room for improvement in the efficiency of parametric estimation for the linear regression model and its robustness to heavy-tailed errors and outliers. This paper proposes an alternative robust regression method based on conditional distribution function of the dependent variable, and proves the consistency and asymptotic normality of the proposed estimator by using empirical process theory. Compared with ordinary least squares (OLS) estimator and two other usual least absolute deviations (LAD) and Huber robust estimators, the proposed estimator can grasp the whole distribution information of the dependent variable and more accurately uncover the true data generation process from the sample. It has better robustness to the heavy-tailed distribution of the error term. It is immune to the outlying observations and can be more easily weaken the bad effect of outliers on the parametric estimation. Simulation in various designs shows that the proposed estimator performs well in finite samples and is quite robust to the presence of heavy-tailed errors or outliers, and outperforms OLS, LAD and Huber estimators.
To improve the accuracy and reliability of the unmanned aerial vehicle (UAV)'s optimal path selection, a new optimal path selection method based on interval gray scale is proposed under the environment of uncertain flight surroundings and unknown security threat information. This paper established a new evaluating scale-interval gray, it is containing interval number and grey number. In which, the interval number is used to describe the uncertainty existing in the evaluation process, and the grey number is applied to describe the quantity of information. A reliability index of decision result is defined simultaneously. Then, UAV's path planning optimal selection method is proposed based on interval grey scale. A comparative simulation between the method proposed in this paper and interval decision method is given. The results show that the method proposed in this paper not only can improve the dispersion of evaluation index between the optimal alternatives and the subprime or the worst alternatives effectively, and reduce the degree of hesitation for decision-maker, but also can provide the reliability information for decision-making results, to increase the decision confidence.
The simulation sequence of grey forecasting model is homogeneous exponential sequence. However, there exist a large number of the approximate inhomogeneous sequences in the practical application. The first order reverse accumulative NHGM(1, 1, k) (FTORA-NHGM(1, 1, k)) model and fractional order reverse accumulative NHGM(1, 1, k) (FORA-NHGM(1, 1, k)) model were proposed on the basis of previous research. The perturbation bounds of the two models were analyzed, and the calculation formulas of the FTORA-NHGM(1, 1, k) model and FORA-NHGM(1, 1, k) model were derived. The reason that the two models were suitable for small samples was given. The FORA-NHGM(1, 1, k) model has higher prediction accuracy because it takes full advantage of the new information of the system. It was found that the solution of the FORA-NHGM(1, 1, k) model has higher stability through the instance analysis. Finally, the FORA-NHGM(1, 1, k) model was used in the prediction of the reliability degree of a certain type of weapon with multiple development phase, and higher prediction was achieved.
In order to quicken search speed and improve optimization effect, a Bloch sphere quantum genetic algorithm based on gradual asymptotic search was proposed. In this algorithm, quantum chromosomes were coded by Bloch sphere coordinates bits. Based on least square method, a new update strategy for quantum chromosomes was constructed. A formula, which contained the magnitude and direction of angle in quantum rotation gate, was established. The phase formula in mutation operation was configured. This proposed algorithm was applied in optimization of extreme value in multi-variables function to verify the performance. The experimental results showed that, the proposed algorithm not only has better diversity and randomicity of population, but also has the advantages of less iteration, faster convergence speed and better optimal efficiency.
This paper proposed a novel resource location algorithm for the peer-to-peer cloud systems. The new model introduced trust mechanism into cloud resources positioning process in order to protect the safety and effectiveness. For the reason that resource requirements are generally divided into character precise query and numeric interval query, while traditional methods are only adapted to find character resources, it put forward a new Chord positioning strategy for the numerical interval resources. At the same time, in order to better adapt to the feature of resource multi-condition searches, reduce the cost of resource location and speed up the search process, it proposed a novel single attribute dominating multi-attribute parallel location algorithm. Simulation results show that our schema can ensure high performance and maintain good stability in the peer-to-peer cloud system.
Uncertainty for dynamic grid resource service, a quantitative analysis resources quality of service (QoS) of multiple resources co-allocation reservation strategy is presented in the paper. Based on the analysis of grid job QoS metrics running on the resources, the model obtain the QoS satisfaction quantification, normalization method and establish functional relationship between resource services QoS and reserved capacity. Additionally, the essay works out a balanced load multiple resources nodes co-allocation reservation strategy through the analysis of the relationship between the resource price and reserved capacity in the task cost constrains under the background of the market economy. The validity of the model and its algorithm are presented theoretically. The performance of the proposed strategy was simulated in a large-scale grid simulation system using the real task load of the practical grid system. The results show that the proposed co-reservation strategy outperforms traditional reservation strategy in terms of acceptance of task number, resource utilization and task delinquencies.
A set pair weight Markov chain model based on sequence clustering method for predicting annual precipitation was established in this paper and applied to forecast the precipitation of Baicheng station (Jilin Province) during 2008-2010. It was an improvement of the traditional method by combining sequence clustering method, set pair analysis and Markov chain. Research results show that the improved method make the partition of precipitation grade interval more reasonable. In addition, it can effectively improve the concentration of prediction probability and the prediction accuracy. The measured values all lie in the prediction interval. In conclusion, the method is with high practical application value. As an attempt of the improvement of precipitation prediction model, its prediction effect is satisfactory.
The joint operation of multi-reservoir system in inter-basin water transfer-supply project is a complex problem, due to its multiple objectives, complex structure and cooperated operation policy. A large number of decision variables make it very difficult to obtain the optimal operation policy. In view of that, a set of water transfer and supply rule curves is developed as the rule form based on the storage of individual reservoir in the system. The joint optimization operation model of complex multi-reservoir system is constructed with rule curve shape constraints, considering both water supply and water transfer together. For solving the optimization model, a progressive reservoir algorithm-particle swarm optimization (PRA-PSO) is proposed in this study based on the principle of progressive optimization algorithm (POA). PRA-PSO is based on particle swarm optimization algorithm (PSO) and gradually optimizes single or two reservoirs' operating policy on the purpose of reducing the variable dimensions and improving its global searching ability. Finally, a large inter-basin multi-reservoir water system in Liaoning province is taken as the case study to verify the efficiency and the reasonability of the proposed model and algorithm.
In order to make full use of existing long-term monitoring of component fault recording data, in the production process of industry and mining, the discrete space fault tree (DSFT) is put forward for processing the data. The method for failure probability space distribution is proposed based on DSFT, namely the factors projection fitting method and ANN method. Implementations of the two methods are put forward. Considering the component failure impact because of the working time t and working temperature c, on a range of c: 0~40℃ and t: 0~50 day, the failure probability space distributions are studied. The results are compared with that of CSFT. Comparative analysis shows that: using ANN in DSFT, the prediction results are more close to the relatively real results with CSFT than using DSFT factors projection fitting method. In the case of only the monitoring data without clear system structure, these two methods can be used, but the ANN method is more accurate.