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20 December 2016, Volume 24 Issue 12 Previous Issue    Next Issue

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The Model Choice and Design of Credit Assets Securitization ZHANG Yong, YANG Zhao-jun, LUO Peng-fei 2016, 24 (12):  1-9.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.001 Abstract ( 1179 )   PDF (1836KB) ( 1088 )   Save It is believed that asset securitization is the root cause of the recent financial crisis. But it is one of the main financing instruments to obtain cheap capital from the capital market because it has merits such as transforming illiquid assets into marketable securities and diversifying risk. China International Capital Corporation Limited forecasts that asset securitization will increase over 40 percent a year on average during next 5 years in China.In addition to its importance as a financial and asset restructuring tool, securitization originates various streams of academic research. But some important questions remain unexplained. The established models don't address why the firm should securitize its assets. They don't investigate wether securitization can be a viable alternative for the firm when it chooses its optimal corporate structure. A model of the multi-asset firm which provides an answer to these questions is developed. The model analyzes various corporate structures and validates asset securitization as one of the value maximizing options which can accomplish the optimal incorporation of the assets in the firm. In the framework of the model, the multi-asset firm can optimally choose between aggregating all the assets in one firm, securitizing a part of them through a securitization vehicle, or spinning them off into single-asset firms. These problems belong to the hot and cutting-edge research fields in finance. They are innovative and interesting in application for the practice of the asset securitization.Using corporate security pricing theory, explicit solutions for valuating all contingent claims are provided under the assumption that the cash flow of bank assets follows an arithmetic Brownian motion. An commercial bank is taken as an example, the motivation of commercial bank asset securitization and the optimal issuing method are given. Numerical analysis shows that:Asset securitization can revitalize the capital, and can improve the value of assets; the securitization lever decreases with the correlation coefficient. Finally, some advice is provided to our country commercial Banks asset securitization method. References | Related Articles | Metrics

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The Role of Cojumps and Macro Announcements in Forecasting the Realized Volatility of Chinese CSI 300 Index QU Hui, CHENG Si-yi 2016, 24 (12):  10-19.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.002 Abstract ( 1201 )   PDF (1052KB) ( 1065 )   Save The realized volatility calculated from intraday high-frequency data well measures the risk of financial assets. Therefore studying its forecasting models is of important value. The heterogeneous autoregressive (HAR-RV-CJ) model of realized volatility which uses lagged continuous-time volatilities and jumps as regressors well characterizes volatility's long memory property with competitive forecasting performance, and thus it has been widely adopted. Considering that the cojumps of component stocks can contain information that is not reflected in index jumps, expanding the HAR-RV-CJ model of index volatility with such cojump information is proposed. Specifically, cojumps are identified using the non-parametric mean cross-product statistic, cojump intensity is estimated using the autoregressive conditional hazard model, and then cojump intensity is included in the HAR-RV-CJ model of index realized volatility to analyze the corresponding forecasting performance improvements. Furthermore, considering that macro announcements can affect the whole stock market and thus the cojump probability, macro exogenous variables such as the consumer price index, the gross domestic product and the balance of trade announcements, etc., are included to augment the basic autoregressive conditional hazard model. Its value to cojump intensity estimation and index volatility forecasting is also considered. Using the high-frequency prices of Chinese CSI 300 index and its component stocks from January 1, 2011 to July 11, 2013 as empirical data, it is shown that component cojumps and index jumps do have different characteristics. Besides the fit performance of these HAR models, their out-of-sample forecasting performance is compared using the superior predictive ability test under four common loss functions. The HAR-RV-CI model which includes cojump intensity instead of past jumps as its regressors, has obviously better fit and forecasting performance than the original HAR-RV-CJ model, the HAR-RV-CJI model which includes both cojump intensity and past jumps, and the benchmark GARCH-jump model. Including macro announcements can improve the fit of the autoregressive conditional hazard model and the index volatility model. However, it does not help the out-of-sample forecasting of index volatility, partly due to the low frequency of the macro announcement variables. Above all, our research confirms the value of including component cojump information in the HAR-RV-CJ model of CSI 300 index volatility, and suggests the appropriate model form for superior forecasting performance. Direct extension would be including the cojump information in the vector HAR models to pursue forecasting performance improvements, which has great value for index futures hedging and portfolio allocation applications. References | Related Articles | Metrics

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An Analysis of the Relationship between Order Imbalance and Stock Returns through Quantile Regression Approach for Large-scale Data XU Qi-fa, CAI Chao, JIANG Cui-xia 2016, 24 (12):  20-29.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.003 Abstract ( 1214 )   PDF (2458KB) ( 896 )   Save In the paste decades, much effort has been devoted to exploring the relation between stock price movements and trading volume to gain a better understanding of an issue that is the law of financial market price changes. Trading volume, however, only measures the absolute quantity of trading activity, but ignores the important information that this trading is buyer-initiated or seller-initiated. Order imbalances can provide additional power beyond trading activity measures such as volume in explaining stock return volatilities. In fact, order imbalance can reflect the information buyer-initiated or seller-initiated. In addition, order imbalance can signal excessive investor interest in a stock, and if this interest is auto-correlated, then order imbalance could be related to future returns.In this paper the relationship between order imbalances and daytime stock returns is investigated to obtain more detailed results. We often confront with two main difficulties in the study. The first one is the heterogeneous effects of the former on the latter under different market conditions. Second, it always involves large-scale data processing. To this end, quantile regression approach is used for large-scale data to reveal heterogeneous effects across different quantiles and hope to obtain more reliable results. Quantile regression approach for large-scale data consists of three steps:(1) computing a well-conditioned basis via QR factorization, (2) computing a sampling matrix to reduce the number of observations, and (3) using standard quantile regression for the reduced subset to compute a high-precision approximate solution. Compared to standard quantile regression, memory requirement and CPU time are reduced obviously by the proposed approach. For empirical illustration, first, Shanghai and Shenzhen stock markets are selected to test the effectiveness o quantile regression approach for large-scale data. Second, two lags of order imbalance are used to study the relationship between lagged order imbalances and daytime stock returns. Third, the contemporaneous imbalance is controlled and two lags of order imbalance are used to study the indirect effects of lagged order imbalances to the returns. Finally, the conditional density of response is predicted through estimated conditional quantiles. The empirical results show that one period lagged order imbalance has positive effects with increasing trend on stock returns at higher quantiles while has negative effects at lower quantiles. Furthermore, the lagged order imbalance has negative effects on stock returns when the current order imbalance is controlled, and the negative effect presents a downward trend with the increasing of quantiles. This implies that the order imbalance has good qualities of explanation and prediction for stock returns. References | Related Articles | Metrics

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Contrarian Effect of Semi-Parametric Alpha Strategy ZHANG Li, DENG Li-ying, ZHOU Yong 2016, 24 (12):  30-38.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.004 Abstract ( 909 )   PDF (2385KB) ( 961 )   Save In this article, a semi-parametric alpha strategy model is proposed. The benefit of the proposed model is that the time-varying coefficient can explain the time point risk rather than the risk in a period of time. The local least square method is used to estimate time-varying coefficient and the estimate of alpha is easily derived by solving an estimating equation. Based on the semi-parametric model, the 30 low-ranking stock portfolios of alpha value are selected to observe the contrarian effect. It's found that the stock portfolio is better than market return and gain premium, showing the contrarian effect. The influence of the holding length and the stability of a stock on contrarian effect are also studied, and it's found that the selected stock portfolio performs better than CSI 300 index and its constituent stock. The empirical analysis proves that the semiparametric model can serve as a comparatively better predicting model, and our results can provide some theoretical references for managing risk and improving return. References | Related Articles | Metrics

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The Inertia Interval of Asset and Its' Portfolio under the Knight Uncertainty HE Chao-lin, LIU Meng 2016, 24 (12):  39-46.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.005 Abstract ( 1101 )   PDF (1292KB) ( 894 )   Save Uncertainty is the basic characteristic of security market, and is the main content of asset pricing and investor's trading behavior. The standard expected utility theory shows that an investor has the unique striking price, the market price above which, she is willing to sell; conversely, she is willing to buy. However, due to the existing of uncertainty, asset's equilibrium price or trading price is not a certain value, but is an interval; an investor has no trading behavior within the interval, which is defined as asset's inertia interval. Assuming that the investor is Knight uncertainty aversion, a grade parameter is introduced to measure the degree of Knight uncertainty and the inertia interval of asset and its' portfolio is studied based on the capacity of the feasible region. Based on the model of capacity expected utility(CEU), the preference expression of investor's decision behavior under Knight uncertainty is given by using the capacity instead of the probability measure. Based on the conjugate measure, the inertia interval of asset trading is constructed and the relationship between the degree of Knight uncertainty and the inertia interval is analyzed. At last, based on the model of Black-Scholes option pricing, Jiangtong and Changhong call warrants are selected as the research objects, whose date range is from October 2008 to August 2011, and an empirical study is done based on the daily return of its' underlying asset and different proportion portfolio. Results show that, with the increasing (decreasing) of the degree of Knight uncertainty, the inertia interval of asset and its portfolio expands (shrinks), which results in the declining (rising) of the market liquidity; with the increasing of the degree of Knight uncertainty, the changing of inertia interval is more obvious for the asset and its' portfolio with high price and high volatility; within the moderate rang of Knightian uncertainty, the trading is relatively active for the asset and its' portfolio with high volatility. In the study the puzzles of "non-market participation" and "the idiosyncratic volatility" in the security market are explained, the characteristic of "limited market participation" in the security market is demonstrated, and evidence is provided for the study on the relationship between asset pricing and market liquidity. References | Related Articles | Metrics

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The Value Analysis of Telecommunications License Based on Real Option WANG Xue-rong, LI Qing-hui 2016, 24 (12):  47-53.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.006 Abstract ( 1131 )   PDF (893KB) ( 975 )   Save Telecommunications license has uncertainties and high investment and financing risks. The value of its flexible management is also hard to measure. The traditional appraisal and decision-making methods are obviously defective, often leading to undervaluation and thus missing out on opportunities on the part of decision makers. Based on the option theory, the analysis framework is constructed to assess the assets, as the telecommunication license. The 4G program of China Mobile Limited (stock code:0941) is taken as a case. Through the comparative analysis, the results show that the traditional method has obvious value leakage, and the leakage rate is as high as 50.3%. But real option method can very good to make up for the defect, and more completely and objectively reflects the true value of such assets. It can provide effective support for the asset class of investment decision-making. References | Related Articles | Metrics

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ETF Optimal Asset Allocation and Empirical Research based on Dynamic Conditional Risk Constraint under Time-Varying Loss-Aversion WANG Liang, JIA Yu-jie, LIU Xiao 2016, 24 (12):  54-62.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.007 Abstract ( 301 )   PDF (934KB) ( 820 )   Save Domestic and foreign literature mainly concerns on the perspective of investors' subjective perception for investment portfolio, but in reality, the market risk is not systemic risk which is not transferred with the will of investors and has a strong endogenous characteristic. Therefore, it believes that investors should pay attention to the impact of market risk constraints on portfolio strategy. VaR has some defects such as not meet the subadditivity, etc., therefore, this paper intends to use CVaR to build a model of market risk constraints. Firstly, this paper analyzes the optimal asset allocation strategy model under the static linear loss-aversion and constructs a dynamic market risk measure method based on TGARCH-EVT-POT-GPD method and presents the ETF optimal asset allocation strategy model based on dynamic conditional risk constraint under time-varying loss-aversion, finally solves this model based on Genetic algorithm. In order to simplify the research, this paper selects a stock index futures contract which has the characteristics of continuous trading, and choose the trading day data of the seven futures contracts as continuous sample. The conclusions of empirical research are as follows:(1)The reference return rate and the loss aversion parameter fixed, when the confidence level reached the maximum, the average investment weight of the individual asset with the low risk invested by the investors of loss averse will be higher, and the variance will be small, and the average CVaR of individual assets and its corresponding variance will also increase gradually. In addition, with the increase of the confidence level, the average return rate and the average CVaR of the portfolio will show a trend of gradually increasing. This shows that the investment strategy of the investors is more conservative, and more sensitive to the estimation of the risk under this condition.(2)When the loss aversion parameter and confidence level are fixed, if the degree of loss aversion of investor is higher, even though the high reference rate of reference return rate, the use of this model can also make the average level of suffering the excess loss by investors reduce in the future.(3)When the other parameters are fixed and the loss aversion parameter and the confidence level change respectively, the correlation value of the return rate obtained based on the dynamic CVaR constraint is more than the yield the return rate correlation value obtained based on the dynamic VaR constraint. The correlation values of CVaR based on this model are less than VaR. References | Related Articles | Metrics

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Research on Assembly Line Scheduling Problem Based on Improved Genetic Algorithm LI Jin, LI Hong, XU Li-Li, WANG Hua 2016, 24 (12):  63-71.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.008 Abstract ( 300 )   PDF (2734KB) ( 230 )   Save The Permutation Assembly-line Scheduling Problem(PASP) is a kind of typical production scheduling problem,which has the property of NP-Hard and is the key of Computer Integrated Manufacturing System(CIMS). This problem can be described as follows:N jobs are proceeded in M machines; each job requires M working procedures, of which each procedure requires different machine; they go through the machines in the same order while the processing sequence are also the same in each machines. The main goal for the problem is to find out the optimal processing sequence of N jobs in each machine to minimize the makespan. Genetic algorithm is one kind of heuristic algorithms used to solve permutation Assembly-line scheduling problem (PASP). However, the offspring is difficult to have various genes in good solutions because of the evolution of its selection and crossover mechanism and then leads to local optimum. This paper aims to propose an improved genetic algorithm based on block mining with recombination for solving PASP, in which association rule is used to extract various good genes and increase the gene diversity. These genes are also used to generate various block for artificial chromosome combination. The generated blocks can not only improves the opportunities of finding optimal solutions but also enhance the efficiency of convergence. The proposed algorithm is validated and compared with other five algorithms by numerical experiments, namely Taillard in OR-Library. To compare with other algorithms, the solutions of proposed algorithm are closest to the optimal solution. The results show that proposed algorithm can have not only high the convergence speed but also better solution quality by increasing the solutions diversity. References | Related Articles | Metrics

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The Lateral Transshipments Model and Algorithm for Three-echelon Spare Parts Logistics Network with Service Levels Constraints JI Shou-feng, WAN Peng, SUN Qi, LUO Rong-juan 2016, 24 (12):  72-81.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.009 Abstract ( 343 )   PDF (2127KB) ( 445 )   Save Lateral transshipment is an effective and paradoxical way in sharing inventory process. On the one hand, the total cost of inventory system can be reduced and on the other hand, the transportation costs will be increased in transit. The time window Wk, which helps to identify different service classes for consumer groups through diversified level of service,is presented to build the three-echelon spare parts logistics network with inventory sharing and time service level limit. The distribution network model with the input of n groups of customers contains a manufacturer, a RDC and the DC parameter——the default value is m. In the queuing system model, customer's requirements are independent of each other and obey the Poisson distribution; order lead time of DC obeys Exponential distribution, so as to standardize the actual complex problems. The detailed research idea of dissertation is as following:(1) a distribution network system is established considering the constraints of service level and inventory pooling so that the total cost can be reduced compared with the traditional way, and then some of inventory parameters such as repurchase dots, order quantity and etc. are considered as the decision-making factors at the same time in the three layers of tree network distribution system model in order to optimize the complex system cost of the local and global; (2) in view of the features of the problems derived from the production and the model described in the first part,a new improve algorithm is designed, based on the idea of greedy algorithm to solve the lateral transshipments model problem and get the optimal solution of decision variables in order to provide reference to managers and increase profits and resources utilization by reducing the cost; (3) through an analysis on the sharing project of the company named China Aviation Supplies Holding Company(CASC), the data from Southwest regions (DC1), northwest regions (DC2) and northeast regions (DC3) of the company is used to construct and validate the model so that the rationality of the model and the feasibility of the algorithm can be seen in the three-stage spare parts distribution system of the paper. Research results indicate that in the tertiary distribution network, when the RDC adopt continuous review (Q, R) replenishment strategy and DC with (Q, R, H), the replenishment strategy of system make total cost lower than the continuous review inventory strategy such as One-for-One ordering strategy which is widely adopted strategies of inventory check in production. inventory sharing strategy between DC can effectively reduce the cost of the whole system and in the case study costs is cut about 30%. References | Related Articles | Metrics

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Knowledge Transfer between Enterprises under Asymmetric Risk Attitude CHEN Guo, QI Er-shi 2016, 24 (12):  82-90.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.010 Abstract ( 301 )   PDF (3153KB) ( 220 )   Save The loss of knowledge is popular in knowledge transfer between enterprises. And the risk attitude of the knowledge sender, which is private information in most cases, always influences the effect of knowledge transfer. In order to prevent the loss of knowledge under these conditions, the risk attitude of the knowledge sender is introduced and assumed as private information. And the knowledge sender who discloses a false risk attitude or the knowledge accepter who doesn't believe the knowledge sender is assumed to be punished. Evolutionary game theory is used to analysis the evolutions of the information disclosure strategy of the knowledge sender and the information processing strategy of the knowledge accepter. By discussing the influences of evolutionary equilibriums on knowledge transfer, the way to realize the best evolutionary equilibrium that can not only maximize the total revenue of the knowledge sender and the knowledge accepter but also avoid the loss of knowledge is found. The conclusions of this paper are further illustrated by numerical simulations. The results show that the knowledge sender should punish the knowledge accepter based on its risk attitude; only when the knowledge sender severely punish the knowledge accepter, can the best evolutionary equilibrium be realized; otherwise, only if the knowledge sender is risk aversion party, may the best evolutionary equilibrium be realized when the knowledge sender punish the knowledge accepter more severely or selects a knowledge accepter who severely punish the knowledge sender. The above findings provide effective suggestions for both sides of knowledge transfer on how to prevent the loss of knowledge during knowledge transfer between enterprises and enrich the related research achievements of knowledge transfer between enterprises. References | Related Articles | Metrics

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A Two-stage DEA Model for Evaluation of Supplier with Dual-role and Undesirable Output Factors WANG Mei-qiang, LI Yong-jun 2016, 24 (12):  91-97.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.011 Abstract ( 296 )   PDF (974KB) ( 311 )   Save The situation that supplier simultaneously has dual-role and undesirable output factors would arise, during supplier evaluation based on DEA. A new idea is proposed for processing dual-role factor in present paper. Dual-role factor act as input and output role simultaneously in DEA model, and all dual-role factors essentially are intermediate variables of network DEA model; to move forward a single step, production system with dual-role factors certainly can be decomposed into multiple subsystems, and anyone dual-role factor is both an input of a subsystem and a output of another subsystem.To measure the efficiencies of suppliers, based on existed relational two-stage DEA model, production and operation of suppliers are treated as two-stage process, and dual-role factors of suppliers are treated as intermediate variables of two-stage process, by virtue of expressing all output factors as weighted sum, whereas the weight of undesirable factors being negative, a two-stage DEA model for evaluation of supplier with dual-role and undesirable output factors is proposed.A real example with 18 third-party logistics providers verifies reasonability and feasibility of the method. The related attributes for 18 third-party logistics providers are partially taken from related attributes of literature[10] [25]. In this example, the input is total cost of shipments, and undesirable output is parts per million of defective parts, and desirable output is revenue, and dual-role factor is supplier capability. The contribution of the present paper consists in two aspects, one is the processing of dual-role factor, a new idea and method is proposed in the processing, the other is that a two-stage DEA model for evaluation of supplier with dual-role and undesirable output factors is proposed. References | Related Articles | Metrics

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Research on Recycling Path Optimization Problem with Feasibility of Path and Concentrated Treatment Mode LIU Yan-qiu, XU Shi-da, ZHANG Ying, LI Jia 2016, 24 (12):  98-107.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.012 Abstract ( 325 )   PDF (1884KB) ( 297 )   Save In recycling path optimization, Third Party Logistics are gradually used to transport Product Recovery Management, due to the uncertainty of information of demand point location and quantity affected manager making policy by optimal vehicle number. However, demand of node are small in the actual operation process. In general, concentrated treatment mode was used in the transport activity. And due to the limited of environmental factors, cannot ensure every path is feasible, transport vehicle need to find alternative routes. In this context, how to work with the Third Party Logistics, how to determine the location and capacity Qj of recovery stations for manufacturers; How to respond to the need and situation of consumers and manufactures, to determine the optimal recycling path; As well as how to transform concentrated treatment mode to the mathematical model in this paper, all these are the problems to be solved.Based on the above issues, an approach base-on Feasibility of Path and Concentrated Treatment Mode is developed for recycling path optimization. Firstly, a computerized model of based on vehicle routing problem in reverse logistics is established after comparing and analyzing the model of LRP and OVRP. Furtherly, an improved ACO algorithm by improving the coding mode and possible selection(ACO-nso) is proposed. Finally, The convergence of the ACO-nso proposed is proved,and the effectiveness of the algorithm is proven with an example and the applicable scope pf the problem is discussed.It is shown that the model and ACO-nso algorithm improved in this paper is suitable for under-intermediate-level scale recycling path routing problem, moreover requires a shorter time of calculation and better global searching ability than traditional intelligent optimization algorithms. The data used in numerical simulation, is primarily references to the relevant literatures and simulation data. The ACO-nso also promotes the research about path optimization and other combinatorial optimization problems. References | Related Articles | Metrics

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Case-Based Decision Analysis Method based on Regret Theory for Hybrid Multiple Attributes Decision Making HAN Jing, YE Shun-xin, CHAI Jian, LI Jian-qiang 2016, 24 (12):  108-116.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.013 Abstract ( 363 )   PDF (1208KB) ( 202 )   Save Although case-based decision theory is prevalent with solving decision issues, the main drawback is that decision makers' psychology has not taken into account. With the foundation and constant improvement of lifelong accountability system and responsibility for the investigations mechanism, it is nonnegligible to consider personal psychological factors. Therefore, how to integrate psychological and behavioral characteristics of decision-makers with case decisions becomes a concern for research question. Thus, in this paper, a multiple-attribute decision making method based on regret theory is proposed. In terms of regret theory, the regret-rejoice value is presented to represent the impact of psychological behavioral factors on the utility of decision-making, and a model with regret aversion, which integrates the cognitive limitations and psychological factors into the decision-making framework is built. At the same time, considering the complexity and uncertainty of reality, and the diversity of information, the case attributes are expanded to five types, including qualitative data, crisp data, interval data, linguistic variables and interval intuitionistic fuzzy data. Then methods for calculating similarity are presented by layering computing and exponential function. Specifically, first, similarity is calculated with an improved method proposed by the authors, so as to screen the approximation case set. Second, the objective utility of cases is calculated, the utility with regret theory is adjusted to get perceptible utility, then perceptible utility is integrated with similarity and the alternatives are ranked. Finally, taking the site selection of a PX project as an example and comparing with CBR and traditional CBDT, the result of this method is found relatively reliable, no matter in terms of wind direction, distance from water sources to cities, and the destructiveness of natural disasters. In addition, it is more in line with the actual decision process in context of the accountability mechanisms, reflecting the regret aversion behavior of decision makers. To sum up, the adaptability for case-based decision theory is expanded to complex multiple-attribute problems. Besides, the objective case is combined with decision maker's subjective feelings, and the effect of decision is taken maker's regret-aversion on final decision is taken into the model; finally, the feasibility and effectiveness of the proposed method are demonstrated by a site selection example. References | Related Articles | Metrics

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Analysis of Using Duality for Presolving in Linear Programming HU Yan-jie, HUANG Si-ming, N. Adrien, WU Yu 2016, 24 (12):  117-126.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.014 Abstract ( 299 )   PDF (1266KB) ( 306 )   Save With today's large number of data, the use of linear programming is facing the real big size of applications with increasing complexity, so data presolving techniques for solving linear programming problems become very important. Duality not only help to solve the original problems (such as dual simplex method), but also is an important part of presolving techniques before solving the problems. For presolving, based on a model of linear programming problems with upper and lower bounds, two methods:dominated columns and duplicated columns in detail to realize using duality in presolving are analyzed and summarized, and the nature of weak dominated columns is proved by using invalid constraints concept. Finally, algorithm is implemented with C programming language for testing standard internationally accepted linear programming problems with number of variables greater than 1500. The results for tested problems show that:(1) For general linear programming problems, using duality in presolving can reduce the size of the problems effectively in two ways:by directly reducing the number of variables and non-zero elements of the problem and by influencing other presolving methods to reduce the number of constraints indirectly. (2) From the view of reducing problem size, most of the problems have shown that results of presolving process for dominated columns are better than those for duplicated columns. Apart from verifying and proving the importance and feasibility of using duality in presolving, new references are also provided for future researchers on presolving methods and techniques in order to come up with more valuable presolving methods. References | Related Articles | Metrics

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Contract Design and Decision Making of Multi-Uncertainty Relational Supply Chain GAO Jia, WANG Xu 2016, 24 (12):  127-138.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.015 Abstract ( 322 )   PDF (1845KB) ( 185 )   Save With the development of globalization and specialization, modern supply chain becomes leaner but also more fragile than before. Meanwhile, diversification of customer taste makes it much more harder to predict demand. All these together put supply chain in an environment where both supply and demand are uncertain.Moreover, due to the growing uncertainty faced by supply chain and business philosophy changing from "competition" to "competition and cooperation", traditional contract designed for determined environment can no longer function very well.With the observation of all that, relational contract is modeled into supply chain where both supply and demand are uncertain. Thus Stackelberg model is first used to build an two-echelon supply chain consists of one supplier and one retailer, and it's supply and demand uncertainties are described by random yield function and newsvendor model, respectively.Then a relational type commitment contract,in which supplier commits on least-supply quantity q and retailer commits on max-purchase quantity Q is designed.By analyzing the decision-making process about both centralized and decentralized scenario under the above contract, it is found that, for centralized supply chain, there is always an optimal strategy no matter whether Q or q is or are the decision variable respectively or simultaneously. For decentralized supply chain, all optimal strategy share one common threshold w(wholesale price)=p(emergency purchase price). When w>=p, supplier will rely more on emergency purchase, but may lose expected payoff due to lower marginal profit compared to w=p, but his expected payoff will decrease. On the contrary, supplier's expected payoff will increase due to larger order from retailer.What's more interesting is that, counter intuitively, a "p" bigger than "w" won't always hurt in our contract, it can actually improve decentralized supply chain's overall performance, which is important because business competition is shifting from inter-enterprise to inter-supply chain.At last, the parameters in our model are specified by their reality constraints, the numerical analysis shows that our contract can improve the decentralized supply chain's performance to more than 95% of the centralized one's and sensitivity analysis of p, supply and demand uncertainty on contract parameter, optimal strategy and expected payoff are also conducted.Over all, the research of SCM is extended by combining supply and demand uncertainty and relational contract together, and some useful managerial insights are provided for supply chain managers under themuch more uncertain business environment. References | Related Articles | Metrics

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The Research on Government Low-carbon Regulation Guiding Enterprise Low-carbon Technology Innovation in Dynamic Game WANG Zhi-guo, LI Lei, YANG Shan-lin, GONG Ben-gang 2016, 24 (12):  139-147.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.016 Abstract ( 344 )   PDF (2702KB) ( 175 )   Save Low-carbon technology innovation is an important means to solve the high emissions. Firstly, the complex of low-carbon technology innovation system is analyzed. A dynamic timed game model is established and the influence of government decision-making to time characteristics of enterprise low-carbon technology innovation system is analyzed according to game state and game formation mechanism between government and enterprise in different periods and different stages. In this decision-making game which is between governments and enterprises regarding to the behavior of low carbon technology innovation, the object function in certain period is based on current game state and the game result for previous progress. After that, set up the enterprise objective decision-making model under the dynamic game and government objective decision-making model under the dynamic game. The government's low carbon regulation will directly affect the behavior of low carbon technology innovation decisions,this business is to maximize profits for the purpose to organize the production of enterprises. The government goal of low carbon economy and environmental protection responsibility requires a steady increasing of enterprise's sales in certain amount emission per unit, or stable steady decreasing in emission per unit amount of sales. This requires constant low-carbon technology innovation. Finally, the double integration path selection method is given for guiding low-carbon regulation. The application simulation is verified based on the data of some enterprises investigated in Anhui Province. Validation results show that the low-carbon regulation path is reasonable. So that government could establish scientific and suitable policy guiding enterprise low-carbon technology innovation in low carbon environment. References | Related Articles | Metrics

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The Impact of Partnership and Logistics Capability on Supply Chain Integration LIU Hua-ming, WANG Yong, LI Hou-jian 2016, 24 (12):  148-157.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.017 Abstract ( 328 )   PDF (1160KB) ( 588 )   Save Despite the abundant research focusing on supply chain integration, which factors affect supply chain integration, and how they affect supply chain integration remain unexplored. From the perspective of supply chain management, a conceptual model for the relationship among partnership, logistics capability, and supply chain integration is proposed and empirically tested by using the data collected from 218 enterprises in the supply chain with a structural equation model.The results show that both partnership and logistics capability have a significant positive impact on supply chain integration. Meanwhile, the results also show that partnership has a significant positive impact on logistics capability. Furthermore, through the logistics capability, the indirect influence on supply chain integration caused by partnership is much greater than the direct influence on that. The model is also tested across different firm sizes and industries. The results show that the effects of partnership and logistics capability on supply chain integration for firms of different sizes and industries are difference. A new perspective for researches about impact path and mechanism between partnership and supply chain integration is provided, and a basis for Chinese enterprises in different firm sizes and industries to selectively deploy partnership and logistics capability for improving supply chain integration is also proposed. References | Related Articles | Metrics

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Research on Competitive Facility Location Under the Operation-sustainable Chance Constraint——An Efficient Real Coded Genetic Algorithm ZHU Hua-gui 2016, 24 (12):  158-165.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.018 Abstract ( 302 )   PDF (1018KB) ( 211 )   Save The competitive location problem is one of the key problems for the research areas of spatial economy, regional development, combinatorial optimization and system engineering. Generally speaking, competitive location problems tend to maximize the market share as the final goal without considering the sustainable operation capability, which leads to the deviation of the actual operating results from the original intention of decision. To tackle this problem, the ability of continuing operations is considered, the constraints of the probability of sustainable operation probability are given and a nonlinear integer programming model for competitive location is established. In this paper, an effective Real Coded Genetic Algorithm (RCGA) is presented for the competitive location problem to maximize the market share with the constraints of the sustainable operation probability. Genetic algorithm is a widely used random search algorithm and has very good performance in solving nonlinear programming and combinational optimization problems. First, it is assumed that the operating costs are the function of the size of the competitive facility and the constraints of the sustainable operation probability are formulated. Then a nonlinear mixed integer programming model for the facility location-design problem is built based on the gravity attractive model. Second, a RCGA is presented regarding the value types of the location variables and the scale variables. The numerical results show that RCGA can get high quality solutions in very short time, and the gap between feasible and optimal solutions is less than 0.5%. Related issues are also discussed by comparison to other algorithms to further demonstrate the success and practicability of the proposed approach, which provides an alternative way and an effective algorithm for the competitive location problem. References | Related Articles | Metrics

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Vehicleand Cargo Matching Method Based on Improved Quantum Evolutionary Algorithm MU Xiang-wei, CHEN Yan, GAO Shu-juan, YAO Si-yu 2016, 24 (12):  166-176.  doi: 10.16381/j.cnki.issn1003-207x.2016.12.019 Abstract ( 336 )   PDF (1898KB) ( 334 )   Save In order to solve the logistics supply and demand information asymmetry and improve the efficiency of logistics business docking, a lot of public logistics information platforms or systems have been built to post and share the logistics information. There exists relative research about how platforms passively response user's query for logistics supply and demand information, but the research about how to match vehicle and cargo information proactively and intelligently is very few.Vehicle and cargo matching problem is regarded as a kind of combinatorial optimization problems in this paper, a mathematical model has been established, and the model declares two decision variables:the constraints and the objective function. A kind of quantum evolutionary algorithm has been designed and proposed to solve the vehicle and cargo matching problem, which is improved by the method of the attenuation fitness with constraint punishment. An index, Quantum Swarm Maturity Value (QSMV), is introduced as a reference criteria for the quantum evolutionary algorithm exit. Vehicle and cargo matching problem based on quantum evolutionary algorithm can be solved into six steps, including:quantum group initialization, fitness calculation, selection of the optimal quantum individual, the judgment of algorithm exit, quantum group evolution and the optimal individual decoding. In the experiment, experimental data is given a set composed of 5 vehicles and 7 cargos. The exact solution is obtained by using the traversal method, which takes 6 hours and the fitness is 0.283226. An optimal solution is obtained by the quantum evolutionary algorithm, which takes 0.656 seconds and the fitness is 0.2832. Furthermore, the improved quantum evolutionary algorithm is compared with the standard genetic algorithm, experiment results show that quantum evolutionary algorithm's convergence speed is increased by 58%, average error is reduced by 86% and stability is increased by 32%. Experiment results also show that quantum group scale has "bottleneck" problem, larger quantum group scale does not improve the algorithm performance obviously, and quantum rotation angle increment is positive correlation to algorithm convergence speed, and negatively correlated to global search ability.The results show that the improved quantum evolutionary algorithm can efficiently get the optimal solution for the vehicle and cargo matching problem, and enable the public logistic information platform to intelligently recommend reasonable supply or demand information for different users, and help users reduce the idle vehicles rate and empty-run rate, and improve the utilization ratio of logistics information resources. References | Related Articles | Metrics