Based on 29 existing climate change integrated assessment models (IAM), this paper discusses the progress of integrated assessment models for climate policy with five key aspects in IAMs including model structure, uncertainty, equity, technical change and abatement mechanism. There are three main categories in IAMs including optimization models, computable general equilibrium (CGE) models and simulation models. In order to model the uncertainties in climate change, IAMs in the future need to answer questions such as the probability distribution of the effects of climate change, the degree to which human society is risk averse and the rate at which human society discounts future benefits and costs relative to those in the present. As for the intergenerational equity, prescriptionist supports the low discount rate and immediate actions in climate policies, while descriptionist supports the high discount rate and "step-by-step" actions in climate policies. As for the international equity, IAMs should place greater welfare weights on the low-income regions. IAMs have the tendency to make technical change endogenous by three approaches including direct price-induced, R&D-induced and learning-induced. As for the GHG emission reduction mechanism, most researchers who use the cost-benefit analysis consider that quantity-based mechanisms are more efficient than price-based mechanisms.
This paper presents a new warrants pricing method based on nonparametric estimation with respect to China warrants market and Hong Kong warrants market, and applies it to out-of-time prediction. The result shows that model-guided nonparametric correction method outperforms direct nonparametric method, semi-parametric model and parametric model. In addition, model-guided nonparametric correction method has better performance than other models in terms of out-of-time prediction ability.
Three compensation mechanisms, that is loss compensation, revenue compensation and fixed government revenue compensation, and their impacts on venture capital scale and efficiency are studied in the framework of incentive theory. The main conclusions include: loss compensation would undermine the efforts of venture capitalists, which is not conducive to enhancing efficiency and expanding scale of venture capital; revenue compensation and fixed government revenue compensation have the same incentive characteristics, they can promote the formation of venture capital partnerships, expand the scale and enhance the efficiency of venture capital at the same time. When government compensated venture capitalist with the same cost, revenue compensation will attract more private investment. Furthermore, due to the possibility of destructing venture capitalists and private investors' limited liability protection, the implementability of fixed government revenue compensation can not be guaranteed.
The paper focuses on the stock market cycle fluctuation in China using the regime switching model. Our results indicate that: heteroscedastic four states Markov regime switching model describes the stock market fluctuation characteristics well; from December 26, 1996 to December 31, 2010, after the raising limit, stock price fluctuation exhibits four different kinds of moving situation: dropping sharply, dropping mildly, rising sharply, and rising mildly; our stock market express rising rapidly and dropping slowly, and dropping slowly is the major fluctuation; periodic fluctuations in the stock market generally divide into more volatile and less volatile two periods.
Comparing to the current research on PPP project's risk allocation based on the symmetry of participants' status, this paper combines the realistic asymmetry of participants' status in the bargaining game theory to construct a bargaining game model for PPP project's risk allocation under the conditions of complete and incomplete information respectively. Through the analysis of the model, the sub-game perfect Nash equilibrium is found as the corresponding solution. The findings of this paper not only supplement the research of the risk allocation of PPP project in the aspect of theory but also have great realistic significance in ensuring the construction of quasi-public project.
Based on the two-item newsvendor model, this paper researches on the jointed pricing decision of remanufacturing system under uncertain demand from the perspective of solo manufacturer. Firstly, the two-item newsvendor model is built based on profit maximization. Then, the concavity of objective function is proved and the Karush-Kuhn-Tucker optimal conditions are given. Moreover, the solutions of the model and the influence of remanufacturing products' WTP to pricing, productivity and profit are analyzed numerically. The results show that the new products' pricing and two products' productivity will decrease while the new products' pricing and manufacturer's profit will increase with the increasing of remanufacturing products' WTP.
Within the lifetime of a modern production system, mixed-model assembly line rebalancing problems caused by new product introduction are frequently aroused. This paper considered the rebalancing cost and efficiency of the new line simultaneously. A new method evaluating rebalancing cost was proposed and the rebalancing model was formulated. A multi-objective genetic-algorithm was developed to solve this problem. The performance of the algorithm was tested on mixed-model balancing benchmark problems. Experimental results show that this algorithm can solve the rebalancing problem effectively and outperforms algorithm proposed by Merengo et al.
On the basis of considering the employee learning rate and handling time in-between processes, this paper researches batches processing modes for manual operating system. We give three formulae about production cycle of a batch of parts for the group-processing mode and production-line modes with method of strict theoretical proof. In the given example case, we analyze and compare the three operation modes, and have got the results that the group-processing mode has the shortest production cycle. Meanwhile, we establish the time model of the processing and handling about each production process and transport process, analyze the number of parts for each handling process, and design a global optimization algorithm about the number of handling equipment for the transport process. The decision-makers can effectively arrange processing and handling tasks of a batch of parts by using the information which can be got from the time model and the number of handling equipment and solve the minimum quantity of the handling equipment by the global optimization algorithm which is firstly introduced in the paper.
Factorial effect principles (effect sparsity principle, effect hierarchy principle and effect heredity principle) are often used to justify the rationality of factorial design theory and data analysis strategies. As for fractional factorial experiments with non-normal responses, this paper proposed a multi-stage Bayesian variable selection (BVS) approach combining generalized linear models (GLM) with the factorial effect principles when there are complex aliasing effects in screening experiments. Firstly, a binary variable indicator was used for each variable of the linear predicator in GLM. Secondly, the prior information of the variable indicators was considered in three different stages through the factorial effect principles respectively. Thirdly, significant factors could be identified by the posterior probabilities of the variable indicators in GLM. Finally, the results of a simulation experiment demonstrated that the proposed method not only can simplify the prior set for the parameters in GLM, but also can effectively identify significant factors in the fractional factorial experiment design with non-normal responses.
Since the generation of electric power contributes a large percentage of carbon dioxide (CO2) emissions in China, CO2 reduction in the electricity power industry is the most effective way to develop low-carbon economy. In the paper, the multi-attribute procurement auction theory is introduced into the electric power generation market. The CO2 emissions from electric power generation and the grid tariff are identified as key attributes for selecting winners. Based on these two attributes, an optimal multi-attribute procurement mechanism considering CO2 emissions in electric power generation market is firstly established. Then, the incentive compatible mechanism analysis and a reasonable procurement auction process are provided. Finally, a case study is conducted to illustrate such procurement auction process motivates the electric power generators to implement the economic behavior and CO2 emission reduction behavior. Therefore, the proposed multi-attribute procurement mechanism is of great significance in the market-oriented reformation of electric industry and the development of low-carbon economy in China.
In this paper, the widely accepted Almon procedure was extended to estimate industry-by-industry IO tables based on fixed industry sales structure assumption to avoid negative flows. Based on the Almon procedure's point of avoiding negative flows during iteration and derivation formula of the fixed industry sales structure assumption, we constructed iterative formula, and adopted scale factors in the part where negative entries may arise to avoid negative flows in every iterative step. Also, we gave intuitive interpretations for iterative formula and scale factors. To test the validity of the method, empirical comparison between the extended Almon procedure and the traditional approach is employed. The extend Almon procedure is proved to have better performance than the traditional method, since it yields more accurate estimation than that of traditional method, against the official survey-based IO table.
Waste disposal facilities are kinds of typical "semi-desirable facilities". Some incompatible objectives such as cost and obnoxious effect etc. should be considered in the process of location and other related decisions of these facilities. The location-routing problem in waste logistics network in cities is one kind of typical periodic location routing problem (PLRP), for different population centers can be visited by different frequency in certain period, however, the multi-objective optimization of this problem is short of research. A multi-objective PLRP is studied in this paper, which combines the practical situation of waste logistics network in cities. A new method is proposed measuring negative effect based on the concept of dispersion distance; a mixed integer programming model concerning two objectives of average total cost and average negative effect is raised; a multi-objective evolutionary algorithm is devised to solve location-allocation problem, visiting scheduling problem and vehicle routing problem simultaneously. The global algorithm strengthens an extension local search deepening the search of feasible collection and transportation plans; a sub-algorithm termed DRECW-LS which strengthens the diversification, randomization and local search based on ECWA solves the periodic location-routing problem. The computing example shows: the algorithm can solve analogous large size problems and shows excellent quality and computing efficiency.
The study finds that spurious Granger causality occurs between independent weak stationary processes, and the Wald statistic modified by the traditional HAC method has higher probability of spurious causality than the primitive Wald statistic. At the same time, the paper shows that estimation of the long-run variance is the underlying reason of occurring spurious causality, the Granger causality test statistic constructed based on modified truncation parameter of traditional HAC method has a limiting distribution that does not depend on nuisance parameters. Through setting various weak stationary processes and using Monte-Carlo simulation technology, the paper finds that the new Wald* statistic can greatly decrease the probability of occurring spurious causality, and it is robust to the persistence of data process and sample size, but has some test size distortions.
The period effect for the limit distribution of a higher-order Markov chain is discussed and the correlation between the connectivity of higher-order matrix and stationary distribution is studied. The conditions of the existence and uniqueness for the stationary distribution of the higher-order Markov chain are proved which improve the theory of parameter estimation. Then the stock index data is modeled with the application of higher-order Markov chain. The estimated parameters indicate that there exist correlations between the neighbors of stock price, and the forecast results are compared with the traditional model.
In this paper, a machine repair system with warm standbys was studied. There were R repairmen under N-policy vacations in the system. Balking and reneging of fault components was also considered. Differential-difference equations of state probabilities are obtained by using Markov process theory, and the exact expression of failure state probability is derived by using matrix theory and the inverse of Laplace transform. Then reliability and the mean time to the first failure of the system are presented. Finally, numerical results are presented.
In this paper, a class of new fuzzy linear programming problem with intuitionistic fuzzy elastic constraints is constructed and discussed based on the fuzzy structured element method. By introducing the basic principle of the weighted characteristic number (WCN), the authors define an order relation and expand the Verdegay's method of the fuzzy linear programming. Then, the authors transform the new fuzzy linear programming problem into two equivalent and clear linear programming models with parameter constrains. The compared optimal feasible solution of the two equivalent and clear linear programming models is given. Finally, the authors give a numerical example to illustrate the general method for solving the proposed fuzzy linear programming problem.
First this paper makes a summary of possibility degree, and proves that four of the possibility degree calculation methods are mutual equivalent. This paper proposes possibility degree axiom, and proves that the priority results of two kinds of integration priority method appearing frequently in the literature, are completely consistent, and do not have the rank preservation. This paper presents a new integration priority method, and proves that the priority result can keep the former interval number orders using this method. An example is given to explain the rationality and feasibility of the new method finally.
A systematic carding on the research of grey incidence analysis modelling have been made in this paper. The grey incidence analysis models developed from the models based on incidence coefficient of each points in the sequences in early period to the generalized grey incidence analysis models which based on integral or overall perspective. From the grey incidence analysis models which measuring similarity based on nearness to the models which considering similarity and nearness respectively. The objects of the research advanced from the analysis of relationship among curves to that among curved surfaces, then forward to the analysis of relationship in three-dimensional space and even the relationship among super surfaces in n-dimensional space. The further problems remained to be studied in this field are clarified too. Several research approaches of grey incidence analysis modeling are clearly revealed.
Traditional personnel-job matching methods assume that personnel capacity is homogeneous, which can not reflect the needs of adopting strengths and avoiding shortcomings. Based on the difference of human capital inner quality, respecting personnel individual strength characteristics, "four in one" personnel matching method based on human capital advantage structure is constructed. According to group job comprehensive indicator system, from the starting point of human capital advantage structure identification method which can describe relative comparative degree of strengths and short comings, job human capital ideal structure is formed according to comprehensively considering the human capital of international level, domestic level, organizational level, personnel human capital advantage structure is formed according to comprehensively analysis of self identification and others' identification. Taking difference degree of advantage structure as point of penetration, personnel-job matching decision is made from the point of whole optimization. The advantage of the method is reflecting "human oriented" and development, maximizing personnel advantages. The calculation example shows the feasibility and validity of the method.
Unlike traditional system integration, the knowledge systems integration is to integrate knowledge-based systems in enterprise in order to weave different domain knowledge. While weaving domain knowledge, the privilege control is required that the activity's scope of agent should be analyzable and controllable to keep agent's autonomy and system's regulation in a balance. With the assumption of ontological interaction commitment, we introduce the structure and algebraic definition of privilege, and then propose the privilege model for privilege control in knowledge systems integration. The privilege model provides a method to represent the privilege of agent's action, to delimit the promised privilege, and to analyze the conflict of privilege compliance.
Business process efficiency is one of critical performance indicators in an enterprise business process management. There was the lack of real-time and quantitative analysis in most existing approaches of business process performance analysis, which could not be used in improving and optimizing business process rapidly. To deal with this problem, this paper presents a real-time management method for monitoring business process efficiency by ECA rules. Through analyzing index architecture of business process efficiency, a framework for managing business process efficiency in real time was proposed and a relational model of multidimensional data warehouse was constructed, and ECA rules and a real-time management control engine for monitoring business process efficiency are studied and analyzed. Finally, results of contrast experiment show that efficiency of the business process implementation increases about 10% and average time of the business activity implementation decreases about 15% through a case test for an order contract review process in a clothing enterprise.
Empirical mode decomposition (EMD), which can extract real time-frequency characteristics from non-stationary and nonlinear signals, however, has an involved end effect in the course of getting the envelops of the signal by the spline interpolation. In this paper, a new method based on wave matching to deal with the end effect is proposed, which replaces the ends of the mean envelop with the most suited sequence in the inner envelop making the post-dealing mean envelope have the most similarity of tendency with the real one. Compared with the classical boundary extension algorithm, the improved algorithm can increasingly suppress the end effect in EMD and reflect the true original signal frequency information as well as amplitude value, and it is well used to forecast the trend of stock market prices. The experiments show that the presented method can more effectively improve the prediction accuracy.
Queue rules are an important component of the queuing system. They directly influence the operational efficiency of the queue system. This paper constructs the queue system simulation model with the complicated queue system as a research object. Further, considering the diversity of queue rules, we propose a genetic optimization algorithm based on queuing system simulation model, which is further modified. Analyzing the eye hospital actual data, it shows that comparing with first come first service (FCFS) rule, the queue rule designed in this paper can reduce more than half of the average hospital waiting patients and ensure the long-term stability of the system. This paper effectively combines the simulation model and the genetic optimization algorithm. It is of decisive significance to optimize queue rules of a complex system and improve the operational efficiency of a queue system.
A deadlock-free scheduling scheme and optimization for underground locomotive transportation system are presented. Firstly, three resource allocation Petri net models under different dispatching policies are modeled, their corresponding maximal makings boundary setting algorithms are designed, and the deadlock-free property of dispatching under maximal marking boundary setting is proved. Then, a genetic algorithm is proposed to minimize the time cost and energy cost, in which the transitions sequence is encoded as a chromosome, the feasibility of chromosomes are tested and amended by judging the transition firing condition, so that each chromosome can be decoded into a feasible schedule to satisfy all resource allocation constraints. Finally, an experimental example is simulated. The designed maximal marking boundary setting algorithm of deadlock-free schedule and genetic algorithm provide reliable theoretical foundation for underground locomotive transportation dispatching.
Timetable of the airport coach is designed based on the relationship between the headway and the passenger volume. Firstly, the timetable is proved to be the important factor influencing the passenger volume and the operating cost. Secondly, referring to the theory of the firm equilibrium, the relationship between the timetable and the passenger volume is discussed. Thirdly, based on the theoretic analysis, the optimization model of an irregular timetable with changed demands is built, and the aim of this model is to maximize the profit of the airport coach system. Finally, the optimized timetable and the being needed coach vehicles are calculated by solving the model with matrix code genetic algorithm.
Due to the lack of reliability indicators and measures for assessing the performance of a day-to-day road network presently, a first time to failure based dynamic reliability indicator was defined by using stochastic failure sequences analysis in reliability theory. It is defined as the probability that the number of days during which the day-to-day road network sustains a reliable performance level equals that of indicated days. This indicator describes the ability that the road network provides reliable service uninterruptedly. An approximate measure was adopted to estimate the indicator, and a corresponding algorithm based on Monte Carlo simulation was proposed. Finally the feasibility of the dynamic indicator and its measure are supported by numerical results of a network.
The automatic incident detection system (AID) has provided an effective way to achieve the freeway incident detection. To have a good command of the traffic running state on highways and provide reliable efficient decision support in disposing an emergency, authors extend two-class classification and SVM to multi-class classification and SVM. According to the process of traffic incident, the traffic state is divided into three classes: the traffic state of free flow, the traffic state of increased congestion, the traffic state of dissipated congestion. This method researches on the VISSIM to obtain the original data set, and selects input traffic features through the principle component analyses (PCA) method, then constructs the multi-class classification and support vector machine (SVM) model, and applies the genetic algorithm (GA) to optimize the model parameters. Finally, the multi-class classification and SVM model with GA has obtained the satisfactory effect.
The paper presents a novel hybrid flow shop problem with multi-jobs families and no-buffer, and it is derived from hybrid operation of container terminal. Since the problem has these characteristics such as multi-jobs families, no-buffer, dedicated machines, setup time and operating time dependent on machines and sequence, we construct a new mixed integer linear programming model. As for the NP-hard of the problem, a constructive heuristic algorithm is developed to solve it. In the algorithm, a scheduling list consisting of triple machine matching pairs is dynamically produced based on machine's inventory and quota, and the triple machine matching pair corresponding to a job denotes the route or trip of a job in hybrid flow shop with 3 stages. Further, the performance of the algorithm is evaluated and validated through simulation experiments and analysis of lower bound, and can achieve approximate optimal solution with low computing cost. Especially, the algorithm is very effective for big size problems in practice.
To improve the optimization capability of traditional shuffled frog leaping algorithm (SFLA), a new immune-shuffled frog leaping algorithm (ISFLA) by hybridizing clone selecting algorithm and shuffled frog leaping algorithm is proposed and used for optimal scheduling of cascade reservoirs. In the proposed algorithm, the total population periodically executes grouping and shuffling operations, and after shuffling the subgroup is constructed and executed cloning and selecting operations to improve the local search ability. Finally, the proposed algorithm is verified through practical example, and the result shows the algorithm is feasible and fast efficient, which provides a new approach to solve optimal scheduling of cascade reservoirs.
A convex optimization approach is presented for solving the time optimal trajectory planning problem along given parametric path of computer numerical control (CNC) systems. The tangential acceleration and chord error constraints are considered in the problem. By constructing the feasible state space of the constraints, the influence of the constraints to trajectory planning is discussed. By using nonlinear variable substitution, the time optimal trajectory planning problem is formulated as a time-independent convex optimal control problem. Based on control vector parameterization (CVP) method, the resulted optimal control problem is further converted into a convex optimization problem which can be solved efficiently. The second derivative of the path parameter with respect to time (parameter-acceleration) acts as optimization variable. The sequential quadratic programming (SQP) method is used to obtain the numerical solution. The results of time optimal trajectory planning for two test paths demonstrate the effectiveness of the approach.
It is well-known that different scheduling orders for UAV performing multitask will result in different income. In allusion to this, this paper proposes a state transition tasks scheduling model based on the features of the task implemented. Then we deduce the cost and income of the task scheduling for acquiring an optimality criterion whose descending order decides the optimal scheduling sequence. After that, the optimality of the policy is proved by simulation and we compare our algorithm with exhaust search and genetic algorithm, which shows that the theoretic result is consistent with.
A novel grey modeling based estimation for narrow slow fading slowly time-varying OFDM channels in mobile environments is proposed. By establishing the grey model, the channel state information of the previous time-slot is utilized to dynamically predict the unknown channel state information of the subsequent time-slot. The feasibility of grey modeling applied for estimating the channel characterized by 2-D time-frequency variables is verified by both theoretical analysis and simulations. Compared with conventional channel estimation approaches, the proposed algorithm not only performs well in narrowband slow fading slowly time-varying channels, but also has low complexity and high spectrum efficiency.
Airborne warning radar target detection tracking is a dynamic process. The model of warning radar system have been proposed by the means of UML, including use case diagram, static entity diagram, collaboration diagram and configuration diagram. The simulation system adopted message drive discrete event simulation method and a message center is considered as the message middleware of the distributed environment. Then the message flow of warning radar target detection and tracking function was designed in detail. The development results demonstrate that the design of the system structure were reasonable and the software have the advantages of high extensibility, strong cohesion, low coupling, good stability and low hardware requirements.
Combining decision and game theory, this paper studies on the model of multistage military conflict decision-making and its solving method in uncertainty environments based on hypergames. The full domain scenario set can be established through the integration of sub-scenario sets reflected enemy's beliefs in every stage. The decision maker can get the synthetic equilibrium strategies of other players on the basis of the full domain scenario set. Finally, one may determine the full game scenario through the integration of full domain scenarios and the feasible strategy of her own with risk assessment. Case study shows the proposed method can deal with uncertainty effectively. Noted that the feasible strategy is no worse than the synthetic equilibrium strategy and can be exactly better than that.
Facing the deficiency that occurs in the traditional risk assessment methods which can't consider the certainty and uncertainty of the system, this paper employed set pair theory in risk assessment of system. Based on set pair analysis same-indefinite-contrary, this paper proposes a brand-new five-element connection number comprehensive evaluation model of risk. This model expands the mathematic expression of risk assessment, which can analyzes the situation of the risk and the developing trend of the risk. And then, the model combines the static risk assessment and dynamic risk assessment.