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This paper ostablishod secondary distribution model of optimization problem, aiming at fchfl minimum logistics eosl under optimal layout of I he workshop, and anl colony-genetic hybrid algorithm is used Lo solve quadratic assignment model. The hybrid algorithm combined Lhe advantages of the anl colony algorithm with the Kinetic algorithm, using the solution of ant colony system as the initial population of the genetic algorithm, overcoming the flaws of converging slowly, falling into the local optimum of the ant colony algorithm and easy to premature convergence of the genetic algorithm, which will achieve the global optimal model. This paper regarded a machine factory workshop as an example, using MATLA1J to solve the results, which shows that: it will save 10.0% logistics fees by using the new layout of genetic ant algorithm than the original program; while it is better and faster to use the hybrid algorithm to solve the shop I.nuni optimization problem than the .nil colony algorithm nv genetic algorithm. [ABSTRACT FROM AUTHOR]

To address the uncertainty of surgery duration, this paper investigates surgery planning scheduling problem with multiple servers, which proposes chance constraints of operating rooms overtime to guarantee the surgery durations of patients is no more than the time limit of operating rooms with a high probability. A stochastic chance-constrained program is proposed to determine which operating rooms to operate, and surgeries to operating rooms allocation. Based on a finite support set of the surgery duration, this paper introduces 0-1 variables to formulate the chance constraints, and derives 0-1 integer linear program counterpart. To improve the efficiency of the model, this paper presents two classes of valid inequalities and uses the longest path algorithm to separate the second class of valid inequalities, which are implemented in a branch-and-cut framework. Computational experiments based on real-life data from hospital in Beijing are conducted to verify the algorithm performance and determine the optimal planning scheme, so as to take full utilization of healthcare resources, i.e. operating rooms. [ABSTRACT FROM AUTHOR]

With the assumption that each appointment patient up in clinic arrives on time, this paper investigates the appointment scheduling problem with no-show. An integer programming model is established with the numbers of appointment patient and probability distribution of remaining patients at each slot as optimization variables. The objective function includes the benefits of serving patients, the patient waiting time costs, and system overtime expenses. By relaxing the coupled constraints about probability distribution of remaining patients at each slot, this paper proposes a Lagrangian relaxation method to solve the model with a dynamic programming algorithm solving the relaxation problem and a sub-gradient method solving the dual problem. Numerical experiments show that the algorithm can find the optimal solution for small scale problems, and that the best solution from the proposed algorithm is better than the one in the related literature for large scale problems. [ABSTRACT FROM AUTHOR]

This paper considered the semivectorial bilevel programming problem. By using Benson's method and dual theory of linear programming, the original problem was transformed into a single level optimization problem. This paper gave a definition of partial calmness of the transformed problem. Based on this definition, this paper constructed an exact penalized problem of the semivectorial bilevel programming problem, gave its optimality condition, and proposed an algorithm to solve it. Finally, this paper gave a numerical example to show that the algorithm is feasible. [ABSTRACT FROM AUTHOR]

This paper proved that, based on the data generated by the linear dynamic causal model, the present alogrithms of causal infernce just as IC, SGS or PC alogrithm can be used to explore the true causal structure between contemporaneous variables correctly, and this result is not dependent on the assumption that the disturbance terms in the dynamic causal structure model are normally distributed. Furthermore, the paper demonstrates that the SVAR model and the linear dynamic causal model are the same data generating process under the assumption of recursive structure, and there are corresponding relations between the coefficient matrix of contemporaneous variables in the SVAR model and the causal structure of contemporaneous variables in the linear dynamic causal model which is a DAG. Therefore, We have established a theoretical basis for the method of identifying the SVAR model based on DAG. The paper has also given a necessary and sufficient condition for identifying SVAR model with the method. Futherly, the results of Monte Carlo simulation have supported all of those conclusions. [ABSTRACT FROM AUTHOR]

Technological developments and changes in relative prices lead to changes in the entire production structure. It is not easy to inversely derive the causes of changes from given changes of input-output coefficient matrix. RAS algorithm is a method to update and levelling typical input-out table. The paper attempts to expand this algorithm for the analysis model of changes in the industrial structure, and to decompose the input coefficient changes into column-specific, row-specific and cell-specific changes without setting industrial structure changes by demand-driven or supply-driven in advance. After that, quantitative analysis about changes in the industrial structure and the reasons of them in China is done in this paper. We find that while industrial structure in China is developing sophisticatedly, labor-intensive industries grow in a high rate, which reflects imbalanced regional development of Chinese economy, and the effect of internalization from industrial upgrading into regional industrial transfer. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) as a novel intelligent optimization algorithm has been used successfully in many fields, but its application to flood hazard risk assessment is a new research topic. This paper introduces the theory and flow of application of particle swarm optimization rule mining (PSO-Miner) algorithm to flood damage risk assessment. This paper selected Beijiang River Basin, China, as study area for flood damage risk assessment based on PSO-Miner algorithm and BPANN method. The results of a case study indicate that the advantages of PSO-Miner algorithm can be summarized as follows: It does not assume an implicit assumption for processing dataset and has strong robustness; it can mine very simple assessment rules; it can have a better performance than BPANN model. So the PSO-Miner algorithm provides a new approach for flood risk assessment. [ABSTRACT FROM AUTHOR]

Swarm intelligent algorithms are derived from the simulation of natural evolution or collective behavior of animals to seek solutions of complicated optimization problems by exploring and exploiting the search space efficiently and effectively. Through the analysis on the search characteristics of swarm intelligent algorithms, the concept of solution set diversity is introduced in this paper according to the infrastructural changes of solution sets in search processes. Two categories of fundamental search strategies, i.e. the diversification search and the intensification search, are then defined and their influences on the stagnation of solution sets evolution are investigated on the basis of the solution set diversity. It is proved in this paper that an intensification strategy inevitably leads candidates solution to a single solution, which is one of the main sources of stagnation; while a diversification strategy is able to reach any point of the coding space from each initial candidate solution, i.e., the whole searching space is the reachable region of the diversification strategy, but the convergence of the algorithm cannot be guaranteed. Three popular swarm intelligent algorithms, i.e. the canonical generic algorithm, the ant colony system and the discrete particle swarm optimization, are tested with a benchmark problem, and the results support the theoretical conclusions. [ABSTRACT FROM AUTHOR]

In order to improve the predictive accuracy of grey system prediction model with a stochastic oscillation sequence, this paper proposes an algorithm which can compress the amplitude and improve the smoothness of a stochastic oscillation sequence through a smoothness operator, and then deduces and establishes the prediction model of stochastic oscillation sequence. Through some examples' simulation, this paper compares simulation errors of this model with others; the results show that this novel method can evidently improve the simulative accuracy of stochastic oscillation sequence. [ABSTRACT FROM AUTHOR]

The recommendation technology based on trust relationship is applied in the field of mobile e-commerce, and the use of social trust network to realize personalized recommendation has been widely studied. The inaccurate measurement of social trust has great influence on the accuracy of recommendation system. The paper is concerned with the problem that trust mechanism is ignored the asymmetric effect brought by the distrust relationship, and then restricts the accurate measurement of social trust. A novel algorithm of recommendation is proposed which is a real-value constrained Boltzmann machine recommendation algorithm based on trust-distrust relationship (TDA-RBM), establish restricted Boltzmann machine for each user, using normalized real value analysis and training process of RBM. Construct trust-distrust supervision mechanism, trust, distrust and the trust transfer are introduced, and the trust as weights for the TDA-RBM prediction score. Finally, we experimented on Epinions datasets and the comparison experiment proves that the TDA-RBM algorithm can effectively improve the accuracy of recommendation, which shows the effectiveness and excellency of the algorithm. [ABSTRACT FROM AUTHOR]

The block allocation strategy affects the operation efficiency of the center terminal yard directly. This paper focuses on the container allocation problem during the loading and unloading process on center terminal, and establishes a dynamic container allocation optimization model to minimize the newly-added numbers of those stacked containers and allocate containers to the optimal positions. A heuristic algorithm is designed based on the characteristic of block relocation problem. Compared the optimization allocation strategy and the lowest allocation strategy, the experiments of different circumstances indicate that the optimization allocation strategy is better than the lowest allocation strategy. The data also shows that the algorithm is effective and the model is efficient in solving this problem. The experiments discuss the effects of container number, bay scales and arrival frequency for trains and trucks, which provide decision supports for the center terminal yard. [ABSTRACT FROM AUTHOR]

This paper, based on the consideration of the handling time, aimed to conduct a study on the optimization of the batches processing modes and resource scheduling for the mixed-model manufacturing system. The time models of the processing and handling for batch parts were established in order to obtain the solutions to the makespan and optimize the handling frequency. In this foundation, a principle was analyzed for determining the optimum processing sequencing. Besides, an optimization algorithm of the mutual-aid teams was also designed to schedule the handling equipments needed by the optimum processing sequencing. The results show that the study in this thesis can provide effective solutions to the makespan of batch parts, optimize the handling time and frequency, acquire the optimum processing sequencing, and schedule the resources of the handling equipments sensibly. [ABSTRACT FROM AUTHOR]

As to the applied range and optimization of grey forecast model, according to the principle that grey GM(1,1) model's parameters are grey and adjustable, the connotation model and parameter packet formulas of GM(1,1, β) were put forward in this paper, and some properties of the model and model's optimization algorithm was analyzed. The results show that the objective valuing region of parameter a of GM(1,1, β) model is (-∞+ ∞), and the objective valuing region of parameter a of GM(1,1) model is (- 2,+2), the objective valuing region of parameter a is determined by the background value coefficient, and it has nothing with raw data sequence; GM(1,1, β) grey differential equation completely suits for the homogeneous exponential function series. Finally, the example of simulating and forecasting the per capita income of urban dwellers in China shows that the grey differential equation GM(1,1, β) is valid. [ABSTRACT FROM AUTHOR]

The paper discussed characteristics of Forman theory and presented an optimization model based on discrete Morse theory, which could obtain an optimal value or approximate optimal one by the algorithm of constructing a discrete Morse function in 3-dimensional space. It is a radical attempt. The experimental results show that this model is effective for applications and especially for optimization problem of a large amount of data. [ABSTRACT FROM AUTHOR]

Identifying protein complexes from the large-scale protein interaction network is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era. Generally, the traditional protein complex discovery algorithms are only based on the protein-protein interaction network (PPI network), and are not so accurate. In this paper, a novel algorithm IPCIPG is proposed based on the integration of the PPI network and the gene expression data. Different from other previous methods which use gene expression data to evaluate the reliability of PPIs, IPCIPG integrates the gene expression data into PPI network during the identification of protein complexes. IPCIPG uses the edge clustering coefficient (ECC) and the co-expression correlation between proteins (PCC) to calculate the weight of each node in the PPI network. And then the node with the highest weight is selected as seed, then, a dense sub-graph will be obtained by extending from the seed. The experiment results on the data of Saccharomyces cerevisiae show that IPCIPG can identify the protein complexes with specific biological meaning more effectively, precisely and comprehensively than the other algorithms HUNTER, HC-PIN, CMC, SPICI, MOCDE, and MCL. [ABSTRACT FROM AUTHOR]

The integrated problem with truck scheduling and storage allocation was researched in this paper. Here, the container's arrival was dynamic and truck was used circularly, so the containers stacking position and truck scheduling had to be decided simultaneously. It was formulated as an integer programming model to minimize makespan of the whole discharging course. In the model, the truck scheduling constraints and stack crane operation constraints were also considered. Two stages tabu search algorithm was designed for solving above problem. The new method was compared with software CPLEX 11.0. The experiment demonstrates that the two stages tabu search obtains optimal solution for seven cases of total cases and the average gap of left cases is less than 5%. The experimental results show that this algorithm can solve this integrated problem effectively. [ABSTRACT FROM AUTHOR]

In order to improve the benefit and competitiveness, dynamic pricing decision-making under uncertain demand is one of the most important problems for a multimodal transport operator. Based on revenue management combined with freight organization features of container sea-rail intermodal transport, from the point of differentiated pricing of contract sale and free sale, a two-stage optimal model was established in this paper, which integrated dynamic pricing and slot allocation together on a multi-node container sea-rail intermodal transport line. The model in the first stage was proposed by considering long-term slot allocation in contract market and the empty container transportation, and the model in the second stage was proposed by considering the dynamic pricing in different booking periods of free market. According to the demand randomness in the models, the methods of chance constrained programming and robust optimization model were employed to solve the models respectively. The example shows that the models and algorithms are feasible and effective. [ABSTRACT FROM AUTHOR]

A principal-agent problem with asymmetric information is considered in this paper. Based on theories of ill-posed bilevel programming, the definition of ill-posed principal-agent problem is proposed. Then, a fuzzy interactive coordinating algorithm is presented for its optimistic formulation. Furthermore, a satisfactory contract is eventually derived for both principal and agent so that both are winners. Finally, an example is given to illustrate the rationality and the operability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In a mixed model assembly line, the demands for products are usually uncertain. When the actual demands are larger than expectation, capacity adjustments should be made. The difficulty and cost of these adjustments rely heavily on the initial balancing. To the best of knowledge, the convenience of future adjustment and corresponding cost have not been considered in literature. This paper investigates the line balancing problem in a mixed model assembly system which faces uncertain demand and makes use of overtime work to meet unexpected demands. The objective is to minimize the expected labor cost, including cost for normal operation and overtime work. A mathematical model is built and a method is proposed to estimate the cost lower bound, based on which a single pass heuristic is designed. The numerical experiment shows that the algorithm is effective and efficient. [ABSTRACT FROM AUTHOR]

This paper analyzes the features and mechanism of the B algorithm proposed by Vincent D. Blondel et al., and discusses the possible impact of nodes' property on community structure detection. Then it proposes an improved algorithm for the B algorithm by reconstructing the initial network and controlling node (community) merging process in order to obtain better modularity and the corresponding network partition. The community structure detection experiments on computer simulation networks and actual networks, show that the improved algorithm we proposed is reliable and effective, which achieves a better network partition with a larger modularity and has shorter computation time. [ABSTRACT FROM AUTHOR]

The ultimate goal of a steganography algorithm is to carry as many bits as possible without introducing any detectable artifact into the cover object. However, embedding quality and payload have an antagonistic relationship. Then, people often make a tradeoff between them depending on their different desires. In this paper, a new steganography algorithm for grayscale images with large payload and acceptable visual effect is proposed. We use a group of functions to guide cover pixel pairs' modification and imply two secret digits base-9 numeral system one time. From the experimental results, the embedding capacity of the proposed algorithm achieves 3.1699 bit per pixel, which is much larger than that of the published works, while the visual quality of stego images is acceptable too. [ABSTRACT FROM AUTHOR]

In view of the situations that the locations of high-speed rail terminals in some cities are far from the city centers, this paper put forward an approach to design feeder bus lines considering two aspects of demands. One is recent demand for transporting high-speed rail passengers between city centers and rail terminals; the other is long-term demand for directly connecting the bus system in city centers and the bus system in the emerging city unit around rail terminals. The optimization model for selecting the bus lines was built based on the two aspects of demands and then the related solution ant-colony algorithm was designed. The case of designing the feeder bus lines for anting rail terminals on Shanghai-Nanjing intercity railway was used to illustrate the model and the algorithm. The index showed the optimized feeder bus lines have the characters of quick and reliable service quality and good potential for the transition to express lines in the future. [ABSTRACT FROM AUTHOR]

The frequent occurrence of natural disasters has drawn people's attention to the aftermath work, especially the effective routing of emergency vehicles carrying relief efforts. Based on the situation where some disaster hit areas can be informed in advance while could not accept emergency service, this paper introduces the disclosure date and release date to nomadic TSP model, studies an online nomadic TSP problem based on advanced information, and gives a lower bound. When the metric space is the real line, an ENO-dd algorithm is presented. For general metric spaces, a GTR-dd algorithm is presented. Competitive analysis is given for these two algorithms respectively. The results indicate that the more advanced information, the better these two algorithms perform. More general information structure and optimal algorithm design can be the future research. [ABSTRACT FROM AUTHOR]

Presented in this paper is a new approach to establishment of the dynamics model of multidimensional Taylor network and its parameter identification, whereby a method based on the model and wavelet for financial time-series forecasting is proposed. The financial time series are decomposed into sub-series of a low frequency signal and several high frequency signals via Mallat algorithm, for each of which a multi-dimensional Taylor network model is established. Model parameters are trained by conjugate gradient method, and then the models are used for forecasting. All forecasting results are superimposed to obtain the predicted value of the original time series. As verified by our experiments, the proposed method works well in ensuring the accuracy of financial time-series forecasting. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive inference algorithm for unmanned aerial vehicle mission decision-making under uncertain environment based on structure-variable discrete dynamic Bayesian networks. The proposed algorithm can dynamically adjust the parameters of mission decision-making model by using soft/hard evidence and a priori information, and make the mission decision-making model suitable for the dynamic environment by reasoning and interactive parameter learning. The simulation results validate that the proposed algorithm can provide assurance for unmanned aerial vehicle mission decision-making under the conditions of incomplete information of abrupt threats, inaccurate or unawareness of a prior parameters. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

After analyzing the characteristics of the free pickup and delivery service of customers to airport, this paper proposed an exact algorithm based on set partition to solve the model balancing the vehicle number and customer satisfaction. The algorithm takes the customers' requirements on the pickup time and arrival time to the airport into consideration. The detour restrictions are dealt with as well. Finally, the validity and effectiveness of this algorithm is demonstrated by a series of numerical test instances. [ABSTRACT FROM AUTHOR]

Node importance evaluation is one of the important network analyses in complex network and data mining. However most of the existing methods are complex and slow for large networks. In this paper, a practical method based on resistive network for fast node importance evaluation was proposed. Node importance was evaluated based on its influence on the average power dissipation in a resistance network, a node was important because it reduced the average energy dissipation of the network. The time complexity of the algorithm is O(n³) in the worst situation, and is O(n) in the distributed computing environment. Experimental results testify that the algorithm is efficient and effective. [ABSTRACT FROM AUTHOR]

Focus on the electricity load forecasting in energy-intensive enterprise, a novel Boosting forecasting method based on geometric conversion is proposed. This method extends the AdaBoost algorithm to the regression problem. In this method, a regression problem such as load forecasting is converted to a binary classification problem through geometric operation. Then confidence-rated AdaBoost is implemented on this classification problem and an ensemble separating plane is obtained. This paper proves that such a plane is identical to a regression function for original load forecasting problem, and also proves that the proposed method has theoretical convergence. Simulation results state that compared to some popular single forecasting models, the proposed method improves the accuracy of the load forecasting in energy-intensive enterprise. [ABSTRACT FROM AUTHOR]

This paper integrated the behavioral science theory into relief supplies optimal scheduling after emergency events, and proposed that the public's psychological factors should be taken into account to make decisions about relief supplies scheduling. We depicted the public's psychological risk perception with prospect theory, defined and selected the public's psychological expectation time as a time reference point, and got out the risk perception curve and function model. We formulated a mixed integer programming model to minimize public's psychological risk perception and dissatisfaction degree of supplies. The corresponding multi-layer search algorithm was designed. Finally, the validity of this model and algorithm is verified through numerical experiment. [ABSTRACT FROM AUTHOR]

Many real-world optimization problems are both dynamic and multi-modal, which require an optimization algorithm not only to find as many as possible optima under a specific environment but also to track their trajectory over dynamic environments. To address this requirement, this paper investigates a memetic particle swarm algorithm for dynamic multi-modal optimization problems. Within the framework of the proposed algorithm, a new speciation method is employed to locate multiple peaks and an adaptive local search method is also hybridized to accelerate the exploitation of species generated by the speciation method. In addition, the re-initialization schemes are introduced into the proposed algorithm in order to further enhance its performance in dynamic multi-modal environments. Based on the moving peaks benchmark problems, experiments are carried out to investigate the performance of the memetic particle swarm algorithm in comparison with several state-of-the-art algorithms in the literature. The experimental results show the efficiency of our proposed algorithm for dynamic multi-modal optimization problems. [ABSTRACT FROM AUTHOR]

Both insurer's default risk and insured's surrender events are important factors in pricing unitized participating life insurance. In order to extend the results presented in our earlier research, we suppose that both surrender and default could happen at the end of each insurance year. In this paper, three models are constructed for pricing surrender option with insolvency. An iterative algorithm based on Monte Carlo simulation method is then implemented in order to compute them. The findings are as follows: price of unitized participating life insurance could be badly underestimated without consideration of surrender option, price of which is highly sensitive to some exogenous variables such as volatility. It was also implied that growth in default risk tends to result in growth in value of surrender option. [ABSTRACT FROM AUTHOR]

Multi-step travel time prediction is more reasonable in reality than one-step travel time prediction method, but it was done research in by few scholars. Meanwhile, the real-time travel time in future several time sections cannot be detected. In this paper, multi-step travel time estimation model and algorithm based on Kalman filtering (KF) were presented. This model took real-time and history travel time of the upper road and road itself into account. The algorithm found the nearest time in history data away from today. They can solve the problem that travel time in future are not be detected, and travel time in future several time sections can be predicted reasonably. Meanwhile, the time lag was solved effectively. Results of experiments show that the accuracy of several-step prediction algorithm is great, and it is meaningful in practical application. [ABSTRACT FROM AUTHOR]

This paper aims to explore the expanded model based on the combination of precision and grade, and mainly proposes the logical OR operation model of variable precision upper approximation operator and grade lower approximation operator. In the new model, precise descriptions and basic properties are studied; conventional and structural algorithms are proposed and analyzed, and a conclusion is drawn, that conventional and structural algorithms have the same time complexity while the structural algorithm has more advantages in space complexity; a medical example is given to illustrate the new model and the algorithms. The new model has the specific meaning related to precision and grade, and partially expands the variable precision rough set model, graded rough set model and classical rough set model; thus, corresponding properties of the previous approximation operators are further obtained. [ABSTRACT FROM AUTHOR]

Static equilibrium models of supply chain networks cannot describe the propagation process of orders, inventories and purchase prices with time from downstream members to upstream members under time-varying consumer demand. By embedding the time variable into static equilibrium models, the paper proposes a dynamic equilibrium model of supply chain networks to characterize the order flows, inventories and prices of every member in the supply chain networks. Based on historical information of orders, inventories and prices, a mathematical expression of order-making strategies in which suppliers consider lead time and inventory cost is presented. A dynamic competition equilibrium model of the supply chain networks is formulated as an equivalent variational inequality formulation. A heuristic solution algorithm is also developed to solve the proposed model. Numerical examples show that the proposed model can not only embed different order-making strategies, but also analyze various issues on the supply chain networks including bullwhip effects, and thus provides a powerful tool for investigating the temporal and spatial evolutionary behaviors of the supply chain networks. [ABSTRACT FROM AUTHOR]

Considering that it is unscientific to directly assume the states of business cycle, when the macroscopic business cycle is analyzed, this paper used the GDP data from the first quarter of 1978 to the fourth quarter as the research sample, and adopted the methods of state space model with Markov switching, EM algorithm and Monte Carlo simulation, and determined the conclusion that the state's number was 2 and dimension equaled to 3. [ABSTRACT FROM AUTHOR]

The traditional clustering methods based on the two-dimension information are not suitable for dealing with the panel data processing, in the consideration of the multiple information characteristics of panel data and according to the "absolute index", "incremental index" and "volatility index" of the panel data, this paper reconstructed the similarity measure distance function and Ward clustering algorithm, proposed an adaptive weighting clustering method for panel data. The provided algorithm not only can degenerate into the traditional "absolute distance" clustering algorithm, but also can conduct clustering prediction for the future category of panel data. Finally, examples show that this method is both effectiveness and flexibility. [ABSTRACT FROM AUTHOR]

This paper mainly deals with the analysis on mechanism modeling of GM(1,N) grey differential equations, based on the structure and characteristics of time series. By the numerical integration method a new algorithm of GM(1,N) forecasting model based on Simpson formula is proposed. Using the method of average relative error, we take empirical analysis on some time series models, and then find that the new algorithm has more accurate fitting precision than the original one. And the validity of the new algorithm is verified for some time series. The new algorithm is worth to be tried on establishing GM(1,N) forecasting model. And the reasonable application of GM(1,N) forecasting model has a practical significance. [ABSTRACT FROM AUTHOR]

To establish practical failure prediction model and facilitate its applications, this paper proposed a failure prediction Bayesian network (FPBN) modeling method based on the failure mode, effects and criticality analysis (FMECA). According to the useful failure relationships embedded in the FMECA unit, the FPBN network construction and the probability parameter computation algorithms were discussed to build the corresponding FPBN unit model. Then, the FPBN unit model of each part in the complex equipment was connected with each other to establish the integrated FPBN model of the whole system. At last, based on the FMECA knowledge of a head up display (HUD), the practical FPBN case of the electronic part was built for the actual failure prediction tasks. The case analysis results show that the FMECA knowledge embedded FPBN model, which has the advantages of uncertainty representation and quantitative analysis, can perform effectively in the failure prediction of complex equipments. [ABSTRACT FROM AUTHOR]

The underwater targets recognition is the key to the dynamic perception of the environment, fast location and visual target tracking of autonomous underwater vehicle (AUV). To eliminate the negative effects, which are brought by the particularity and complexity of imaging environment, this paper presents a rapid and accurate target recognition system for AUV to use the obtained result for the next task. Firstly, an efficient contour segmentation algorithm is given by the integrated application of some popular algorithm. Secondly, affine transformation invariants which is constant to image translation, rotation and scaling are constructed through the analysis and modification of the contour moment. Lastly, the design idea of a modified FCM clustering recognition is discussed in detail. Experiments of underwater target recognition are made on four types of actual targets prove that the recognition system can be applied to underwater object detection and have the relatively high robustness and real-time effectiveness. [ABSTRACT FROM AUTHOR]

Setup (changeover) time is needed by the processes with cleaning or changing fixtures operations, which is strongly dependent on the immediately succeeding operation. The scheduling problems in this environment are sequence-dependent. This paper focuses on the single machine scheduling problem with sequence dependent setup times to minimize the total weighted tardiness, which is NP-hard. An iterated filter-and-fan algorithm (IFF) is proposed to solve it, which searches the solution space as a manner of a branch tree. In this algorithm, when the branching moves cannot improve the root solution, a new root solution with inherited features is generated, from which the search continues to work. According to the solution characteristic of the problem, a branching move strategy with sequence segment recomposing and referenced local search is proposed to generate branch nodes. The algorithm is tested by 120 instances from literature. The results show the solutions obtained by the IFF algorithm are better or not worse than those obtained by DE and DPSO algorithm for most instances. Besides, the best solutions for 42 instances are improved by the IFF algorithm. [ABSTRACT FROM AUTHOR]

With the rapid increasing number of Web services in service network, how to discover the desired trustworthy service quickly and accurately has been a hot issue of service-oriented computing. In open network environment, the efficiency of trusted service discovery is low. Take advantage of the properties of "small-world", the paper proposed a trusted service discovery method which based on trust and recommendation relationships. Firstly, an open service network model with trust and recommendation relationships was proposed. The two relationships in the model were defined. Then, based on the model and the trust and recommendation relationships, a dynamic trusted service discovery algorithm was presented. The trusted service discovery method can reduce the searching path and search trusted services for service requester in a short time. Finally, the-results of the simulation experiences showed that this method can return satisfactory trusted services quickly and efficiency. [ABSTRACT FROM AUTHOR]

Frequent itemsets mining, which is the basic in the field of data stream mining, has been paid more and more attention by researchers. Due to the uncertainties, continuities and large amount of data streams, many mining algorithms are difficult to deal with these dynamic data streams. In this paper, hashed table and the interesting degree of association rules are introduced, where the former is used to represent the synoptic data structure and the latter is applied to incorporate attention of customers. After that, a new frequent itemsets mining algorithm named MIFS-HT(mining interesting frequent itemsets with hash table) is proposed. Comparing with lossy counting and a similar algorithm called mining frequent item sets over data streams by matrix (MISM for short), the result shows that MIFS-HT is more effective both in time and space efficiency. [ABSTRACT FROM AUTHOR]

Fuzzy C-means clustering (FCM) is a fast and effective clustering algorithm, but it doesn't consider the difference of the samples size, while there exist great difference in the sample capacities of each class, the decision of FCM will be benificial to the class with less samples. A new clustering algorithm was proposed in the paper and named as general equalization fuzzy C-means clustering (GEFCM), GEFCM modified the minimum cost function of FCM and the factor of samples size was embedded in GEFCM cost function, GEFCM weakened the disturbance of sample size difference to clustering decision. The properties of GEFCM was obtained by theoretical analysis, GFECM breaks the restriction which FCM fuzzy membership can only be the distance analytical solution. The effectiveness and robustness of GEFCM are proved through simulation experiments. [ABSTRACT FROM AUTHOR]

In order to solve the problems of slot allocation and dynamic pricing of container liner shipping under the condition of random demand, this paper established respectively the slot allocation model for customer's loaded container shipping and liner's empty container allocation and transportation in contract market as well as the dynamic pricing model in spot market by two stages. The chance constrained programming and the robust optimization algorithm were designed respectively with regard to the random characteristics of the slot allocation model and the error feature of statistic in the dynamic pricing model. The results of a case study validate that the established models and their algorithms are feasible and effective. [ABSTRACT FROM AUTHOR]

Multi-agent coalition formation is one of the key problems in multi-agent systems. The main research is how to dynamically generate task-oriented coalition and the optimal structure in multi-agent systems. For the establishment of a mechanism to organize agent stability completes the task that can not be completed by a single agent and reach the global optimum. This paper presents a multi-objective comprehensive evaluation model, and a multi-objective quantum evolutionary algorithm is proposed to solve multi-agent coalition formation problem. The algorithm uses coding mapping, the combination of resources and task allocation are combined to a process, which reducing the complexity of the problem. Experimental results show that the proposed algorithm can solve the multi-task agent coalition formation problem effectively and efficiently. [ABSTRACT FROM AUTHOR]

Based on the origin-destination-based Frank-Wolfe algorithm proposed by Anthony Chen (2002) which means the shortest-path of all OD pairs with the same origin was searched in the algorithm at one time and the link flow was updated by using one-OD-at-a-time flow update strategy, the initialization, linear search and shortest path search of ODBFW algorithm was improved in this paper, and the improved ODBFW algorithm was proposed. The improved ODBFW algorithm was used for Sioux Falls network which was common used in international similar researches and the road network of Zhucheng City, Shandong Province, China to compare the feasibility, efficiency and stability of the standard FW algorithm, ODBFW algorithm and improved ODBFW algorithm. The results indicated that the improved ODBFW algorithm has more efficiency and stability than standard FW algorithm. The time used for the improved ODBFW algorithm to reach convergence is nearly 15% less than the time used for the original algorithm in small road network, and is nearly 5% less than that in large road network. [ABSTRACT FROM AUTHOR]

This paper considers a problem of uniform-machine scheduling with machine release times to minimize makespan. Firstly, four properties of swapping jobs are proposed. Then, based on the properties, a heuristic algorithm is presented, which takes the LPT schedule as its initial solution and improves the solution by repeatedly swapping the jobs between two machines with the maximal and minimal makespan. Finally, computational results show that the presented heuristic is efficient and effective. [ABSTRACT FROM AUTHOR]

Vehicle routing problem (VRP) plays a vital role in logistics research. The multi-depot vehicle routing problem with time windows (MDVRPTW), an extension of VRP with time windows (VRPTW), is very complicated to be solved. This paper presented a two-stage heuristic to solve MDVRPTW. In the two-stage heuristic, aggregation-based clustering algorithm was firstly proposed to transfer MDVRPTW to several VRPTWs, and ant colony optimization (ACO) was developed to optimize each VRPTW. Two improvement strategies: crossover operation and adaptive ant-weight strategy, are designed to improve ACO. The performance of the two-stage heuristic was examined by use of some classic instances. Results show that the two-stage heuristic is a powerful tool for MDVRPTW. [ABSTRACT FROM AUTHOR]

Timed influence net (TIN) is a powerful formalism to model uncertain causal relationship, and has been widely used in effectiveness evaluation of military operation alternatives. Stochastic timed influence net (STIN) is proposed to enhance TIN formalism by relaxing the constant delay assumption. Two key parameters, stochastic delay and stochastic belief sequence, are introduced in STIN to evaluate the effectiveness of alternatives under uncertain delays in the generation and propagation of operational effect. The mathematical description of and belief propagation algorithm in STIN is discussed in detail. This paper presents an example to illustrate the modeling process of STIN and the effectiveness of the belief propagation algorithm. [ABSTRACT FROM AUTHOR]