Forthcoming articles


International Journal of Applied Decision Sciences


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International Journal of Applied Decision Sciences (10 papers in press)


Regular Issues


  • A Novel Selection Method of Network Intrusion Optimal Route Detection based on Naive Bayesian   Order a copy of this article
    by Yu Nuo 
    Abstract: In order to improve the network security performance and resist the increasingly complex and diversified network intrusion, and reduce the false alarm rate of network intrusion and improve the detection efficiency, this paper proposes the selection method of the network intrusion optimal route detection based on Naive Bayesian. We selected the feature subset of network route data by the principal component analysis and accordingly processed the network route detection sample set, getting the input characteristics of network route detection. The research selected the new low dimensional feature of network route data through linear or nonlinear transformation, and used the Naive Bayesian network structure to classify the new network route data set. Simulation results show that the proposed method can improve the detection rate of network intrusion optimal route and reduce the false alarm rate, getting a more perfect result of network intrusion detection.
    Keywords: network intrusion detection; principal component analysis; PCA; normalisation; optimal route of network intrusion.
    DOI: 10.1504/IJADS.2018.10007261
  • Reversion Strategy for Online Portfolio Selection with Transaction Costs   Order a copy of this article
    by Xingyu Yang, Huaping Li, Yong Zhang, Jin'an He 
    Abstract: We concern the online portfolio selection problem with transaction costs, which is an unavoidable factor in real financial trading. By exploiting the mean reversion property of stock prices, we propose a portfolio selection strategy named ``Mean Reversion strategy with Transaction Costs (MRTC)''. To avoid overmuch transaction costs, the strategy adaptively transfers a proper amount of capital between stocks to adjust the turnover. Furthermore, we conduct numerical experiments on several real market datasets, and show that our proposed algorithm outperforms the existing state-of-the-art ones when taking transaction costs into account.
    Keywords: Online Portfolio Selection; Investment Strategy; Mean Reversion; Transaction Costs.
    DOI: 10.1504/IJADS.2018.10007603
  • Solving group decision-making problems in manufacturing systems by an uncertain compromise ranking method   Order a copy of this article
    by S.M. Mousavi, N. Foroozesh, H. Gitinavard, Behnam Vahdani 
    Abstract: The purpose of this paper is to introduce a new modified compromise ranking method (VIKOR) known as sorting the possible alternatives and determining the compromise solution under interval-valued hesitant fuzzy sets (IVHFSs) for solving group decision-making problems in manufacturing systems. Proposed interval-valued hesitant fuzzy modified VIKOR (IVHF-MVIKOR) method utilizes the membership degrees to demonstrate the degrees of satisfiability for each possible alternative according to selected criteria for the manufacturing assessments. Then, a new index for relative importance weight of the DMs is introduced with the extended fuzzy sets. Also, some operations in the proposed IVHF-MVIKOR method are developed for manufacturing decision problems. Then, new indexes are presented with interval-valued hesitant fuzzy hamming distance measure for the purpose of rankings. Finally, by presenting a practical example in flexible manufacturing systems, the performance of IVHF-MVIKOR method is evaluated and compared with two well-known methods under interval-valued hesitant fuzzy information.
    Keywords: Interval-valued hesitant fuzzy sets (IVHFSs); group decision-making problems; manufacturing systems; modified VIKOR method; interval-valued hesitant fuzzy hamming distance measure.
    DOI: 10.1504/IJADS.2018.10007845
  • Approach for three-parameter interval grey linguistic variable decision-making based on fuzzy integral and Mobius transformation   Order a copy of this article
    by Baojun Sun, Cunbin Li, Jiahang Yuan 
    Abstract: This paper presents the concept of three-parameter interval grey linguistic variable, which combines three-parameter interval grey numbers with grey linguistic variables. Firstly, basic definitions and new laws are given. Secondly, considering the attributes interdependency, this paper utilises the fuzzy integral theory to construct a grey fuzzy integral correlation degree decision-making model under three-parameter interval grey linguistic variable environment. To solve this model, the Mobius transformation coefficients based on weights and interaction degrees are defined to calculate two-order additive fuzzy measures. Then the maximum projection models are used to calculate the weights, and the interaction relations and interaction degrees are determined by decision makers. Subsequently, the grey fuzzy integral correlation degrees of alternatives are calculated by precise function transformation, grey correlation degree and fuzzy measures. At last, an illustrative example has been taken to demonstrate the validity and feasibility of the proposed method.
    Keywords: decision-making; three-parameter interval grey linguistic variable; fuzzy integral; Mobius transformation.
    DOI: 10.1504/IJADS.2017.10006579
  • Short-term traffic flow prediction based on optimised support vector regression   Order a copy of this article
    by Yang Xu, Da-wei Hu, Bing Su 
    Abstract: In order to provide accurate and reliable prediction of short-term traffic flow to realise intelligent transportation control, support vector machine (SVM) regression method is established to predict short-term traffic flow. Then, parameter selection optimisation model for SVM is studied. Support vector penalty coefficient and the parameters of the kernel function play an important role in learning precision and generalisation ability of regression model. So, a kind of improved artificial fish swarm algorithm is used to optimise the SVM regression to select the optimal parameters. The experiment results show that the proposed scheme can effectively reduce mean absolute percentage error and mean square error in the real traffic flow forecasting. The proposed scheme can improve the prediction precision of the short-term traffic flow.
    Keywords: short-term traffic flow prediction; support vector machine regression; artificial fish; accuracy.
    DOI: 10.1504/IJADS.2017.10005300
  • Hawk-dove game study on green railway alignment selection   Order a copy of this article
    by Guangquan Zhou, Xiaoping Wu 
    Abstract: In this study, we set asymmetric logical frames to simulate the collaborative process, and assume that the two given environmental factors are players. Given the equal strength of the players in the natural state, it is clear that the classical hawk-dove game model does not consider the strength of both sides. Hence, we continue to develop models based on the classical model, considering the effects of asymmetric interaction. Similarly, in model analysis, we introduce value engineering theory to simplify the model. The suitability of the model design is verified through computer simulations. Simulation graphics show that the probability of cooperation between two sides increases with the difference in strength between the two sides. In addition, the lower the unit value of conflict, the higher is the probability of cooperation. In this strategic direction, we can avoid the application and disoriented development of a wide variety of technology strategies.
    Keywords: green railway alignment selection; asymmetry evolution; hawk-dove game model; cooperation.
    DOI: 10.1504/IJADS.2017.10006716
  • Vehicle routing optimisation algorithm for agricultural products logistics distribution   Order a copy of this article
    by Mingqi Sun, Dezhi Pang 
    Abstract: This paper aims to handle the problem of vehicle routing optimisation in agricultural products logistics distribution. The vehicle routing optimisation problem is converted to a graph model calculation problem and then the node set of the graph contain depots and customers. The vehicle routing optimisation is to seek an optimal one from all possible paths which consumes least fuels. The main innovation of this paper is to introduce the ant colony algorithm in the vehicle route optimisation problem. In vehicle routing, each ant starts from the depot and goes through several customers and then goes back to the starting point. Furthermore, customers are determined with the pheromone information and multiple pheromone information matrixes are built up. Finally, experimental results demonstrate that our proposed algorithm can significantly reduce fuel cost in agricultural products logistics distribution.
    Keywords: vehicle routing optimisation; agricultural products; logistics distribution; ant colony algorithm.
    DOI: 10.1504/IJADS.2017.10005299
  • A commercial real estate price evaluation model based on GT-BCPSO-BP neural network   Order a copy of this article
    by Yongbo Liu 
    Abstract: Aimed at coping with the complexity of commercial real estate price evaluation, the advantages of grey correlation theory, bacterial chemotaxis particle swarm algorithm and BP neural network are integrated to firstly put forward a novel model of commercial real estate cost evaluation. First, grey correlation theory was used to reduce the factors affecting commercial real estate price and optimise input variables of BP neural network. Then, the bacterial chemotaxis particle swarm algorithm with constriction factors is adopted to optimise the initial weights and thresholds. Through this method, BP neural network can be used to solve nonlinear problems and to improve the rate of convergence and the ability to search global optimum. An engineering project in the city of Hunan is selected to make empirical analysis. It shows that this novel model enjoys a high practical value as it can be applied to make scientific evaluation of commercial real estate price evaluation.
    Keywords: commercial real estate; cost evaluation; grey correlation theory; bacterial chemotaxis particle swarm optimisation; BCPSO; artificial neural networks.
    DOI: 10.1504/IJADS.2017.10005301
  • Fuzzy cause selecting control charts for phase II monitoring of a two stage process   Order a copy of this article
    by Peyman Soleymani, Amirhossein Amiri 
    Abstract: In this paper, it is assumed that there is a two-stage process in which the quality characteristic of the second stage is represented by fuzzy number which is monitored to detect shifts in the process. Also due to the existence of cascade property in a two-stage process, the quality characteristic of the second stage is affected by the quality characteristic in the first stage. Using fuzzy random variable which includes two kinds of uncertainty randomness and fuzziness simultaneously is considered. We proposed fuzzy Shewhart cause-selecting control chart and fuzzy exponentially weighted moving average (EWMA) cause-selecting control chart to detect different magnitudes of shift in the process parameters in phase II analysis. The performance of the proposed methods is evaluated by simulation in terms of average run length (ARL) criterion. Finally, a numerical example is given to show the application of the proposed methods step by step.
    Keywords: phase II analysis; fuzzy cause-selecting control chart; fuzzy multistage process; fuzzy cascade property.
    DOI: 10.1504/IJADS.2017.10005298
  • Wind turbine generator selection and comprehensive evaluation based on BPNN optimised by PSO   Order a copy of this article
    by Wei Sun, Zhipeng Xu 
    Abstract: With the development of the electric power system in China, wind power, as a clean energy, can be utilised to optimise the structure of electrical energy. By reducing the emission of pollutants, it will benefit the sustainable development of the national economy and environment. In wind power projects, scientific and rational choices for the wind turbine generator in actual wind farm are critical since it is directly related to the economic benefits of wind power projects. By analysing the status of current wind power capacity at the scale of the globe and China, wind power is projected to play an increasingly important role in the future. On this basis, we developed the comprehensive evaluation system of wind turbine generator selection and established a comprehensive evaluation model based on BP neural network which was optimised by particle swarm. A real example was employed to verify the validity of the proposed method, thus can provide guideline of the evaluation of the wind turbine generators selection in wind farms.
    Keywords: wind turbine generators selection; comprehensive evaluation; BP neural network; particle swarm optimisation; parameter optimisation.
    DOI: 10.1504/IJADS.2017.10006786