Forthcoming articles

 


International Journal of Applied Decision Sciences

 

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International Journal of Applied Decision Sciences (4 papers in press)

 

Regular Issues

 

  • 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
     
  • Vehicle Routing Optimization 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 optimization in agricultural products logistics distribution. The vehicle routing optimization problem is converted to a graph model calculation problem, and then the node set of the graph contain depots and customers. The vehicle routing optimization 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 optimization 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 optimization; agricultural products; logistics distribution; ant colony algorithm.
    DOI: 10.1504/IJADS.2017.10005299
     
  • Short Term Traffic Flow Prediction based on Optimized 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 realize intelligent transportation control, support vector machine regression method is established to predict short term traffic flow. Then, parameter selection optimization model for support vector machine is studied. Support vector penalty coefficient and the parameters of the kernel function play an important role in learning precision and generalization ability of regression model. So, a kind of improved artificial fish swarm algorithm is used to optimize the support vector machine 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 improves 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
     
  • 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 gray correlation theory, bacterial chemotaxis particle swarm algorithm and BP neural network are integrated to firtly put forward a novel model of commercial real estate cost evaluation. First, gray correlation theory was used to reduce the factors affecting commercial real estate price and optimize input variables of BP neural network. Then, the bacterial chemotaxis particle swarm algorithm with constriction factors is adopted to optimize 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 a 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; gray correlation theory; bacterial chemotaxis particle swarm optimization; BCPSO; artificial neural networks.
    DOI: 10.1504/IJADS.2017.10005301