Forthcoming articles

 


International Journal of Applied Decision Sciences

 

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International Journal of Applied Decision Sciences (3 papers in press)

 

Regular Issues

 

  • Impacts of using relative weights in multiple criteria decision making: a comparative study between independent- and overlapping-criteria decision problems   Order a copy of this article
    by Panitas Sureeyatanapas, Supachai Pathumnakul 
    Abstract: Multiple Criteria Decision Analysis (MCDA) methods have been widely employed in real-life decisions. An assumption generally seen is that each criterion plays a role in determining the result according to its relative weight. However, signs of imprecision in relative weights have been implied in the literature, and this may indicate that they do not always provide intuitive and satisfactory conclusions. Most MCDA methods, furthermore, were performed regardless an existence of overlap among criteria although the decision theory reports the risk of obtaining a misleading conclusion from this. Due to the lack of empirical study to demonstrate such issues, this study investigates and supports the proposition that the relative weights do not precisely reflect actual contributions of decision criteria particularly when overlaps among criteria exist. Moreover, the use of the weights in such situation, through typical additive and multiplicative aggregation methods, is likely to generate a counter-intuitive or unsatisfactory conclusion.
    Keywords: multiple criteria decision analysis; MCDA; relative weight; decision making; overlap among criteria; criteria weights.
    DOI: 10.1504/IJADS.2017.10002663
     
  • Stock price prediction based on chaotic hybrid particle swarm optimization-RBF neural network   Order a copy of this article
    by Sainan Wang, Luda Wang, Shouping Gao, Zhi Bai 
    Abstract: The stock market is an important part of the capital market, which plays a significant role in optimizing capital allocation, financing and increasing the value of assets and other areas. Hence, the correct model for estimating and predicting the stock price has a very important practical significance to provide investors with investment decision reference. In this paper, a novel chaotic hybrid PSO-based RBF neural network model (CHPSO-RBFNN) has been proposed for forecasting the stock price, which can effectively prevent the RBF neural network from the local minimum trap and provide great learning ability. The presented methodology was tested with stock 601998, and the results showed that CHPSO-RBFNN can improve the prediction of accuracy and a high efficient and accurate stock prediction model compared to the traditional RBFNN and PSO-RBFNN methods.
    Keywords: Stock price; RBF neural network; optimization; chaotic hybrid particle swarm algorithm.
    DOI: 10.1504/IJADS.2017.10003822
     
  • The OD matrix estimation model of passenger flow based on the POI around the bus station   Order a copy of this article
    by Jia Wang, Yang-Lingzhi Yang, Bo Zhou, Srikanta Patnaik 
    Abstract: In this paper, an optimised model that is based on the existing flow of bus passengers OD estimation model is established sufficiently. First of all, by analysing the types and quantities related to the point of interest around the bus station, the intensity of land utilisation surrounding the bus station is identified, and then combining this factor with the principle that the probability of the number of travel stations obeys the characteristics of Poisson distribution, a new improved flow of bus passengers OD estimation model is set up eventually. At last, comparing the estimation results of OD from the former and the improved model through the example test, the result shows that the improved flow of bus passengers OD estimation model is more accurate than before, so that this new model can provide more reliable and practical data support for optimisation of public transit line network.
    Keywords: bus station; point of interest; POI; passenger flow; OD matrix.
    DOI: 10.1504/IJADS.2017.10004135