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International Journal of Applied Decision Sciences

 

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International Journal of Applied Decision Sciences (11 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
     
  • Bipolar Fuzzy Soft Expert Set and its Application in Decision Making   Order a copy of this article
    by Yousef Al-Qudah, Nasruddin Hassan 
    Abstract: In this paper, we extend the two concepts of bipolar fuzzy sets and soft expert sets to bipolar fuzzy soft expert sets. We will define its basic theoretic operation, namely complement, union, intersection, AND and OR on bipolar fuzzy soft expert sets along with illustrative examples, and study some related properties with supporting proofs. The basic properties and relevant laws pertaining to this concept are proven. We then construct an algorithm based on this concept. Finally, we apply it to a decision-making problem to demonstrate the applicability of the proposed method. It is shown that this concept is effective in solving decision-making problems using an illustrative example.
    Keywords: bipolar fuzzy set; bipolar fuzzy soft set; soft expert set; decision making.
    DOI: 10.1504/IJADS.2017.10004211
     
  • Analytical Upper Bounds for American Exotic Currency Options with a Stochastic Skew Model   Order a copy of this article
    by Zhi-Yuan Feng, Xu-Cheng Wang 
    Abstract: On the basis that most instruments traded on options markets are American-style ones, this paper develops the analytical upper and lower bounds of American cross-currency and quanto options under the stochastic skew model proposed by Carr and Wu (2007) when domestic risk free rates are higher or lower than the foreign risk free rates. The analytical bounds derived here are not only very tight and accurate for American option pricing, but also offer a quasi-closed form solution which is able to enhance evaluation and hedging efficiency in real world markets. We also acquire the analytical solutions for European cross-currency and quanto options given by applying two separate mean-reverting square-root processes to two separate time-changed L
    Keywords: American option; Lévy processes; exchange option; quanto option; USA.
    DOI: 10.1504/IJADS.2017.10004221
     
  • Artificial neural networks based prediction of hourly horizontal solar radiation data, case study   Order a copy of this article
    by Chaba Mouna Siham, Hanini Salah, Laidi Maamar, Khaouane Latifa 
    Abstract: The aim of the present study is to predict global solar radiation (GSR) received on the horizontal surface using artificial neural network (ANN). The measured data of the year (2013) was provided by the Applied Research Unit of Ghardaia Algeria. The best results were obtained with a 7/24/1 ANN model trained with the quasi-Newton back propagation (BFGS) algorithm. The prediction accuracy for the internal and the external validation set was estimated by the 2QLOO and 2 Qext, which are equal to 0.9984, 0.9977 for ANN, with percent root mean square error (PRMSE) of 4.71% and the mean bias error (MBE) 0.021% for the internal validation and 5.60%, 0.42% for the external validation, respectively. These results show that the optimised model is robust and have a good predictive power explained by a good agreement between the measurement and prediction values of the solar radiation.
    Keywords: artificial neural network; ANN; the quasi-Newton back propagation; BFGS; global solar radiation; GSR; prediction; sensitivity analysis.
    DOI: 10.1504/IJADS.2017.10004222
     
  • Electricity consumption scenario prediction based on factor analysis and least squares support vector machine optimized by fruit fly algorithm   Order a copy of this article
    by Siwei Wei, Ting Wang 
    Abstract: Electricity consumption forecasting is the basis and premise of power grid planning through the analysis of historical electricity consumption data and related factors. For future electricity consumption accurate prediction and influencing factors analysis, we use both scenario analysis and econometric methods comprehensively. Firstly, this paper analyses the effects of GDP, population, energy consumption and many other factors of electricity consumption in depth and then extracts the key influencing factors of electricity consumption. Secondly, electricity consumption scenario prediction model is established based on factor analysis and least squares support vector machine optimised fruit fly algorithm. Thirdly, the performance of proposed model is tested through the comparison of different models and we get the forecast results for further analysis. The proposed model is proved to have a good prediction accuracy and we provide more than one research perspective about future development of electricity consumption for decision-makers by scenario analysis.
    Keywords: factor analysis; fruit fly algorithm; least squares support vector machine; LSSVM; electricity consumption prediction; scenario analysis.
    DOI: 10.1504/IJADS.2017.10004223
     
  • Production Portfolio Optimization under Uncertainty: Threats of Emissions Trading   Order a copy of this article
    by Frantisek Zapletal 
    Abstract: This study is devoted to profit margin maximization of heavy industrial companies in Europe which are under the economic and environmental pressure caused by Emissions Trading Scheme (EU ETS). The aim of the study is to use an optimization model maximizing a company's total profit margin under uncertainty related to the duty of CO2 emissions trading in order to assess possible threats and impact of emissions trading. Two main factors, which are considered to be uncertain, are the prices of the emission permits and the demand for products of the company. Since the uncertainty is involved in the model, the fuzzy optimization (level sets approach) is used to face it. The calibration of the model is done using real data set of an European steel company. Based on the results of the verification, a potential impact of emissions trading on companies, their profit and sustainability, has been discovered.
    Keywords: fuzzy optimisation; level sets approach; production portfolio; emissions trading; steel company.
    DOI: 10.1504/IJADS.2017.10004224
     
  • A hierarchical multi-criteria group decision-making method based on TOPSIS and hesitant fuzzy information   Order a copy of this article
    by Hossein Gitinavard, Mir Saman Pishvaee, Fatemeh Jalalvand 
    Abstract: This study develops a novel approach based on technique for order performance by similarity to ideal solution (TOPSIS) to solve the multi-criteria group decision-making problem using hesitant fuzzy information. Firstly, some basic concepts about the hesitant fuzzy sets are described. Then, the proposed hesitant fuzzy hierarchical TOPSIS method is elaborated. In this method, we consider the weight of each decision maker to decrease the errors. Also, decision makers could assign their preferences values to compute the separation measure obtained from the concept of weight of the strategy of the majority of criteria in the classic VIKOR method. Finally, a numerical example from the literature about the selection of new product idea from a real case study is presented to indicate the usefulness and performance of the proposed hesitant fuzzy hierarchical TOPSIS method. Comparative analysis is also provided to show the superiority of the proposed method against the fuzzy VIKOR method.
    Keywords: hesitant fuzzy set; HFS; group decision making; TOPSIS method;hierarchical structure; industrial selection problem.
    DOI: 10.1504/IJADS.2017.10004521
     
  • Facility Management Outsourcing through Multi-Attribute Auctions   Order a copy of this article
    by Alessandro Avenali, Giorgio Matteucci, Fabio Nonino, Pierfrancesco Reverberi 
    Abstract: We introduce a multi-attribute auction-based mechanism with an endogenous score as a means to innovate the procurement of facility management (FM) activities in private and public sectors. The mechanism allows the procurer to request bids on several measurable technical and economic attributes of the supply of FM services. The procurer also assigns weights to such features to signal their importance to the sellers, while the score obtained with respect to each attribute is endogenously determined on the basis of the submitted offers for the attribute. The proposed mechanism mitigates the most relevant drawbacks due to the lack of skills and of crucial information on the outsourced non-core activities, while requiring the procurer very little auction design effort. On the one hand, the mechanism can extract from suppliers valuable private technical knowledge as well as information on the supply cost. On the other hand, it saves the procurer from detailing ex ante both the score which will be assigned to any possible bid for every attribute and the exact value to require for any technical feature of the supply.
    Keywords: facility management activities; outsourcing; incomplete contracts; procurement design; multi-attribute auctions; endogenous score.
    DOI: 10.1504/IJADS.2017.10004553
     
  • Retail vs private investors: are gains and losses perceived differently?   Order a copy of this article
    by Andrea Lippi 
    Abstract: The aim of this paper is to test whether the investors initial starting capitals and the macroeconomic context may influence their gains and losses perception. To this purpose, our analysis, for the first time in the literature on the topic, considers and compares two samples of investors, private and retail, examined in two different years, 2015 and 2009. Based on the answers obtained from specifically devised questionnaires, the paper is organised in three steps of analysis to: 1) test the differences in gain and loss perception; 2) check the level of satisfaction/dissatisfaction in situations of gain and loss; 3) test the tendency to preserve the status quo. The results obtained demonstrate that private and retail investors perceive gains and losses differently, even if a convergence over the years between the two samples examined is observed. Maintaining the status quo is the main goal for private investors.
    Keywords: perception of gains; perception of losses; decision making; private investors; retail investors.
    DOI: 10.1504/IJADS.2017.10004572