International Journal of Applied Decision Sciences (9 papers in press)
Electricity consumption scenario prediction based on factor analysis and least squares support vector machine optimized by fruit fly algorithm
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.
Production Portfolio Optimization under Uncertainty: Threats of Emissions Trading
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.
A hierarchical multi-criteria group decision-making method based on TOPSIS and hesitant fuzzy information
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.
Facility Management Outsourcing through Multi-Attribute Auctions
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.
Retail vs private investors: are gains and losses perceived differently?
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.
Fuzzy Cause Selecting Control Charts for Phase II Monitoring of a Two Stage Process
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.
Vehicle Routing Optimization Algorithm for Agricultural Products Logistics Distribution
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.
Short Term Traffic Flow Prediction based on Optimized Support Vector Regression
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.
A Commercial Real Estate Price Evaluation Model Based on GT-BCPSO-BP Neural Network
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.