Forthcoming articles


International Journal of Applied Decision Sciences


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International Journal of Applied Decision Sciences (9 papers in press)


Regular Issues


  • A Hierarchical SBM-Tobit Approach for Examining the Influencing Factors of Industrial CO2 Emission Efficiency in the Yangtze River Delta   Order a copy of this article
    by Jie Zhang, Mei Yang, Zhencheng Xing* 
    Abstract: As the most developed region, the Yangtze River Delta (YRD) has been one of the largest CO2 emitter in China. Chinese government has proposed the concept of YRD urban agglomeration to improve its competitiveness. To this end, we evaluate industrial CO2 emission efficiency (ICEE) of 26 cities in YRD and its space-time distribution during 2006-2015 by applying SBM-Undesirable model and the method of GIS visualization respectively. Moreover, we used Tobit model to study factors influencing ICEE. The results are as follows: (1) ICEE of YRD increases in fluctuation during our study period; (2) There is a spatial cluster in the distribution of ICEE; (3) The proportion of industrial research and development funds to regional GDP (R&D), population size (PS) and the degree of opening up to the outside world (OPEN) positively influence the ICEE, while industrial energy structure (IES) and the actual use of foreign direct investment (FDI) negatively do.
    Keywords: Yangtze River Delta; industrial CO2 emission efficiency; influencing factors; SBM-Undesirable model; Tobit regression.
    DOI: 10.1504/IJADS.2018.10011840
  • A risk-based emergency group decision method for haze disaster weather based on cumulative prospect theory   Order a copy of this article
    by Haitao Li 
    Abstract: The frequent occurrence of extreme haze episodes currently in China has caused widespread public concern. The Chinese government has developed and implemented a series of long-term measures to mitigate the serious situation. Nevertheless, some emergency response measures are also needed in the short term. Hence, a risk-based emergency group decision method for haze disaster weather based on cumulative prospect theory (CPT) with linguistic evaluation information is proposed. This method obtains and expresses group decision-makers' (DMs') evaluation information based on additional linguistic evaluation scale and its extended scale, calculates the comprehensive prospect value matrix of each haze emergency response alternative based on CPT, after that, calculates the final decision results with DMs' weights. On these bases, the best haze disaster emergency response alternative can be selected. Finally, an application case of HD city in North China is presented to illustrate the usefulness and effectiveness of the proposed method.
    Keywords: haze disaster weather; risk-based emergency group decision-making; cumulative prospect theory (CPT); linguistic evaluation information.
    DOI: 10.1504/IJADS.2018.10011984
  • Time-Cost Trade Off Resource-Constrained Project Scheduling Problem with Stochastic Duration and Time Crashing   Order a copy of this article
    by Zhe Zhang, Xuejuan Zhong 
    Abstract: In this paper, time crashing is implemented to solve the limitation of buffer insertion when consider the high variability of activities and resources in resource constrained project scheduling problem (RCPSP). The activity duration and resource are considered to be beta and exponential distributed, respectively. To settle the discrete time-cost trade off problem, a non-linear combinatorial optimization model is developed to pursuing the time, cost and robustness of the project. Six improvement principles are proposed and three balance points are discovered according to the different situations in the time-cost trade off process, in which the earliness bonus, tardiness penalty, instability cost and time crashing cost are involved. To minimize total budget of the project, tabu search and starting time criticality heuristic is designed as the solution method. Finally, a numerical example is presented to highlight the efficiency of proposed model and solution method.
    Keywords: RCPSP; time crashing; time-cost trade off; uncertainty.
    DOI: 10.1504/IJADS.2018.10011985
  • A Heuristic Algorithm Enhanced with Probability Based Incremental Learning and Local Search for Dynamic Facility Layout Problems   Order a copy of this article
    by T.G. Pradeepmon, Vinay V. Panicker, Rajagopalan Sridharan 
    Abstract: The dynamic facility layout problem (DFLP) involves finding an arrangement of facilities that minimizes the sum of material handling cost and rearrangement cost over multiple periods. In this paper, the DFLP is modelled as a multiple Quadratic Assignment Problem (QAP), one for each period. Probability based incremental learning algorithm with a pair-wise exchange local search (PBILA-PWX) is proposed for solving the QAP for each period. The proposed heuristic and 16 algorithms available in the literature are applied for solving a set of 48 benchmark instances of the DFLP. For most of the problem instances, the proposed heuristic provides better results in comparison with an existing robust algorithm. The deviations of the solutions for the proposed heuristic are found to be within 5% of the best known solutions. A case study conducted for determining the machine shop layout of a firm manufacturing printing machines is also presented.
    Keywords: quadratic assignment problem; dynamic facility layout problem; estimation of distribution algorithm; probability based incremental learning; pair-wise exchange local search.
    DOI: 10.1504/IJADS.2018.10012583
  • Resource optimisation and fault detection algorithms for cloud computing platforms based on SVM and resource reserve strategy   Order a copy of this article
    by Xilong Qu, Srikanta Patnaik 
    Abstract: Efficient operation of cloud computing platforms depends on the optimised virtual resources and faster fault diagnosis system of the virtual machines. This paper proposes an algorithm by introducing a virtual machines based on elastic reservation mechanism, which can improve the availability of cloud resources through the demand analysis taking the help of support vector machines which has advantages resolving nonlinear and high dimensional classification problems. Secondly it adopts the anomaly detection algorithm based on support vector machines for failure analysis. In addition, the dimensionality problem can be sorted out by means of principal component analysis (PCA) algorithm and a kernel function used for distance measurement. It establishes the topological structure for the image set of feature space with Delaunay triangulation and analyses the relationship between kernel parameter and regulator, in order to build an effective model.
    Keywords: virtual machine; cloud platform; support vector machine; SVM; principal component analysis; PCA; Delaunay triangulation.
    DOI: 10.1504/IJADS.2018.10011764
  • An automated data-driven tool to build artificial neural networks for predictive decision-making   Order a copy of this article
    by Chun-Kit Ngan 
    Abstract: We propose the development of an automated data-driven tool to assist data analysts in building an optimal artificial neural network (ANN) model to solve their domain-specific problems for predictive decision making. The proposed approach combines the strengths of both sequential training methods and multi-hidden-layer learning algorithms to dynamically learn the best-fitted parameters, including learning rate (LR), momentum rate (MR), number of hidden layers (NHL), and number of neurons in each hidden layer (NNHL), for the given set of key input attributes and multiple output nodes. Specifically, the contributions of this work are three-fold: 1) develop the new extended algorithm, i.e., multidimensional parameter learning (MPL), to learn the optimal ANN parameters; 2) provide the user-friendly GUI tool for data analysts to maintain the data manipulations and the tool operations; 3) conduct the experimental case study, i.e., determining the severity level of Alzheimer's patients, to present the superior result (i.e., 95.33%) in terms of prediction accuracy and model complexity by using the learned parameters (i.e., LR = 0.6, MR = 0.8, NHL = 2, NNHL at the 1st layer = 28, and NNHL at the 2nd layer = 24) from the MPL algorithm.
    Keywords: artificial neural networks; ANNs automated data-driven tool; predictive decision making; parameter learning algorithm.
    DOI: 10.1504/IJADS.2018.10010870
  • Constrains optimal propagation-based modified semi-supervised spectral clustering for large-scale data   Order a copy of this article
    by Dayu Xu, Xuyao Zhang, Jiaqi Huang, Hailin Feng 
    Abstract: We focus on the problem of high computational complexity in the clustering process of traditional spectral clustering algorithm that cannot satisfy the requirement of current large-scale data clustering applications. In this article, we establish a constrained optimal propagation based semi-supervised large-scale data clustering model. In this model, micro similarity matrix is constructed by using prior dotted pair constraint information at first. On this basis, the Gabow algorithm is exploited to extract each strongly connected component from the micro similarity matrix that is represented by its connected graph. Then, a new constrained optimisation propagation algorithm for each strongly connected component is proposed to calculate the similarity of the whole dataset. Finally, we employ the singular value decomposition and the accelerated k-means algorithm to obtain the clustering results of large-scale data. Experiments on multiple standard testing datasets show that compared with other previous research results in this field, the proposed clustering model has higher clustering accuracy and lower computation complexity, and is more suitable for large-scale data clustering applications.
    Keywords: spectral clustering; large-scale data; pairwise constraint; affinity propagation; singular value decomposition; SVD.
    DOI: 10.1504/IJADS.2018.10010706
  • Applying a hybrid BWM-VIKOR approach to supplier selection: a case study in the Iranian agricultural implements industry   Order a copy of this article
    by Armin Cheraghalipour, Mohammad Mahdi Paydar, Mostafa Hajiaghaei-Keshteli 
    Abstract: In today's economy, due to the importance of quality and quantity of the product, supplier selection plays a significant role in procurement planning of each factory. Agricultural implements industry is one of the industries included in this sensitivity. Thus, in this paper a supplier selection framework for this industry is considered. For this purpose, a strong approach, namely best worst method (BWM) along with a well-known MCDM technique with the name of VIKOR are employed. At first, the criteria with a view to the literature review and opinions of industry experts are identified. Afterward weights of the criteria are obtained by BWM and then candidate suppliers are ranked by using BWM and VIKOR. In order to check the quality of expert's inputs, the consistency tests are applied. Moreover, to investigate the robustness of the approach sensitivity analysis is considered. Finally, according to the obtained results, it is clear that proposed framework could be effective like as existing approaches for supplier selection problems. Also, agricultural managers implementing industries need simple methodologies to select the proper suppliers and improve their situation.
    Keywords: supplier selection; agricultural implements industry; best worst method; BWM; VIKOR; sensitivity analysis.
    DOI: 10.1504/IJADS.2018.10010871
  • A novel approach for mining probabilistic frequent itemsets over uncertain data streams   Order a copy of this article
    by Tianlai Li, Fangai Liu, Xinhua Wang 
    Abstract: With the growing popularity of internet of things (IoT) and pervasive computing, a large amount of uncertain data has been collected. Frequent itemsets mining has attracted much attention in database and data mining communities. Current methods exists some disadvantages, such as inaccurate, low efficiency, etc. To address this problem, we propose a novel approach, called uncertain pattern-slide window algorithm (UP-SW) is presented. In this algorithm, a new tree structure called USFP-tree is designed to save the redeveloped header table; the model of slide-window is adopted into the renewal process of mining result. The USFP-tree is structured based on dynamic array (ARRAY) and link information (LINK), as the slide-window slides, the mining result saved in USFP-tree is refreshed. The probabilistic frequent itemsets are obtained by traversing the final ARRAY of header table. Experimental results and theoretical analysis show that UP-SW has better performance than several other UP algorithms, especially on the mining efficiency and reducing the memory usage.
    Keywords: data mining; uncertain data streams; probabilistic frequent item sets; sliding windows.
    DOI: 10.1504/IJADS.2018.10010708