International Journal of Information Technology & Decision Making

According to the literature about customer satisfaction and loyalty, it is possible to define knowledge-based system to support management decision-making in the organizations. Nevertheless, the problem as to how much the context impacts on correlation has not been investigated in the literature. This paper focuses on developing of Decision Support System (DSS) taking into account correlations among statistical factors, i.e., expert knowledge, and customers’ opinions depending on several contextual features, e.g., culture, location, in order to build context-sensitive simulation environment. The proposed work defines a general system design workflow to tailor knowledge-based DSS by using a fuzzy model to quantify correlations among variables in a given context. We explore ontologies to represent correlations among statistical factors, e.g., Calculative Commitment, Quality of Service. We apply fuzzy data analysis techniques to train fuzzy classifier on the customer’s opinions collected by survey. Finally, synergistic usage of Description Logic and Fuzzy Theory allows the implementation of a simulation environment that supports the management team to tune business strategies. The framework has been instantiated for a case study to support public administration at the University of the Salerno.

This paper introduces an alternative fuzzy clustering method that does not require fixing the number of clusters a priori and produce reliable clustering results. This newly proposed method empowers the existing Improved Artificial Fish Swarm algorithm (IAFSA) by the simulated annealing (SA) algorithm. The hybrid approach can prevent IAFSA from unexpected vibration and accelerate convergence rate in the late stage of evolution. Computer simulations are performed to compare this new method with well-known fuzzy clustering algorithms using several synthetic and real-life datasets. Our experimental results show that our newly proposed approach outperforms some other well-known existing fuzzy clustering algorithms in terms of both accuracy and robustness.

While establishing a new health facility, such as hospitals, multiple factors should be taken into consideration. Due to the variety, the impacts of these factors on location decision should be evaluated initially. Then, spatial effects of the location factors on the plane should be determined to reach an acceptable decision. These factors can be clustered under the main criteria that are competition, accessibility and environment. Attributable to the problem structure, the decision criteria can be represented with a hierarchical way, and evaluations of the priorities of the decision criteria can be done with the Analytic Hierarchy Process technique. On the other hand, spatial convenient levels of the decision criteria and suitability map can be created with GIS/spatial analysis. Integration of these two different approaches is proposed for locating a new hospital facility. By the integration methodology proposed here, a novel approach for a hospital location decision is brought to the literature. To test the integration process, 13 criteria are determined as decision criteria. Anatolia Region of Istanbul, Turkey is selected as the study area due to the high health care demand in that region. The alternative nodes are found with soil formation considerations. The nodes represented the convenient construction sites for the new facility. After reaching the ideal solution, a sensitivity analysis is conducted on AHP-GIS integration for measuring the effectiveness of the alternative scenarios. After completion of the study, it has been observed that the methodology is capable of measuring the land suitability levels and works with all factors without having any problem.

In this paper, we introduce the summing-type and multiplying-type decomposable balance function, discuss their properties in detail, and investigate the effectiveness of the balance function for the orness measure of variable weight vector and for the factor weights. These results will be used to provide some principles for selecting the balance function in decision making and support systems.

Measuring the performance of clusters characterized by the unbalancedeness and units with no correspondence in other clusters (“uncorrespondencedeness”) has not achieved the desired attention in the literature. Particularly, the operational research has been almost exclusively focused on performance evolution over time, where clusters are generally balanced and the units repeat themselves over these groups. Such analysis has been based on the Malmquist and the Hicks–Moorsteen indexes (MI and HMI), which are solely based on Shephard’s radial distance functions and do not account for all inefficiency sources. Making use of the so-called geometric distance functions (GDFs) and the GDF-based MI, we propose a generalization of the Hicks–Moorsteen index (HMI), based on targets instead of distances to the efficient frontier, allowing the introduction of all inefficiency sources in the productivity model. Moreover, we propose a Monte Carlo-based framework to achieve the pseudo-corresponding units for general cluster performance analysis. This framework is then a generalization of the conventional performance evolution over time. Then, we show that the HMI can be decomposed into economically meaningful indexes and can be rewritten as the geometric mean of the input and the output-oriented MIs. Given these conclusions and our proposed framework, the employment of the HMI to the general clusters analysis is straightforward. Other economically meaningful conclusions are also obtained in this paper.

Developing aggregation operators for interval-valued hesitant fuzzy sets (IVHFSs) is a technological task we are faced with, because they are specifically important in many problems related to the fusion of interval-valued hesitant fuzzy information. This paper develops several novel kinds of power geometric operators, which are referred to as variable power geometric operators, and extends them to interval-valued hesitant fuzzy environments. A series of generalized interval-valued hesitant fuzzy power geometric (GIVHFG) operators are also proposed to aggregate the IVHFSs to model mandatory requirements. One of the important characteristics of these operators is that objective weights of input arguments are variable with the change of a non-negative parameter. By adjusting the exact value of the parameter, the influence caused by some “false” or “biased” arguments can be reduced. We demonstrate some desirable and useful properties of the proposed aggregation operators and utilize them to develop techniques for multiple criteria group decision making with IVHFSs considering the heterogeneous opinions among individual decision makers. Furthermore, we propose an entropy weights-based fitting approach for objectively obtaining the appropriate value of the parameter. Numerical examples are provided to illustrate the effectiveness of the proposed techniques.

Recent improvements in positioning technology have led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. In common, these object sets are called object movement patterns. Due to the emergence of many different kinds of object movement patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of patterns. It is costly and time consuming to mine and manage various number of patterns, since we have to execute a large number of different pattern mining algorithms. Moreover, we have to execute these algorithms again whenever new data are added to the existing database. To address these issues, we first redefine movement patterns in the itemset context. Second, we propose a unifying approach, named GeT_Move, which uses a frequent closed itemset-based object movement pattern-mining algorithm to mine and manage different patterns. GeT_Move is developed in two versions which are GeT_Move and Incremental GeT_Move. To optimize the efficiency and to free the parameters setting, we further propose a Parameter Free Incremental GeT_Move algorithm. Comprehensive experiments are performed on real and large synthetic datasets to demonstrate the effectiveness and efficiency of our approaches.

This paper considers a multiple criteria decision-making (MCDM) problem under risk in fuzzy environment in its general form. There are m alternatives which need to be ranked on the basis of a set of n criteria. The alternatives and the criteria are evaluated based on a set of l characteristics. The entire data is presented in the form of interval valued intuitionistic fuzzy soft set of root type. In addition each criterion is assigned a subjective criterion weight based on expert’s evaluation and each characteristic is assigned a probability weight on the basis of decision maker’s knowlege and understanding of the importance of the characteristic. This problem may be called as a MCDM problem under risk in fuzzy environment in its general form. A method for ranking the alternatives using the new score functions, prospect theory and method of determining the optimum criteria weights is explained. An algorithm is developed for this purpose and its working illustrated with a suitable example.

Bonferroni mean (BM) is a very useful aggregation operator, which can consider the correlations between the aggregated arguments and the multi-valued neutrosophic set can be much more convenient to denote the incomplete, indeterminate and inconsistent information, in this paper, we applied the Bonferroni mean to the multi-valued neutrosophic set, and proposed some Bonferroni mean operators of multi-valued neutrosophic numbers (MVNNs). First, we gave some operational laws and a comparison method of MVNNs, then we presented the weighted Bonferroni mean (WBM) operator and weighted geometric Bonferroni mean (WGBM) operator. Further, we proposed the multi-valued neutrosophic weighted Bonferroni mean (MVNWBM) operator and the multi-valued neutrosophic weighted geometric Bonferroni mean (MVNWGBM) operator and some properties of them are also investigated. Finally, the decision making methods are developed based on MVNWGBM operator and MVNWBM operator, and an example about investment selection is given to illustrate the applications of the developed methods and the influence of different parameter values on the decision-making results.

Room demand estimation models are crucial in the performance of hotel revenue management systems. The advent of websites for online room booking has produced a decrease in the accuracy of prediction models due to the complex customers’ patterns. A reduction that has been particularly dramatic due to last-minute reservations. We propose the use of parsimonious models for improving room demand forecasting. The creation of the models is carried out by using a flexible methodology based on genetic algorithms whereby a wrapper-based scheme is optimized. The methodology includes not only an automated model parameter optimization but also the selection of most relevant inputs and the transformation of the skewed room demand distribution. The effectiveness of our proposal was evaluated using the historical room booking data from a hotel located at La Rioja region in northern Spain. The dataset also included sociological and meteorological information, and the list of local and regional festivities. Nine types of regression models were tuned using the optimization scheme proposed and grid search as the reference method. Models were compared showing that our proposal generated more parsimonious models, which in turn led to higher overall accuracy and better generalization performance. Finally, the applicability of the methodology was demonstrated through the creation of a six-month calendar with the estimated room demand.

As a generalization of the compositional rule of inference (CRI) algorithm and the fully implicational algorithm, the differently implicational algorithm of fuzzy inference not only inherit the advantages of the fully implicational algorithm, but also has stronger practicability. Then, the variable differently implicational algorithm was proposed to make the current differently implicational algorithms compose a united whole. In this paper, the variable differently implicational algorithm is further researched focusing on the fuzzy modus tollens (FMT) problem. The differently implicational principle for FMT is improved. Moreover, the unified solutions of the variable differently implicational algorithm for FMT are accomplished for R- and S-implications. Following that, as an important index of fuzzy inference, the continuity of this algorithm is analyzed for main R- and S-implications, in which excellent performance is obtained. Finally, its optimal solutions as well as inference examples are provided for several specific R- and S-implications.