International Journal of Information Technology & Decision Making

Researchers work on facility location problems in different structures and solve them by developing different models over the years. In this study, we focus on the automated teller machine (ATM) site selection problem for a public bank in İstanbul. Twenty-five districts, which are located at the European side of İstanbul, are designated as ATM candidate districts. The balanced scorecard (BSC) model is modified to determine the criteria according to the literature review and experts’ judgments for this problem. Internal, Customer, and Financial perspectives of the traditional BSC model are used as the main criteria and an external perspective is considered as the fourth criterion for modified balanced scorecard. The experts’ criteria evaluations are gained and consolidated by the Modified Delphi Method. The criteria weights are determined by the Pythagorean Fuzzy-AHP method using the final pairwise comparison results obtained from the Modified Delphi Method. Subsequently, the Pythagorean Fuzzy TOPSIS method is applied to determine the best alternative site among alternatives to locate the ATM according to these criteria. Finally, the results are presented and discussed by sensitivity analysis.

Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation system (Iter_ALS Iterative Alternate Least Square) to enhance the recommendation accuracy by integrating rating and emotion tags. The rating score reveals overall perception of the item and emotion tags reflects user’s feelings. In the absence of emotional tags, scores found in rating is assumed as positive or negative emotional tag score. Lexicon based semantic analysis on emotion tags value is adopted to represent the exclusive value of tag. Unified value is represented into Iter_ALS model to reduce the sparsity problem. In addition, this method handles opinion bias between ratings and tags. Experiments were tested and verified using a benchmark project of MovieLens dataset. Initially this model was tested with different sparsity levels varied between 0%-100 percent and the results obtained from the experiments shows the proposed method outperforms with baseline methods. Further tests were conducted to authenticate how it handles opinion bias by users before recommending the item. The proposed method is more capable to be adopted in many real world applications

In this study, to enhance flexibility and agility, a special DSS is developed for a system re-design problem, while a decision-making technique is employed afterwards to enable the accelerated and reliable results, as a progressive approach to close a gap of related literature. Proposed approach generates all possible machine schedules regarding some tangible traditional constraints, after that, generated schedules are ranked by ORESTE method regarding human related linguistic variables and ergonomic factors this time, with the aim of harvesting the best outcome. Thus, all traditional and newly introduced problem constraints could be optimized in the best schedule, while time and effort sources of organisation are preserved by employment of proposed DSS, mathematical modelling, and ORESTE. A numerical application of proposed approach is executed and mechanism of proposed novel model is explained in detail as well as usability and salutary of the approach is proved with real life data.

The main goal of this study is to investigate whether social media, as a recent communication channel, has an impact on customer lifetime value (CLV). No studies have been done in Turkey with similar purposes in the telecommunication sector. To reach this goal, there has been an attempt to develop both artificial neural network models and sector-specific applicable models. Four years of data between 2011 and 2014 belonging to customers in the telecommunication sector who have a Twitter account are used in this study. The CLV is modeled through radial basis function (RBF), multilayer perceptron (MLP), and Elman neural network approaches, and the performance of such models is compared. According to the findings, calculated CLV error values are at an acceptable range in all formed models. Additionally, it is determined that the CLV was calculated with a lower error value in models where social media variables were used. The Elman neural network is determined to perform better compared to RBF and MLP.

Socially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provides valuable information, such as which locations are frequently visited by a social media user, which locations are common for a social media user group, and which locations are socially important for a group of urban area residents. However, discovering socially important locations is challenging due to huge volume, velocity, and variety of social media datasets, inefficiency of current interest measures and algorithms on social media big datasets, and the need of massive spatial and temporal calculations for spatial social media analyses. In contrast, cloud computing provides infrastructure and platforms to scale compute-intensive jobs. In the literature, limited number of studies related to socially important locations discovery takes into account cloud computing systems to scale increasing dataset size and to handle massive calculations. This study proposes a cloud-based socially important locations discovery algorithm of Cloud SS-ILM to handle volume and variety of social media big datasets. In particular, in this study, we used Apache Hadoop framework and Hadoop MapReduce programming model to scale dataset size and handle massive spatial and temporal calculations. The performance evaluation of the proposed algorithm is conducted on a cloud computing environment using Turkey Twitter social media big dataset. The experimental results show that using cloud computing systems for socially important locations discovery provide much faster discovery of results than classical algorithms. Moreover, the results show that it is necessary to use cloud computing systems for analyzing social media big datasets that could not be handled with traditional stand-alone computer systems. The proposed Cloud SS-ILM algorithm could be applied on many application areas, such as targeted advertisement of businesses, social media utilization of cities for city planners and local governments, and handling emergency situations.

In this paper, we propose a multi-attribute group decision-making (MAGDM) method based on Dempster–Shafer Evidence Theory (DST) and linguistic intuitionistic fuzzy numbers (LIFNs), in which both the expert weights and attribute weights are unknown. Firstly, we represent LIFNs as basic probability assignments (BPAs) by DST based on linguistic scale function (LSF), and a linear programming model is proposed to combine the objective weights and subjective weights of attributes to obtain the combined weights. At the same time, the experts’ weights are obtained through Jousselme distance. Secondly, we use the weights to correct the evidence, and the comprehensive evaluation value of each alternative is calculated by the combination rule of evidence. Further, a new MAGDM approach with DST and LIFNs is presented. Finally, we give an example to explain the proposed method and compare it with other methods to show the feasibility and superiority.

Increased global competition has led to a slowdown in Taiwan’s domestic semiconductor industry growth, which has resulted in many semiconductor companies reducing their investments and or seeking mergers and acquisitions (M & As) to increase market power, expand their business territories or increase their competitive edge. However, as there is general uncertainty regarding the efficiencies to be gained from these M & As, there has been an increase in M & A supervision. While past research has explored company operations and management efficiency after mergers, there has been less focus on potential mergers. Therefore, this study used a resample slacks-based measure (RSBM) and merger potential gains models to evaluate potential merger efficiency gains. Data on 29 Taiwanese-listed integrated circuit (IC) design industry firms were collected to evaluate the efficiency of potential M & As, from which it was found that the potential M & As efficiencies had positive and negative values, indicating that efficiency gains were not guaranteed. A positive value was found for a potential M & A between MTK & NOVATEK and MTK & DAVICOM, which meant that a potential M & A would increase operating efficiencies and reduce costs.

The digital age has added a new term to the literature of information and computer sciences called as the big data in recent years. Because of the individual properties of the newly introduced term, the definitions of the data-intensive problems including optimization problems have been substantially changed and investigations about the solving capabilities of the existing techniques and then developing their specialized variants for big data optimizations have become important research topic. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging characteristics of the real honey bees is one of the most successful swarm intelligence-based metaheuristics. In this study, a new ABC algorithm-based technique that is named source-linked ABC (slinkABC) was proposed by considering the properties of the optimization problems related with the big data. The slinkABC algorithm was tested on the big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies were compared with the different variants of the ABC algorithm including gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC), quick ABC (qABC) and modified gbest-guided ABC (MGABC) algorithms. In addition to these, the results of the proposed ABC algorithm were also compared with the results of the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Phase-Based Optimization (PBO) algorithm and Particle Swarm Optimization (PSO) algorithm-based approaches. From the experimental studies, it was understood that the ABC algorithm modified by considering the unique properties of the big data optimization problems as in the slinkABC produces better solutions for most of the tested instances compared to the mentioned optimization techniques.

This paper presents the ordered weighted average weighted average inflation (OWAWAI) and some extensions using induced and heavy aggregation operators and presents the generalized operators and some of their families. The main advantage of these new formulations is that they can use two different sets of weighting vectors and generate new scenarios based on the reordering of the arguments with the weights. With this idea, it is possible to generate new approaches that under- or overestimate the results according to the knowledge and expertise of the decision-maker. The work presents an application of these new approaches in the analysis of the inflation in Chile, Colombia, and Argentina during 2017.

The use of web-based education and e-learning environments has increased with the developments in educational technology. Schools, universities, public institutions, and other private sector companies started deploying these systems to train their students, members, and employees. Exams are carried out during the evaluation process of these trainings. Web-based tests are sometimes used for these exams. When there are so many questions about the same topic, it is a time-consuming and difficult problem to prepare these exams in terms of the best quality, quickly and effectively. In order to overcome this issue, artificial intelligence techniques are utilized as well as conventional methods for producing test papers. In this study, an Intelligent Question Evaluation and Selection Software (I-QUESS), that enables the selection of questions according to desired preferences by using Fuzzy Analytic Hierarchy Process (FAHP) and genetic algorithm (GA) as hybrid, was developed. This proposed hybrid system was used in a case study to create test sheet for web-based environments.