International Journal of Information and Decision Sciences (23 papers in press)
by Duangruthai Voramontri, Leslie Klieb
Abstract: The goal of this paper is to research empirically the role of social media in consumers decision-making process for complex purchases - those characterized by significant brand differences, high consumer involvement and risk, and which are expensive and infrequent. The model uses the information search, alternative evaluation, and purchase decision stages from the classical EBM model. A quantitative survey investigates up to what degree experiences are altered by the use of social media. Results show that social media usage influences consumer satisfaction in the stages of information search and alternative evaluation, with satisfaction getting amplified as the consumer moves along the process towards the final purchase decision and post-purchase evaluation. The research was done among internet-savvy consumers in South-East Asia, and only considered purchases that were actually made by consumers, not including searches that were abandoned.
Keywords: social media; consumer decision-making; EBM model; EKB; information search; complex purchase.
Population Size vs. Number of Crimes - Is the Relationship Superlinear?
by Yu Sang Chang, SungSup Brian Choi, Jinsoo Lee, Won Chang Jin
Abstract: Do large cities suffer from an even greater incidence of crime? According to the Urban Scaling Theory, the number of crimes committed may follow a superlinear relationship as a function of the population size of city. For example, if the population size increases by 100%, the incidence of crime may increase by 120%. We analyzed a total of 11 types of crimes which had occurred in about 250 cities with more than 100,000 inhabitants in the United States during the period of 1995-2010. We found that the relationship between the number of crimes counts and the population size of cities have followed a superlinear power function without exception in all 176 cases. However, significant variations exist among the superlinear relations by types of crime. We also found that the values of scale exponents display time-invariant pattern during the 16-year period.
Keywords: Crime counts; Population size of city; Superlinear relationship; Time-invariant distribution; Sublinear relationship.
DEVELOPMENT OF DECISION SUPPORT SYSTEMS THROUGH THE CONTRADICTIONS OF INFORMATIONAL SOCIETY
by Andrey Larionov
Abstract: In this article authors have investigated the development of existing methods and computer applications used to support decision-making at different levels of a corporate management to set up controlling systems. An increased number of challenges when doing business and inadequate management reaction leads to accumulation of contradictions and issues which could be solved by using Decision Support Systems (DSS) - the set of tools that support the process of decision making. Development of the informational systems and applications, globalization of economy, and organizational maturity create background to new tools and methods used by DSS. Informational society sets up pre-requisites to the learning module incorporated into DSS structure to help to identify competency of the user, give recommendation to the more advanced use of it and allow to share experience collected by other users. Authors have presented original classification of the DSS types and stated paradigm of DSS development in informational society in the future.
Keywords: Operational decisions; Tactical decisions; Strategic decisions; Decision maker; DSS; thinking processes; settlement systems; diagnostic systems; monitoring systems; DSS of calculation; DSS Settlement; diagnostic DSS; Approximate reasoning (or expert) DSS; explicit knowledge; implicit knowledge.
A data fusion approach for business partners selection
by Leonilde Varela, António Castro, Rita Ribeiro
Abstract: Strategies of market diversification push companies to provide novel products and services to customers, belonging to new geographic and demographic segments. Additionally, market development strategies targeting non-buying customers in selected segments or new buyers in new segments may be paired with increased product diversification and improved business agility. To fulfill the requirements associated with manufacturing, a wider range of products and increased customized demands imply having a wider set of competences available. Most companies find it increasingly difficult to have all required competencies in their internal structures; therefore, they need to rely on strategic business partnerships and suppliers to be successful. In this paper, we discuss a data fusion decision approach for supplier and business partner evaluation, which includes the past, current and forecast information about business partners. This approach may prove vital for companies to establish strong collaborative business networks.
Keywords: Collaborative Networks; Supplier evaluation; business strategies; dynamic multicriteria.
A comparison of Generalized Maximum Entropy and Ordinary Least Square
by G.R. Mohtashami Borzadaran, M. Sanei Tabass
Abstract: The Generalized Maximum Entropy (GME) estimation method is based on the classic maximum entropy approach of Jaynes (1957). It has the ability to estimate the parameters of a regression model without imposing any constraints on the probability distribution of errors and it is robust even when we have ill-posed problems. In this paper, we simulate two sets of data from regression model with different distribution for disturbance, standard normal and Cauchy distributions respectively. For this data set, regression coefficients are obtained by GME and OLS methods and these techniques are compared with each other for some sample sizes. Moreover, we have used some prior information on parameters to obtain GME estimators. The estimation results of GME in the case of non-normal distribute, are discussed here.
Keywords: Regression model; Generalized Maximum Entropy; Monte Carlo experiment; Ordinary Least Square.
Fuzzy Rule Base Optimization using Genetic Algorithm for Mobile Web Page Adaptation
by Neetu Narwal
Abstract: There is a global rise in use of mobile devices like mobile phones, PDA, palmtop etc. for web browsing. Web page usually includes the scrolling that makes web browsing time-consuming. In this work we used genetic algorithm based fuzzy inference system and utilized the power of genetic algorithm to optimize the fuzzy rules base web content classification. The content of the web page is partitioned into blocks and applies the genetic based fuzzy inference system to discriminate the main block. The filtered main blocks are then reorganized on the device. As a result of our approach the mobile web user is presented with the filtered web page content without noise which results in persistent content, fast accessing, and better utilization of limited space. We implemented the system and result shows that the hybrid genetic based fuzzy inference system provides better classification accuracy (93.57%) as compared with Fuzzy Inference System (78.55%) accuracy of classification.
Keywords: Genetic Algorithm; Fuzzy Inference System; Web Page Visual Blocks.
The impact of using new significant reference point with TOPSIS methods: study and application
by Zhor CHERGUI
Abstract: In this paper, the impact of Pareto optimality concept on revised TOPSIS method is studied. In particular, we study theoretically the cases in which a preference relation changes when delimiting the choice of the best alternative(s) in an efficient restrictive area. In order to define the most reliable approach a comparative study is established. On this basis, an accurate new method called TOPSIS Nadir is introduced. Furthermore, an adaptation for Interval data area is carried out in which we discuss some forms of normalization. By following the same steps of the TOPSIS methods for Group Decision Makers, we develop and compare two new procedures.
Keywords: Group Decision Makers; TOPSIS methods; Reference points; Crisp data & Interval data; Forms of normalization.
Fuzzy classification as a decision making problem in hesitant environments
by Mahdi Ranjbar, Ali Vahidian Kamyad, Sohrab Effati
Abstract: This paper presents a new approach for fuzzy classification in the hesitant environments by decision making process. Our intention of the hesitant environments is situations, which there are different evaluations of experts for one problem. Typically, the classifier learns to predict class labels using a training algorithm and a training data set, when the training data set is not available, a classifier can be designed from prior knowledge and expertise. In this paper, we focus on cases that a classifier can be designed by knowledge of experts while each expert can classify data with a feature, independently, by linguistic terms. In this paper, we assume the classification task as a decision making problem in which, each feature as an attribute, each class as an alternative and each expert as a decision maker are considered. In the new classifier, we can use different score functions and aggregation operators in hesitant fuzzy sets for fuzzy classification in various viewpoints. Finally, our new approach is applied to a practical problem in economics, then for validation of the proposed model, we use Iris data from the UCI repository.
Keywords: Decision making problem; Fuzzy classification; Hesitant fuzzy set; Aggregation operator; Score function.
Inverse fuzzy soft set and its application in decision making
by Nasruddin Hassan, Ahmed Khalil
Abstract: Molodtsov introduced the theory of soft sets, which can be seen as a new mathematical approach to vagueness. In this paper, we introduce a new soft set called an inverse fuzzy soft set, along with its properties, characteristics, and operations. Then we construct an algorithm using max-min and min-max decision of inverse fuzzy soft set for a fuzzy decision making problem. Finally, we apply the algorithm to two decision making problems to illustrate its applicability. It is
shown that our proposed approach is viable and provide decision makers a more mathematical insight before making decisions on their options.
Keywords: algorithm; decision making; inverse soft set; inverse fuzzy soft set.
An interpretive structural modelling of enablers for Collaborative Planning, Forecasting and Replenishment implementation in high-tech industries
by Farhad Panahifar, Sajjad Shokouhyar
Abstract: The Collaborative Planning, Forecasting and Replenishment (CPFR) initiative is an increasingly popular approach that helps firms better collaborate with others within supply chain and coordinate activities to serve customers with improved service level and precise demand forecasting. It is highlighted in the literature that firms for successful CPFR implementation need to identify its Critical Factors (CF) consisting of enablers and barriers. Thus, the aim of this paper is to identify effective enablers and their relationships which enable firms to successful implementation of CPFR through the development of a structural model. To complete this task, ISM approach is applied by following a set of structured steps with a group of CPFR experts from industry/academia and Matrice dImpacts Crois
Keywords: CPFR; implementation enablers; supply chain management; collaboration; high-tech industries.
Pressure and Ethical Decision-making
by Cheryl Stenmark, Crystal Kreitler
Abstract: Performance pressure degrades performance on many types of tasks (Beilock & Carr, 2001). Mounting evidence, however, suggests that pressure may not affect ethical decision-making (EDM; Removed for review et al., 2010; 2011). It is important to determine whether pressure impacts EDM, so that training interventions can address pressure effectively. For the present study, participants analyzed an ethical dilemma using a cognitive tool (ACED IT), expressive writing, or a control task, and their decisions were compared for participants in high and low pressure conditions. Results revealed that The ACED IT group performed better on EDM indices than did participants in the other groups. Neither the main effect for pressure nor the interaction of the two IVs was significant with regard to the cognitive processes involved. Pressure did, however, have a significant main effect on perceptions of the problem, as measured by the PMIS. Implications of this pattern of results is discussed.
Keywords: ethical decision-making; cognitive tool; ACED IT; pressure; moral intensity.
Spatiotemporal Assessment of Water Quality in the Distribution Network of City of Sharjah, UAE
by Maruf Mortula, Kazi Fattah, Tarig Ali, Alaeldin Idris, Mayyada AlBardan
Abstract: Maintaining a healthy water distribution network (WDN) is key to providing good quality services to the consumers in a sustainable manner. WDN in the City of Sharjah, United Arab Emirates (UAE), has more than 3000km of pipeline and receives water from different sources. Understanding the variation of water quality over time is critical to appropriate management. The objective of this paper was to assess the variability of the water quality in Sharjah WDN. Monitoring data for residual chlorine, iron, and fluoride were collected from 46 different locations throughout the distribution system. Graphical and GIS-based analyses were conducted to understand the temporal (for three source waters and three locations in the WDN) and spatial variability (all locations) of water quality throughout the distribution network. Temporal variations indicated seasonal water quality variations throughout the three-year period (2012-2014). The spatial variability indicated that the old part of the city was susceptible to water quality degradation.
Keywords: water distribution network; water quality; infrastructure integrity; geographic information system; spatial variability; infrastructure management.
A non-stationary NDVI time series modelling using Triplet Markov Chain
by Ali Ben Abbes, Mohamed Farah, Imed Riadh Farah, Vincent Barra
Abstract: Nowadays, vegetation monitoring using remotely sensed data is an
important far-reaching real-world issue.The main purpose of this study is to
build a Triplet Markov Chain (TMC) to model and analyse vegetation dynamics
on large scales using non-stationary Normalised Difference Vegetation Index
(NDVI) time series.TMC is a generalisation of Hidden Markov Models (HMMs),
which have been widely used to represent Satellite Time Series Images but
which they proved to be inefficient for non-stationary data. The TMC model
proposed in this paper overcomes this limit by adding an auxiliary process
which allows modelling non-stationarity. In order to assess the performance of
the proposed model, experimentation is carried out using Moderate Resolution
Imaging Spectroradiometer (MODIS) NDVI time series of the northwestern
region of Tunisia. The TMC model is compared to standard HMM and Seasonal
Auto Regressive Integrated Moving Average model (SARIMA) and proved to
achieve the best performance with an overall accuracy prediction rate of 92.8%
and a kappa coefficient of 0.885.
Keywords: NDVI Time Series; Vegetation Dynamics; TMC; HMM; Non-Stationarity; Remote Sensing.
OFFICE LOCATION SELECTION BY FUZZY AHP AND VIKOR
by TAYFUN ARAR, SERHAT KARAOĞLAN, CEREN DİRİK
Abstract: In a globalized business world, reaching the basic goal of companies which is profit maximization has become such competitive for both manufacturing and service firms. While reaching this basic goal, increasing income is not sufficient, but also minimizing the costs is required. One of the long-term cost decisions, location selection, needs to be considered in detailed by business firms. Especially for a firm in service sector, there are other goals and responsibilities such as satisfying permanently changing needs and expectations of customers. As the wedding sector has a growing share in the economy, the choice of office location for companies operating in this sector has gained great importance. In the light of this purpose, there are some criteria in location selection decision for a business firm which operates in wedding sector. In this research, the criteria used in office location selection have been chosen by literature review and experts views. These criteria are weighted by Fuzzy AHP (Analytic Hierarchy Process). Then, using the weighted criteria, the best option among the alternative offices from different locations is chosen by VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) technique.
Keywords: Office Selection; Location Selection; Wedding Sector; Fuzzy AHP; VIKOR.
A novel architecture based on fuzzy cognitive maps and holonic systems for decision making in a cooperative context
by Asma Maziz, Nacereddine Zarour
Abstract: Ensuring consistency and good decision making is one of the most topical problems in an information system; it becomes more difficult in a cooperative context. In this paper, we propose an architecture based on fuzzy cognitive maps (FCM) tool and holonic multi-agent paradigm that enhance the decision making process in cooperative information system (CIS). Furthermore, the concept of ontology is used for semantically enrich our architecture. We modeled each sub-CIS by a holonic agents where everyone used a FCM for a more precise analysis of complex dynamic system decisions. This group will try to make a collective decision to solve any given distributed problem. To put our approach into practice, we considered road safety field to see how to educate people in order to reduce the fatal accidents number. Finally, we validated our proposition through experiments to show how it improves the decision making process in a cooperative context.
Keywords: Decision Support; Cooperative Information Systems; Fuzzy Cognitive Maps; Multi-agent Systems; Holonic systems; Ontology; Road safety.
Decision Support for Nutrition Management of Grapes using Ontology based on Decision Trees
by Archana Chougule, Vijay Kumar Jha, Debajyoti Mukhopadhyay
Abstract: For any decision support system, having meaningful, up-to-date, interoperable and consistent knowledge base is important. Ontologies can be sued for knowledge semantics and knowledge sharing. Hence ontologies are getting more importance these days as heterogeneous integrated systems are used in almost all areas. Ontology gets evolved with increase in domain knowledge of experts. Change management of ontology is must to keep consistency of knowledge base. This paper demonstrates use of decision tree for ontology building and evolution. Detail algorithm for extending ontology from decision tree is discussed in the paper. For decision support using knowledge in ontology, ontology reasoning is used. Semantic web rule language is the technique used for ontology reasoning. Accuracy of decision support depends on strength and correctness of inference logic. Paper describes how accuracy of decision support improves with semi-automated construction of SWRL rules. The approach is validated with example of nutrition management system for grapes.
Keywords: Decision support; ontology evolution; decision tree; semantic web rule language; grapes; nutrition management.
A decision making methodology for material selection using Genetic Algorithm
by Elyas Abbasi Jannatabadi, Masoud Goharimanesh, Ali Jahan, Ali Akbar Akbari
Abstract: Material selection is a challenging task for designers due to the immense number of different materials available today. Choosing the right materials plays an important role in numerous engineering applications because an inappropriate selection of materials can significantly affect the performance of the final product. As a result, a number of techniques have been proposed to select materials in the engineering design process. However, most of the proposed systems are knowledge intensive and cannot deal with the situation where the information of weight criteria is incomplete or unknown. So, in this paper a logical approach is presented for choosing an optimal material by employing the Genetic algorithm. The proposed material selection procedure reduces the personal bias for assigning the weight of different attributes. Seven examples are included to demonstrate the applicability of the suggested approach. The findings of this work provide the insights for further researches on more complicated design problems such as simultaneous material selection and geometry optimization.
Keywords: Materials selection; Genetic Algorithm; multiple criteria analysis; multi criteria decision making; weighting factors; Ranking.
Efficient Evacuation in a Multi-Exit Environment: An Agent-based Decision Support Model
by Kashif Zia, Dinesh Saini, Arshad Muhammad
Abstract: A majority of research work carried out in crowd evacuation rely on simulation due to non-availability of real and realistic trial data. In this paper, an agent-based simulation study of an evacuating crowd is presented. The model is based on the microscopic behavioral rules formulated through small-scale empirical evidence in conjunction with crowd behavioral theories. In particular, the study focus on the possibility of efficient evacuation from the environment with limited perceptions. Extending Moore's neighborhood model, local congestion avoidance mechanism capable of detecting the relative displacement and orientation of the all the individuals in its neighborhood is considered. Other strategies based on exit capacity and exit population are also modeled and tested. A probabilistic exit selection strategy is also designed that considers a sensitivity of an exit as a deciding factor. The simulation results show that the enhanced exit selection strategies make the proposed system more robust and increase the evacuation efficiency substantially.
Keywords: Crowd evacuation; Decision support model; Multi-exit efficiency; Agent-based modeling; NetLogo simulation.
An Integrated Fuzzy Delphi and Fuzzy Inference System for Ranking the Solutions to Overcome the Supply Chain Knowledge Flow Barriers
by Vishal Bhosale, Ravi Kant
Abstract: Supply Chain (SC) is assumed as a leading operations strategy in both manufacturing and service organizations. With rapid change and competition in the SC, knowledge is recognized as an important source of competitive advantage. In todays, business word, organizations should manage the SC knowledge to stay ahead in the competition. However, evidence suggests that there are several SC knowledge flow barriers (SCKFBs) which obstruct knowledge flow in SC. The purpose of this research is to identify SCKFBs and propose solutions to overcome the SCKFBs. In this study an example of Indian automobile brake manufacturing organization is presented to exemplify the use of the proposed framework for SCKFBs and solutions to overcome them. The weight of major SCKFBs is calculated by fuzzy Delphi (FD) method and ranking of the solutions of SCKFBs is evaluated using the fuzzy inference system (FIS) method. To overcome SCKFBs, the top rank solutions obtained are visible technologies that offer real-time customer and demand knowledge, building strategic relationships with SC partners and induce mutual trust with SC partners. Visible technologies offer real-time customer demand data; which enable SC partners to maximize operational efficiencies and enhance customer value creation. The extensive list of solutions of SCKFBs facilitates an organization to focus on higher ranked solutions and improve policies to implement them. This study may be one of the first to bring together a large range of SCKFBs on the same platform, and an attempt to give solutions for its barriers in order to put SC to the next level using integrated FD-FIS methods.
Keywords: Supply chain; knowledge flow; Fuzzy Delphi; Fuzzy inference system.
Studying the Effect of Community Structure for Seed Selection in an Influence Model
by Carolina Xavier, Vinícius Vieira, Alexandre Evsukoff
Abstract: This paper presents a study of influence spreading in real complex networks which shows that community structure in networks can be used to guide the selection of seed nodes to spread information and ideas over the network. The results obtained by the application of the methodology to a set of benchmark networks suggest that the distribution of seeds between the central nodes of the networks communities can increase the range of information spreading when compared to alternative methods using central nodes as seeds considering only the global context of the network.rn
Keywords: Influence maximization; seed selection; communities; Spreading Activation Model.
Enhancing the performance of sentiment analysis task on product reviews by handling both local and global context
by Bagus Setya Rintyarna, Riyanarto Sarno, Chastine Fatichah
Abstract: Commonly, product review analysis includes extracting sentiment from product documents. The contextual aspect contained in a review document has potential to improve results obtained by the sentiment analysis task of product reviews. In this regard, this paper proposes an approach that takes into account both local and global context. The main contribution of this work is threefold. Firstly, local context is defined and the graph-based Word Sense Disambiguation (WSD) method is extended to deal with this contextual issue. The method is aimed at assigning the correct sense of a word in the context of a sentence, which means choosing the correct sentiment value of a word with respect to the context. Secondly, global context is defined for addressing contextual issues related to the specific domain of a review document, which can affect the sentiment value of the words contained in it. To address the global context issue, an improved SentiCircle-based method is used and a similarity-based technique is provided to select the pivot word. This method can be employed to assign sentiment value at sentence level. Thirdly, a weighted mean-based strategy to determine sentiment value at document level is presented. Several experiments were conducted to assess the proposed method and compare it with a baseline method. Overall, the proposed method outperformed the baseline method in almost all performance evaluation measures (precision, recall, F-measure and accuracy).
Keywords: sentiment analysis; local context; global context; word sense disambiguation; SentiCircle.
Special Issue on: DASA'16 Decision and Logistics
Modeling Time Complexity of Micro-Genetic Algorithms for Online Traffic Control Decisions
by Ghassan Abu-Lebdeh, Kenan Hazirbaba, Omer Mughieda, Bassant Abdelrahman
Abstract: Genetic Algorithms are especially effective optimization tool when (near) optimal solutions for traffic control decisions for large-scale combinatorial problems are sought in real time. Optimizing online traffic control in urban networks is such a problem where online control decisions are recurrently sought based on real-time traffic information input. Limited time is available to reach near optimal solutions hence the best-thus-far solution (BTFS) is often the best one can do. It is thus critical that the best of BTFSs is identified. For that, offline evaluation is necessary to ensure appropriate combination of GA parameters and operators are selected for the real time online implementation in a real world setting. This paper describes an experimental approach to test the suitability of micro-Genetic Algorithms (m-GAs) to solve very large combinatorial traffic control problems and establishes relationships between time to convergence and problem size. A discrete time dynamical traffic control problem with different sizes and levels of complexity was used as a test-bed. The results showed that m-GAs can tackle computationally demanding problems. Upon appropriately sizing the m-GA population, the m-GA converged to a near-optimal solution in a number of generations equal to the string length. The results also demonstrated that with the selection of appropriate number of generations, it is possible to get most of the worth of the theoretically optimal solution but with only a fraction of the computation cost. The results showed that as the size of the optimization problem grew exponentially, the time requirements of m-GA grew only linearly thus making m-GAs especially suited for optimizing large scale and combinatorial problems for on-line optimization.
Keywords: On-line Traffic Control Decisions; Genetic Algorithms; Sustainable Transport; Genetic Algorithms Convergence.
Special Issue on: DASA'16 Decision and Logistics
Decision Aid System for Omani medical herb leaves recognition using computer vision and artificial intelligence
by Majed Bouchahma, Mohsin Al- Balushi, Sheikha Al-Housni, Hamood Al-Wardi
Abstract: Herbs have been widely used in food preparation, medicine and cosmetic industry. Knowing which herbs to be used would be very critical in these applications. This research aims to define a method to classify the herbs plants based on their leaves colors and shapes. An Open Source Decision Aid System is designed and developed especially for helping scientist. The proposed system employs artificial and image processing techniques to perform recognition on a number of Omani species of medical herbs.
Keywords: Decision Aid System; medical herbs; computer vision; artificial intelligence;SURF.