International Journal of Information and Decision Sciences (38 papers in press)
The Application of Strategic Alignment in a Fuzzy Environment: A Case Study in Banking
by Ayfer Basar
Abstract: As a natural consequence of rapid changes and high competition, companies have to determine right business and information technology (IT) strategies consistently to accomplish business value and IT flexibility. They also need to align their strategies and develop an appropriate roadmap in todays competitive environment. The lack of strategic alignment induces many problems in terms of profitability, efficiency, quality, and performance. In fact, there are many studies about strategic alignment in the literature. However, there is not an accepted method to align business and IT strategies and establish a suitable roadmap based on these strategies especially in the fuzzy environment. This study presents a new methodology to meet banks strategic alignment problem which can be defined by hierarchically and solved in a top-down structure. For this aim, a method of aligning first business and IT strategies; then IT strategies and domains is proposed depending on the customer expectations and technological improvements. Expert judgments are collected in pairwise comparison with Hesitant Fuzzy Linguistic Term Sets (HFLTS) for aligning and relatively weighting strategies and service domains. The importance weights obtained by HFLTS are also used to find the most appropriate investment rate for each service area. Proposed methodology is applied in a technology company of a big Turkish national bank to take investment decisions. The results are approved by experts working as strategic managers in banks and technology companies.
Keywords: Strategic alignment; banking; information technology; pairwise comparison; HFLTS.
Unorganized Entrepreneurship and Employment Generation in India
by Dhyanadipta Panda
Abstract: Considering Indias population growth and paucity of job opportunities, entrepreneurship is the only key to come out of the juncture of unemployment. Entrepreneurship may be in the organized or unorganized form. So far as engagement of workforce is concerned, unorganized sector in India outweighs the organized sector. But the unorganized sector fails to win the confidence of the stakeholders owing to its unsystematic approach and unavailability of regulatory framework. Due to this the stakeholders of this sector dont boast about their affiliation. This paper unfolds how unorganized sector in India easily accommodate heavy workforce but with many gray areas. Case study method and in-depth interview method are followed to highlight the problem and focus group discussion is conducted to design a framework for win-win situation.
Keywords: Unorganized sector; entrepreneur; self-employment; contribution; challenges.
SWARA Approach for Ranking of Agricultural Supply Chain Risks of Odisha in India
by Debesh Mishra, Suchismita Satapathy
Abstract: Disruptions in supply chain process could have negative effects on firms performance and if the highly influential risks factors involved in disruptions of the supply chain are ranked and mitigated properly based on their importance, then those disruptions could be well managed. In order to assist the decision makers and other managers to take appropriate decision, this study provides the ranking of the risks in agricultural supply chain of Odisha in India using the SWARA (Step-wise Weight Assessment Ratio Analysis) method based on the four main categories in agricultural sector such as crops, livestock, fishing and forestry & logging. In addition to the above, this study also ranks the risks based on agricultural supply chain related risk considering the important risk variables involved in the supply chain.
Keywords: Agricultural sector; Supply chain; Risks; Ranking; SWARA; Odisha.
Comparative Study of MCDM Methods under Different Levels of Uncertainty
by Akshay Hinduja, Manju Pandey
Abstract: Often, data in MCDM problems are imprecise and changeable due to the mandatory participation of human judgment, which is often unclear and vague. Hence the selection of an appropriate MCDM method is crucial to the optimal decision making. All the MCDM methods are heavily affected by individual or group preferences and therefore even a small change in the data can cause rank-reversal. With the regular proliferation of such methods and their modifications, it is important to carry out a comparative study that provides comprehensive insight into their performances under uncertain conditions. In this paper, we use the Monte Carlo simulation approach to empirically compare the results of five well-known and widely applied MCDM methods, WSM, WPM, TOPSIS, GRA, and MULTIMOORA under different levels of uncertainty. The findings of this paper will assist decision makers in the selection of most robust and reliable MCDM methods for different decision scenarios. The results of this research are significant additions to the current repository of knowledge in the Multi-Criteria Decision Analysis as well as the literature pertaining to the Information Systems. It also provides insights for many managerial applications of these MCDM methods.
Keywords: Multi-Criteria Decision Making; Comparative analysis of MCDM Methods; Monte-Carlo Simulation; Uncertainty.
Integrating Statistical Correlation with Discrete Multi-Criteria Decision Making
by Malik Haddad, David Sanders, Giles Tewkesbury, Nils Bausch
Abstract: This paper analyses two hypotheses that considers a correlation between the number of alternatives and the number of criteria considered in a Multiple Criteria Decision Making (MCDM) problem with the minimum percentage change required in the lowest criterion weight to change the outcome of a method. Two MCDM methods are considered, The Analytical Hierarchy Process (AHP) and The Preference Ranking Organization METHod for Enrichment of Evaluations II (PROMETHEE II) were applied to the same sets of criteria weights and performance measures. More than two thousand randomly generated sets of criteria weights and performance measures are considered. The minimum percentage change in the lowest criterion weight required to change the outcome of a method is calculated. Pearsons r parametric test is used to test the hypotheses. Results from parametric test were statistically significant and shows a weak negative correlation for hypothesis one and weak positive correlation for hypothesis two.
Keywords: Multiple Criteria Decision Making; AHP; PROMETHEE II; Correlation; Criteria; Pearson’s r parametric test.
Performance Evaluation in a Two-stage Network-DEA with Intermediate Products
by Hoda Golshani, Mohammad Khoveyni, Hadi Bagherzadeh Valami, Robabeh Eslami
Abstract: A main difference between conventional data envelopment analysis (DEA) and network DEA (NDEA) is considering the internal structure of a decision making unit (DMU). The existing NDEA methodologies neglect to address the issue of incorporating the influenced of intermediate directly in objective function. To overcome this shortcoming, we are setting an NSBM model for evaluating the overall efficiency score by using two steps in a generic two-stage structure. Hence, we solve an additive model for finding the amount of optimal value of intermediate measures, firstly. Then, our modified NSBM model have incorporated optimal value of objective function is introduced. So, our sole contribution will be introducing a model for finding overall efficiency and calculating unique value for stage efficiencies, input, intermediate and output oriented efficiency that is an important issue in network research area. Finally, an empirical example is provided for verifying our proposed approach.
Keywords: Data envelopment analysis (DEA); Intermediate products; Slack-based measure (SBM); Efficiency; Network-DEA (NDEA); Two-stage; Intermediate-oriented efficiency.
Surveying forecasting: a review and directions for future research
by Shari De Baets
Abstract: How is forecasting doing in todays world? Its a question researchers have been asking for a long time. For half a century, we have been surveying practitioners, conference attendees, other academics, managers and high-level executives. From the introduction of forecasting in organisations onward, we have questioned technique use and familiarity, accuracy and evaluation methods, the place of forecasting within organisations and the hurdles and barriers that prevent forecasting from evolving as fast in practice as it does in academia. This paper summarizes these findings and concludes with a number of recommendations for future surveys, as we will need to continue tracking the state of the art of forecasting practice. Recommendations includes surveying the analysts rather than the forecasting managers, using an international sample, focusing on process-oriented performance measures and looking into the barriers that prevent a more widespread adoption of sophisticated forecasting techniques.
Keywords: forecasting survey; practitioners; forecast improvement.
An ontology-based approach for automatic goal requirements engineering in data warehouse design
by Fahmi Bargui, Hanene Ben-abdallah
Abstract: Goal-oriented approaches in data warehouse development projects still face two main issues. First, analysts often lack domain knowledge required during goal decomposition. This may lead to identifying erroneous requirements that most likely propagate to the remaining project phases, potentially leading to the project failure. Second, the identification of the data warehouse content from requirements is done manually by the designers in an error-prone process. In this paper, we address these two issues. We propose an ontology that formalizes and automates the reasoning about decision-making knowledge, which allows analysts to compensate their lack of domain knowledge during goal decomposition. In addition, to demonstrate the feasibility of our proposal we present a semi-automatic process that assists the construction of the ontology. Furthermore, the proposed ontology ensures the traceability between both decision-making and data warehouse knowledge. Thanks to this traceability, we propose a set of rules that automatically derive a data warehouse schema from requirements specification.
Keywords: Decision Support Systems; Data Warehouse; Data Mart; Multidimensional modeling; Requirements elicitation; Goal-oriented Requirements Engineering; Automatic Reasoning; Ontology.
Rough Set based Quality of Service Optimization Guidelines with Stack Parameter in MANET
by PRATHVIRAJ NAGARAJA
Abstract: In Mobile Ad-hoc NETwork(MANET),providing a guaranteed service for packet delivery has to pass through several hurdles. Providing guaranteed service should address multiple Quality of Service (QoS) parameters under uncertain network conditions. The QoS parameters should be prioritized in consideration of MANET constraints and applications running on MANET. Decision making from imprecise information will solve NP-complete problem of providing multi constrained QoS routing. The QoS aspects of MANET are impacted by the multi layer stack parameters. An analysis has been conducted to identify the trade off point for configuring the stack parameters to achieve better QoS in packet delivery. The outcome of analysis is discussed in terms of packet error rate, energy efﬁciency, goodput and delay with the support of Rough Set Theory(RST).The multi-objective analysis capabilities of RST make it suitable for taking dynamic decision to prioritize QoS parameter,which provide the guaranteed service in packet delivery. The Rough Set Exploration System (RSES) is used for extensive analysis of QoS in data delivery. The different stages of RSES like data exploration, discretization, reduction and decision rules are applied on QoS values of available routes. The rough set decision rules are filtered out based on probabilistic properties like strength, certainty and coverage of the decision rules. The positive dependency between conditions and decisions of selected decision rules are confirmed with certainty and coverage of relationships.
Keywords: MANET; Quality of Service; Rough Set Theory; Rough Set Exploration System.
Academic Students' Performance Prediction Model: An Oman Case Study
by P. Vijaya, Satish Chander, Gupta S.L
Abstract: Education system in Oman attains a fast growth and it requires effective standards to increase the number of graduates with quality education and with effective skills and knowledge. The Higher Education Institutions (HEIs) in Oman is increasing in number and it poses the need for graduates with world-level competing tendencies. Keeping this in mind, the proposed methodology proposes a novel method of predicting the academic performance of the students enrolled in the universities of Oman. For predicting the academic performance of the students, the Dragonfly Optimization-based Deep Belief Network (DrDBN) is employed. The data is collected using the proper questionnaire session and the best feature is selected based on the fuzzy-based entropy function. The training algorithm determines the optimal weight to the Deep Belief Network (DBN) for predicting the best solution. The proposed method predicts the performance of the student in the semester exams and adopts a proper teaching standard to equally benefit the students of all grades and in addition, the prediction strategy contributes a lot to the students to utilize their full potentials in the process of learning. The effectiveness of the proposed DrDBN is checked depending on the MSE and the RMSE metric values and is evaluated to be the best when compared to other existing techniques with low MSE value as 0.532, and low RMSE value as 0.026, respectively.
Keywords: Student performance prediction; Dragonfly algorithm; fuzzy entropy; Deep Belief Network; MSE.
Health Information Exchange Adoption: Influences of Public Insurance Programs
by Hsun-Ming Lee, Ju Long, Mayur Mehta, Peiqin Zhang
Abstract: For many years, the U.S. government has pushed the adoption of Health Information Exchange (HIE), which is a key to spur large-scale innovation in the healthcare delivery. As funding has diminished, healthcare managers need to assess the adoption incentivized by government programs. This study helps to get a better understanding of how the adoption is influenced by the factors associated with the policies regulated by public insurance programs: Medicare and Medicaid. Using the technology-organization-environment (TOE) framework, we evaluate the Health Information Exchange (HIE) adoption factors associate with policy implications. Based on a dataset integrated from data reported to the Healthcare Cost Report Information System and Hospital Comparison data, we conducted a logistic regression analysis to model the probability of HIE adoption as a function of TOE factors. Besides factors that affect hospitals technology adoption, such as hospital sizes and geographic locations, our research also revealed three significant HIE adoption factors not thoroughly examined before, including imaging efficiency, scale of outpatient departments, and payer mix. Our research could provide insights for practitioners and healthcare managers when examining the strategies associated with HIE adoption.
Keywords: Health information exchange; Healthcare; Technology adoption; TOE framework; Public insurance.
Technical Debt Reduction using Epsilon-Nash equilibrium for the Perturbed Software Refactor Game Model
by Vimaladevi Madhivanan, Zayaraz Godandapani
Abstract: Introduction of various software development processes and methodologies are aimed at building a quality software product. Irrespective of the existence of such techniques, there is a constant requirement to achieve a better and improved quality of the software products. Refactoring is a well known and vital technique for quality improvement that is applied to all types of software systems, which helps in the improvement of the internal structure of the code, without modifying the externally visible properties. Technical Debt (TD) is a metaphor that is one of the reasons for software to become obsolete. TD occurs whenever the required uncompleted changes exist in software due to constraints such as deadline management. The process of refactoring can be effectively applied to reduce the Technical Debt and the improvement of other vital quality attributes such as Abstraction, Inheritance, and coupling. This paper discusses a refactoring model that can be applied for an object oriented software system for improved quality by applying the concepts of Game Theory. A Multi-Player Perturbed Software Refactor Game Model is developed, that models Inheritance, Abstraction, Coupling and the Technical Debt, as multiple players of the game. An optimal strategy for refactoring the source code is arrived by calculating the ?-Nash equilibrium of the perturbed game. The results attained are compared against the popular Genetic Algorithm (GA), Artificial Bee Colony (ABC) and Simulated Annealing (SA) optimization algorithms, taking three open source java project samples. The results show that the proposed Game-Theoretic approach outperforms the other compared optimization algorithms with significant improvement in terms of the quality gain for Technical Debt and Coupling functions.
Keywords: Refactoring; Quality attributes; Technical debt; Game Theory; Epsilon-Nash equilibrium; Multiplayer Software Game; Trembling Hand Perfection.
Evaluation of Risk Causing Factors for the Incidence of Neck and Shoulder Pain in Adolescents using Fuzzy Analytic Hierarchy Process
by T. Padma, S.P. Shantharajah
Abstract: The incidence of neck and shoulder pain is recurrent in adolescents, and numerous factors causes for the occurrence of such a risk. Neck and shoulder pain in adolescents constitutes a large socioeconomic challenge and is responsible for substantial personal impacts and societal costs; thus requires intensive and systematic research to identify the potential causes and their precedence in pain occurrence. This study intent to explore the prevalence of neck and shoulder pain among the adolescents and to examine the array of risk factors associated with the hazard of the pain. Through background study and knowledge engineering process the potential risk factors identified are physical-, psychological-, psychosocial-, behavioural-, emotional- and sedentary-related. A set of domain experts comprising of an orthopedic surgeon, a neurologist, a psychoanalyst, a public health physician and a physiotherapist were involved in building a knowledge base. A Fuzzy analytic hierarchy process method exploiting indeterminate human knowledge and experience is applied to prioritize the derived risk factors and determines the precedence of risk level in neck and shoulder pain incidence among the adolescents which are categorized based on their ages as early-, middle- and late- adolescence group (age between 10-17, 18-21 and 22-24 respectively). The arrived results indicate that the middle adolescence category have a significantly greater chance for pain occurrence followed by early adolescence category; and then by late adolescence category. The outcomes of this research will support physicians, parents, policy makers and social workers in decision making and planning for the welfare of youths.
Keywords: Adolescent health; Neck and shoulder pain; Knowledge Engineering; Domain experts; Fuzzy Analytic Hierarchy Process.
Has globalisation reaped rewards? A fresh perspective from India.
by Shikha Gupta, Nand Kumar
Abstract: Using annual time series data from 1980-2015, the study aims to estimate the empirical relationship between Indias trade, globalisation, and GDP growth. For this, ADF along with PP and KPSS techniques are used to establish stationarity. As there is evidence of co-integration, Vector Error Correction Model and DOLS estimations are used to adjudge the adjustment of variables. Wald test and Toda-Yamamoto Granger causality follow in analysis to investigate the short-run and the long-run causality, respectively. In order to assess the response path, variance decomposition and impulse response functions are created. Globalization has a negative effect and trade bears a positive effect on economic growth. However, contrary to the perception of New Growth Theory, increasing trade and globalisation do not have an impact on the long-run economic growth. The novelty of the study lies in using an augmented version of KoF Index to avoid the problem of collinearity and more robust approach.
Keywords: Trade; Globalisation; Growth; Openness; KoF Globalisation Index.
Quantitative Class Association Rule-based Approach to Lecturer Career Promotion Recommendation
by Tubagus Mohammad Akhriza, Indah Dwi Mumpuni
Abstract: This paper introduces an effective method for recommendation of career promotion for educators in universities. Given an educator with an activity profile currently in a certain career state, the system is able to recommend the next career state that is most suitable for the educator by learning the pattern of historical activities of other educators. The system also recommends activities and their volume that the educators should take in order to achieve the career state. Patterns of activities and their volume were obtained using the quantitative class-association rule mining method that classifies quantified activities to a next-state class. The experiment using educator career data taken from Indonesian universities, produced several recommendations that were somewhat contrary to the opinions of experts, about educators who can make a career leap, an indication that expert subjectivity was more dominant than a statistically more reliable recommendation system in making decisions
Keywords: Association rule; Career promotion; Higher education; Indonesia; Recommendation system.
Performance evaluation of arc welding processes for the manufacturing of pressure vessel using novel hybrid MCDM technique
by Manoj Mathew, Santosh Kumar Patanwar, Sanjay Gupta, Taranjeet Sachdev
Abstract: Selection of welding process for the manufacturing of pressure vessel in the fabrication unit is conventionally done by the manufacturer depending upon his/her experience of welding process. This approach has a delimitation that it ignores many conflicting criteria affecting the selection of best welding process, so past, researchers have used multi criteria decision making techniques for the evaluation/selection of the best welding process. It is seen that, while evaluating welding processes, subjective preferences having vagueness are given by the decision maker. Also, If the evaluating criteria are more in number, then the number of comparisons needed for formulating pairwise comparison matrix in analytic hierarchy process become more, making it complex and time taking for the decision maker. It is found that fuzzy set theory is effective in handling vagueness and subjectivity related to linguistic values in the decision-making process and best worst method (BWM) can be a good alternative of AHP as it requires less comparisons. So, a novel hybrid fuzzy MCDM approach combining fuzzy AHP, BWM and fuzzy TOPSIS is developed, which brings out a reliable and robust solution in the selection of the best welding process. A numerical illustration is shown which evaluate and select best welding process among shielded metal arc welding, flux-cored arc welding, gas metal arc welding and submerged-arc welding for the manufacturing of pressure vessel made of ASTM A516 Grade 60 carbon steel plate based on seven evaluating criteria i.e. cost of welding, productivity, welder fatigue, reliability, technical skill required, cleaning required after welding and initial preparation. It is found that, submerged-arc welding is the best welding process for the manufacturing of pressure vessel. Sensitivity analysis is also carried out to check the robustness of the method and SAW remains to be the best welding process for pressure vessel manufacturing.
Keywords: MCDM; Fuzzy AHP; Fuzzy TOPSIS; BWM; welding process evaluation.
Low Data Intensive Models For Supporting Taxi Policy Making: case Study In Cyprus
by Glykeria Myrovali, Josep Maria Salanova Grau, Thanasis Gkoutzikas, Aristotelis Savvas
Abstract: Taxi industries are present everywhere around the world providing door-to-door mobility services. The type of services and policies that apply to the taxi sector vary significantly from city to city, ranging from controlled environments with strong restrictions to open and more competitive markets. Decision makers responsible for taxi policy making are supported by economic models of the taxi sector in most cases. In some cases, the lack of available data does not allow for having such models and therefore decision making is based on assumptions, failing some times in establishing taxi services of quality.
This paper presents a 3-step methodology for defining solutions of Cypriot taxi industry. Based on the results of the abovementioned steps, the paper concludes with the development of alternative viability assessment methodologies for taxi industry and some proposals for the re-formation of taxi services including also pricing policies in Cypriot environment.
Keywords: taxi industries; taxi modelling; modal split.
An Adaptive Neuro-Fuzzy Inference for Blockchain-based Smart Job Recommendation system
by E.P. Ephzibah, R. Sujatha, Jyotir Moy Chatterjee
Abstract: Blockchain is a technology that supports secured transaction in a public distributed database. It maintains a peer to peer network where a transaction cannot be modified or tampered by unauthenticated users. It provides a safe message transfer from a sender to a receiver. Job recommendation is an online system that provides a mapping between the job seeker and the employer. Focusing on the demerits of the existing job recommendation systems like unsecured message transmission, unauthenticated data, time-consuming search, irrelevant keyword matching, etc., this paper proposes a public blockchain of job recommendations based on incremental hashing. The examinations show that this blockchain job recommendation provides process integrity, traceability, security, high levels of transparency, drastic reduction in operational cost and high standard and systematic. The system has two stages. Firstly, using blockchain technology, the authenticated data is fetched. Secondly, a classification model using adaptive neuro-fuzzy inference system is built for mapping the job seeker to the recruiter. This approach proves to be authenticated as well as a smart job recommendation system.
Keywords: Blockchain; Distributed Database; Peer to Peer Network; Job Recommendation System; Unsecured Message Transmission; Unauthenticated Data; Time-consuming Search; Incremental Hashing; Classification Model; Adaptive Neuro-Fuzzy Inference System (ANFIS).
What determines the household decision to borrow for investment or Repayment of old debt? The Indian Story
by Moumita Poddar, Tanmoyee Banerjee, Ajitava Raychaudhuri
Abstract: Borrowing for investment in either physical or human capital promotes growth while that for consumption or debt-repayment may lead to so called debt-trap for the households. The present paper probes deeper into the decision making process of the households regarding choice between these alternative borrowing. The data comes from All India Debt and Investment Survey (NSS 70th round). The methods used are Craggs Box-cox double hurdle model and Instrumental variable (IV) probit model. Our study shows the decision to borrow for investment purposes depends on such factors as gender, religion, location, education, asset position as well as on the status of financial inclusion of households. The decision to borrow for repayment of existing debt is most prevalent among urban educated households in addition to land-owning rural borrowers.
Keywords: Institutional borrowing; capital formation; Financial inclusion; Inequality; potential debt-trap.
Stock price forecasting using hidden Markov model
by Ernest Amiens, Ifuero Osamwonyi
Abstract: We used Hidden Markov Model (HMM) with single observation to estimate stock prices of selected manufacturing companies from the Nigerian Stock Exchange. Data from 22 November 2013 to 6 July 2018 were partitioned into two data sets for training and testing. Subsequently, the data were differenced, trained, tested and used to forecast closing prices for sixty days for each equity. The HMM was implemented with Matlab. The research revealed closing price prediction accuracy ranging from 3.33% to 96.67% and trade signal precision ranging from 31.67% to 97.67%. Also, the MAE values range from 0.0013 to 34.2867 while the MAPE values are between 0.1498% and 6.0034%. The hypothesis tested revealed that the model is efficient. Similarly, the comparison test conducted revealed the performance of HMM is better than ARIMA and Neural Network (NN). The research proposes that Hidden Markov Model be adopted in the exercise of stock price forecasting.
Keywords: stock forecasting; hidden markov model; stock price; manufacturing firms; neural network; ARIMA; MAPE Nigerian stock exchange; forecast accuracy.
Design of Machine Learning and Rule Based Access Control System with Respect to Adaptability and Genuineness of the Requester
by Kriti Srivastava
Abstract: Access control system plays a major role in data security. It becomes more challenging for the system to provide accurate access control, if data is huge and data requesters are not fixed. This is very predominant in todays era of big data where new data and new requestors are adding to the existing system very frequently. The main issue here is to justify adaptability (for new data and new requester) in the access control system. Traditionally access control was implemented for a set of fixed resources and fixed users, but human intervention was required earlier if the resources or the users, increased. In role based methods, new roles were added by the administrator and mapped with the resources. In policy based, policies were modified by the administrator. Human intervention is not considered as completely adaptable.Over a period, many research work had been published in this area to provide better adaptability in access control methods. They can be categorized into four main categories: probabilistic based models, machine learning based models, ontology based models and fuzzy inference to rule based models. All these methods had proven their adaptability in terms of increase in users or resources. Earlier researchers had shown their test results as access control works well if the number of requesters or resources are increased but the missing part in their published work is verification of genuineness of the users. We have to evaluate the need of adaptability in the existing access control system and then add new requester or change the access rights of an existing requester of the system. If the system has to take decision, then first step is to analyze whether there is actually a need to add the requester. Adaptability in these cases should be provided with great analysis of environmental and physical factors. Information is very sensitive in any use case and it should be granted to genuine requesters only. A detailed assessment of each new request with respect to the requester, information and environmental factors should be done and then the decision should be taken whether to grant or to deny the access.
Keywords: Adaptive access control; decision making; rule based system; fuzzy inference.
Testing the Validity of Cauchy Model Based on the Informational Energy
by Hadi Alizadeh Noughabi
Abstract: In this article, a test statistic for testing the validity of the Cauchy model based on the informational energy is introduced. Consistency of our test is shown. Also, we show that the distribution of the test statistic does not depend on the location and scale parameters. Through a simulation study, we obtain the critical values of the test statistic and then the power of the test is computed by Monte Carlo method against different alternatives. The performance of the proposed test with some competing tests is compared. Our results show that our test is superior to the classical nonparametric tests and can apply to a testing problem in practice.
Keywords: Goodness of fit test; Cauchy distribution; Informational energy; Power of test; Monte Carlo simulation.
A multi-criteria location selection model based on Fuzzy ANP and Z-number VIKOR methods: a case study
by Pedram Memari, Seyedeh Samira Mohammadi
Abstract: Initial investment for construction of a power plant is an important issue which technologies and methods are moving forward to minimize the total costs. Therefore, a power plant should be established in a region which will increase the reliability and efficiency with minimum total cost. In this study, location of a solar power plant is optimized with considering a set of criteria including environmental, economic, social and strategic aspects in which energy resource usage will be maximized along with the least cost and the economic growth of country. For this purpose, Fuzzy ANP and Z-number VIKOR approaches are used to rank the 20 candidate cities in Iran. The obtained results indicate the efficiency and reliability of this study.
Keywords: location optimization; solar energy; Fuzzy ANP; Z-number VIKOR; reliability.
Technical Efficiencies and Technical Change Gaps in Africa: Application of DEA on African Sectors Input-Output (IO) Frameworks
by Samson Gebrerufael
Abstract: This paper presents the African sectors technical efficiencies and the technical change gaps. The non-parametric data envelopment analysis (DEA) is employed using the standard input-oriented BCC model. To present the state of the technical efficiencies of African sectors, the technical coefficients of 25 sectors are examined using the IO tables of 2005 and 2013 taken from the Eora MRIO database (2013). In 2005 and 2013, the only strongly efficient sector (the benchmark sector) is found to be the financial intermediation and business activities sector. Even if the technical inefficiency prevails in Africa, the actual technical efficiency changes are observed between 2005 and 2013 as the technical coefficients of 2013 are found to be relatively smaller than that were in 2005. However, the changes in the actual coefficients are found to be lower (in absolute value) than the potential technical coefficients and therefore lost input savings and lost outputs. Simply, the average potential input savings in 2005 had the sectors had employed the 2013 lowest technical coefficients is found to be 93.9% (in absolute value). Hence, the African sectors have been performing weak in avoiding the wastage of inputs.
Keywords: technical efficiency; mix-inefficiency; actual technical change; potential technical change; technical Change gap; BCC Model.
Context vector convergence of computational behaviour and cultural traits for team selection
by Hrishikesh Kulkarni, Manisha Marathe
Abstract: Selection of team for match, mission or project is always challenging since every mission is different and every match brings new uncertainties. Your best resource may not be the right choice for the given task. It is the context of task, behaviours of individuals and above all constitution of the team in that scenario contribute to the outcome. The context vector convergence (CVC) of behavioural vectors helps in deriving the actual effect of two vectors in overall team performance. The personality vector is used to derive behavioural context while mission vector is used to derive the scenario context. These two vectors are graphically associated in convergence to identify and recommend the best team combinations. While formulating the vector, cultural aspects and behaviours are captured through expressions and interactions. Top three combinations are compared to validate hypotheses. The promising results reinforce the premise to establish further research directions.
Keywords: behavioural psychology; machine learning; artificial intelligence; cognitive sciences; computational psychology; context; cultural computing; computational social sciences; mission vector; personality vector; context vector.
The effect of social capital on the effectiveness of community development programmes in Malaysia
by Amir Imran Zainoddin, Azlan Amran, Mohd. Rizaimy Shaharudin
Abstract: This study aims to determine the influence of social capital on the effectiveness of the farmer's development programme established by a MNC in Malaysia for business-community relations as part of the company's CSR endeavours. The sampling technique employed in this study was census sampling with all of the 400 respondents being included in the study. The results unveiled that the relational and cognitive dimensions were positively and significantly related to the effectiveness of the community development programme. Nevertheless, the structural dimension failed to follow similar inclinations. The finding has contributed to the social capital theory by supporting the relational and cognitive dimensions as the factors that influence the success of the community development programmes. Future study is suggested to measure the effectiveness of community development programmes using financial or non-financial aspects, utilise the stakeholder theory perspectives, as well as validate the inconsistencies in the outcomes of the past studies.
Keywords: corporate social responsibility; social capital; effectiveness; community development programmes; farmers.
Decision tree classifier: a detailed survey
by Priyanka, Dharmender Kumar
Abstract: Decision tree classifier (DTC) is one of the well-known methods for data classification. The most significant feature of DTC is its ability to change the complicated decision making problems into simple processes, thus finding a solution which is understandable and easier to interpret. This paper provides a brief review on various algorithms developed in literature for constructing and representing decision trees, splitting criteria for selecting best attribute and pruning methods. The readers will be able to understand why decision trees are more popular among all other methods of classification, what are their uses, limitations and applications in different diverse areas. They will also come to know about a decision tree induction algorithms, splitting criteria, pruning methods, concepts of ensemble methods, fuzzy decision trees, hybridisation of DTCs, etc. These enhancements are found very helpful in solving complex datasets with less computation in very short time period while achieving high accuracy.
Keywords: decision tree hybridisation; classification; iterative dichotomiser 3; ID3; CART; ensembles; splitting criteria; pruning methods.
Impact of knowledge flows on supply chain performance: an experiment on four Indian luggage manufacturing firms
by Vishal A. Bhosale, Ravi Kant, Mark Goh
Abstract: This paper seeks to investigate the role and impact of supply chain knowledge flow enablers (SCKFEs) in improving the supply chain performance of four luggage manufacturing firms. The paper applies fuzzy analytic hierarchy process (AHP) to obtain the weights of the SCKFEs, and fuzzy multi-objective optimisation by the ratio analysis (MOORA) to rank the firms practicing knowledge flows. A case study of four Indian luggage manufacturers suggests that the better the implementation of the SCKFEs, the better the knowledge flows and hence better supply chain performance. This study reveals how firms practicing knowledge flows influence their supply chain performance.
Keywords: knowledge flow; supply chain performance; multi-criteria decision making; MCDM; analytic hierarchy process; AHP; MOORA.
Hierarchical two-pathway autoencoders neural networks for skyline context conceptualisation
by Ameni Sassi, Wael Ouarda, Chokri Ben Amar, Serge Miguet
Abstract: In this paper, we proposed a novel hierarchical two-pathway autoencoders architecture to transform a local information based on skyline scene representation, into nonlinear space. The first pathway is intended for the transformation of the geometric features extracted from the horizon line. The second pathway is applied after the first one to joint the colour information under the skyline to the transformed geometric features, and to get the landscape context conceptualisation. To evaluate our suggested system, we constructed the SKYLINEScene database containing 2,000 images of rural and urban landscapes, with a high degree of diversity. In order to investigate the performance of our HTANN-Skyline, many experiments were carried out using this new database. Our approach shows its robustness in skyline context conceptualisation and enhances the classification rates by 1% compared to the AlexNet architecture; and by more than 10% compared to the hand-crafted approaches based on global and local features.
Keywords: deep neural network; autoencoder; scene categorisation; skyline; curvature scale space; features transformation; classification; horizon line; hierarchical; skyline context conceptualisation.
Strategic decision making to maximise the efficiency of water usage in steel manufacturing process via AHP and BBN: a case study
by José Cristiano Pereira, Geane Cristina Fayer
Abstract: This study proposes a method for strategic decision making, considering the identification and prioritisation of the potential risks that could stop production in the steel production processes in a water crisis scenario. This method combines AHP and BBN to assess risks arising from the water crisis scenario in steel manufacturing industries. As a methodological approach experts and professionals from a group of steel manufacturing companies were interviewed do identify risk factors considering a water crisis scenario and the risk probabilities were elicited accordingly. No previous work dealing with risk analysis to prioritise risks arising from the water crisis scenario in steel manufacturing processes could be found. As a result of this study, a global risk matrix is proposed. The study provides a method to be used by professionals, engineers and decision makers in the identification of risk factors that could impact the operation of steel manufacturing companies.
Keywords: risk analysis; water shortage; analytic hierarchy process; AHP; Bayesian belief network; BBN; steel industry.
Query optimisation in real-time spatial big data
by Sana Hamdi, Emna Bouazizi, Sami Faiz
Abstract: Nowadays, real-time spatial applications have become more and more important. Such applications result dynamic environments where data as well as queries are continuously moving. As a result, there is a tremendous amount of real-time spatial data generated every day. The growth of the data volume seems to outspeed the advance of databases and data warehouses especially that users expect to receive the results of each query within a short time period without holding into account the load of the system. To solve this problem, several optimisation techniques are used. Thus, we propose, as a first contribution, a novel data partitioning approach for real-time spatial big data named vertical partitioning approach for real-time spatial big data (VPA-RTSBD). This contribution is an implementation of the matching algorithm for traditional vertical partitioning. Then, as a second contribution, we propose a new frequent itemset mining approach which relaxes the notion of window size and proposes a new algorithm named PrePost*-RTSBD. Thereafter, a simulation study is shown to prove that our contributions can achieve a significant performance improvement.
Keywords: real-time spatial data; transaction; stream data; feedback control scheduling; quality of service; data partitioning; frequent itemset mining; simulation.
Credit cards in a developing economy: a data mining approach
by Ankit Mehrotra, Reeti Agarwal
Abstract: Usage of credit cards has been witnessing an increase in recent years in India. The study was undertaken to comprehend the effect of the different demographic characteristics of the respondents on credit cards owned by them. Findings indicate that friends/family members are most influential in affecting customer's knowledge of credit card. It was seen that for pitching more than one credit card, the group of customers that should be targeted are those with low income and in the age group 46-60 years.
Keywords: C&RT; credit cards; data mining; demographic variables; feature selection; gender; income; Indian customers; influencing medium; target group.
Family members as an external source of travel information
by Zahra Shekarchizade, Bahram Ranjbarian, Vahid Ghasemi
Abstract: The aim of this work is to investigate the effect of family structure, duration of family life and family members' acquaintance with travel destination on information search behaviour of heads of families to buy a package tour. A sample of 70 Isfahani heads of families who had bought an outbound package tour in January-September 2017 was selected. The results indicate that family structure and duration of family life have impacts on the perceived value of seeking information among family members. In families that have different value structures and in various stages of family life cycle, the perceived value of seeking information among family members is different; however, the perceived value was not significantly effective in the level of seeking information among family members. Indeed, family members' acquaintance with travel destination has a significant impact on the level of seeking information by using perceived value of seeking information among family members.
Keywords: family members; travel information; family structure; duration offamily life; familiarity; information search behaviour; perceived value; familial factors; external source; decision making.
Special Issue on: ICDSST - PROMETHEE DAYS 2018 Decision Support for Transport Methodologies, Tools and Applications
SELECTION AND ASSESSMENT OF PEDESTRIAN AREAS IN URBAN ENVIRONMENTS: A MODEL AND EXPERT-OPINION-BASED APPROACH
by Iraklis Stamos, Evangelos Mitsakis, Josep Maria Salanova Grau, Maria Morfoulaki, Kornilia Kotoula
Abstract: Many cities worldwide have implemented pedestrian zones in the past years, as part of an effort to ease the impacts of congestion. However, as pedestrianisation directly affects traffic circulation, operation and accessibility, the issue of optimally selecting pedestrianisation areas within an urban road network is pivotal. The present paper applies a two-level optimization method for identifying road links and axes within an urban road network, which, if selected for pedestrianisation aiming at improving the pedestrians LoS, would result in the minimum impacts on overall traffic and accessibility. The method is based on a unified network performance measure for calculating the criticality of network links, using the total network demand and the difference in travel time as a consequence of a link closure. Outcomes of the proposed method are presented for a case study in Thessaloniki, Greece. Using a detailed traffic model, we present parts of the road network where traffic is restricted for private vehicles and turned into pedestrian zones, causing less additional travel times to the entire network. Results are expressed in traffic-related performance measures (e.g. network travel time). Road links for pedestrianisation are ranked and validated through a survey, targeted to experts and representatives of the responsible local governmental authority.
Keywords: pedestrianisation; optimal selection of road links; traffic impacts.
Decision Making on Sea; An Expert System for Risk Assessment in Maritime using Data Mining
by Dimitrios Kokotos, Alkiviadis Kyriakakis
Abstract: This work proposes the prototyping implementation of a dynamic expert system. The essence of the paper is the proposal of prediction of ship accidents. The database used consists of data collected from official investigation reports of the Coast Guard and the validation process of the proposed expert system was based on this data. The real-world data are noisy and have many missing attribute values. A classifier based on C5 algorithm is able to work even in presence of these limitations thus is used for building decision trees. The models are described through the provision of Predictive Model Markup Language. The decision tree models are used in the knowledge acquisition and its representation.
The optimal decision rules estimated the dependency of the most important predictor upon the target variable "Source of accidents". The comparison between two periods shows that accidents due to human error were reduced; this result is in line with the International Maritime Organisation report.
The resulting patterns can be used to gain insight into aspects of shipping safety and to predict outcomes for future situations as an aid to decision-making. The proposed expert system is an essential reference for sea and coastal operations since it is expected to provide valuable decisions for shipping safety.
The optimal decision rules estimated the dependency of the most important predictor upon the target variable "Source of accidents". The comparison between two periods shows that accidents due to human error were reduced; this result is in line with the International Maritime Organisation report.
Keywords: Classification algorithms; Prediction; Ship accident; Maritime Safety; Decision Trees; Data Mining; Off-shore.
Sustainable Development and Morphological Analysis: A Multi-Level Strategic Planning for the Transport Sector
by Maria De Fátima Teles, Jorge Freire De Sousa
Abstract: Societies face complex challenges, which require a harmonious transition to future patterns. A strategic response to reconfigure society should assure the provision of critical resources and the resilience of the socio-technical systems in the long-term. The implementation of a new dominant technology and paradigm in the transport context is a complex process: it is multidimensional, requires seamless integration of various features and entails trade-offs in the decision-making process. The authors use General Morphological Analysis (GMA) as a theoretical framework that supports decision-making in the transition management of transport to a new powertrain technology. This example is just an illustration of a broader representation of all the possible solutions of a large-scale problem as it is the case of any multi-level process of governance, leading to the pursuit of new paradigms. The originality of the paper lies on using a GMA that addresses sustainable challenges in a transport system from a multi-level perspective.
Keywords: sustainable development; multi-level process; integrated decision-making; general morphological analysis; transport.
Multi-criteria location of multi-modal terminals in integrated public transport systems
by Jairo R. Montoya-Torres, Johanna Camargo-Perez
Abstract: A global trend worldwide in people transportation in urban areas is to integrate different transportation modes into the same network so the needs of several stakeholders are considered. This paper proposes a methodology for the location of multimodal terminals in integrated public transport systems considering multiple decision-making criteria for large-sized cities. To validate the methodology, the case of the city of Bogot
Keywords: people transportation; urban; location; multi-criteria decision-making; case study.
Perishable food distribution in the Last Mile, a Multi-objective VRP Model
by Javier Arturo Orjuela-Castro, Juan Pablo Orejuela-Cabrera, Wilson Adarme-Jaimes
Abstract: The delivery of perishable food in mega cities is negatively affected by traffic congestion and long routes resulting into economic losses due to the perishability of fruit, high costs and tardy deliveries to business establishments located in districts. The perishable foods supply chains acting parties are compelled into making decisions on whether they must better their response time, maintain the quality or reduce their costs, in this sense, the necessity to establish models that contemplate various objectives arises. This article proposes a Multi-objective model for the delivery within the last mile of a mega city exemplified in perishable fruits. Our proposal to manage perishability is that of continuous loss, to the extent that the amount of food that deteriorates It is proportional to the amount transported and to the time in which it is transported, which is also affected by the distance traveled, the average speed of travel and the time spent at each stop, an approach not found in the literature review
Keywords: Multi-objective VRP; perishable foods; fruits; traffic congestion in megacities; quality of foods.