International Journal of Information and Decision Sciences (49 papers in press)
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.
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.
Three-Level Multi-criteria Analysis for Measuring the Efficiency of Grant Awarded Research Projects
by Noor Saifurina Nana Khurizan, Adli Mustafa, Hamidah Abd. Hamid
Abstract: This paper is written for the purpose of demonstrating a new way of applying Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) in examining the performance of university funded academic research projects. Three stages of multi-criteria analysis were employed in this study involving AHP, DEA and preferential voting. AHP methodology was employed to construct a four-level hierarchy system to group the evaluation criteria and generate the related priority score. Based on the priority score generated from AHP, a score for the output data was then calculated and standardized. The original DEA model was then used to find the efficiency score for each research grant by solving its multiple objective problems. Later, DEA analysis was conducted using different combinations of output to distinguish the research grant projects that fully utilized its input to consistently produce the output. Further, an alternative way of producing an overall ranking was explored by employing the methodology of preferential voting using DEA. A case study of Universiti Sains Malaysia (USM) funded research projects is presented to demonstrate the application with real life data.
Keywords: Data Envelopment Analysis; DEA; Analytic Hierarchy Process; AHP; Preferential Voting; R&D; Sponsored Research; Higher Education; Efficiency Anlaysis.
Does Doctor's Skills Influence Patient Satisfaction Loyalty and Compliance in Low-Medium Income Countries
by Firas AlOmari
Abstract: In order to achieve quality assurance in healthcare organization, there is a need for unceasing assessment of clinical practice of doctors performance. The purpose of this research paper is to investigate the impact of doctors skills on patients satisfaction, loyalty and behavioural compliance in Syrian healthcare setting. Convenience sampling method was used to select 301 patients from six hospitals, in the Syrian capital Damascus, to complete the questionnaire. The proposed model was created to examine the doctors impact on patient satisfaction, loyalty and compliance. Both exploratory and confirmatory factor analyses are used to verify the scales. The validity and reliability of the proposed model had been confirmed. The results revealed that doctors skills are a multidimensional construct comprised of three dimensions: doctors competence, listening skill and explanation skill. The analysis of the proposed model indicated that doctors skills construct has a significant positive direct effect on patient satisfaction, loyalty and compliance. In addition, the statistical results showed that patient satisfaction has a partial mediating effect between doctor skills and patient loyalty. Besides, results of this study found that satisfied patient with the perceived clinical service does not secure medication compliance; in other words, patient satisfaction does not mediate the relationship between doctor skills and compliance. To the authors knowledge, this is one of very few studies conducted to understand and assess the doctors role on patient satisfaction, patient loyalty and medication compliance in Syrian healthcare setting. The current research introduced several noticeable contributions to the healthcare marketing and management in Syria.
Keywords: doctor's skill; patient satisfaction; patient loyalty; medication compliance; Syrian Healthcare.
Human Decision Making Modelling for Gambling Task under Uncertainty and Risk
by Nimisha Gupta, Mitul Kumar Ahirwal, Mithilesh Atulkar
Abstract: In this paper, modelling of human decision making process and comparison among various Reinforcement Learning (RL) techniques with utility functions has been performed. Iowa Gambling Task (IGT) is used to collect real time data to understand and model the decision-making (DM) process involving uncertainty, risk or ambiguity. Performance of models is evaluated based on their Mean Square Deviation (MSD) value. This helps to predict the probability of the next choice that lead to the selection of the advantageous deck as compared to disadvantageous one. Along with that, the deck selection pattern between male and female with the learning process of the participants were also analysed. By comparing the MSD value of various RL models, it is found that the MSD value of DM model consists of Prospect Utility (PU)Decay Reinforcement Learning (DRI) with Trial Dependent Choice rule (TDC) is best.
Keywords: Human Decision making; Iowa Gambling Task; Reinforcement learning model; Utility functions.
Multi-criteria model for selecting project managers in the public sector
by Fernando Escobar, João Varajão, Nilton Takagi, Ulysses Almeida Neto
Abstract: To assure effectiveness in the management of projects is required that project managers have the right competencies, according to the context and characteristics of each project they are involved. Based on several competencies frameworks (including PMIs PMCDF, IPMAs ICB, APMs CF, and AIPMs PCSPM), this paper proposes a new and unified multi-criteria model to be used as a decision-making tool for selecting the most suitable managers and defining competencies pathways for public sector projects. Expanding the current literature, it is proposed a hierarchical structure comprising weighted elements related to behavioural, management, and contextual/organizational competencies. For researchers, the presented unified model of competences enables a better understanding of the phenomena and can be used to structure further research in other contexts than the public sector. It is also a valuable tool for practitioners and project management offices since it allows comparing the candidates for managing a project using an organized and rigorous process anchored on empirically well-grounded criteria.
Keywords: Project manager; project management; selection; decision; AHP; competencies; frameworks; public sector; Brazil.
Development of optimal Ordering strategy, with power demand and changeable deterioration under allowable delay in payments
by R.P. Tripathi
Abstract: In the present competitive trade world, each business would like to compose extra income by means of less investment. In view of an economic ordering quantity (EOQ) system by variable deterioration our objective is to learn the effect of fixed lifetime items under trade credits. At present scenario in any selling operation broker regularly offers the retailer a permissible delay phase .Some commodities like vegetables, fruits, liquids, pharmaceuticals, volatile liquids deteriorate continuously up to termination dates. This study considers an inventory model for power demand with deterioration has their highest life time. Two different cases are discussed, including a sub case. Mathematical formulation is given for two unlike circumstances to demonstrate the proposed model condensed Taylors series is applied in favor of exponential terms for judging blocked form explanation. Numerical designs and sensitivity investigation are made available to reveal the model.
Keywords: Inventory; expiration; spoilage; trade credit; power demand.
Investigate the causal effect of diversification strategy on risk-adjusted performance using Bayesian additive regression trees
by Zahra Faraji
Abstract: This paper aims to develop a hybrid causal model based on the inverse probability weighting (IPW) and Bayesian Additive Regression Trees (BART) as an advanced machine learning technique. IPW relies on parametric logistic regression model to estimate the propensity scores, however, the required assumptions and pre-specified relationships degrade its application for many real-world problems. We use BART to model the propensity scores to mitigates the limitations of standard IPW model. In addition, we apply Bayesian model to estimate the average treatment effect (ATE) in the pseudo-population instead of simple regression to provide posterior predictive distribution of ATE. Using a simulation study, we show our model corrects the bias and RMSE introduced by the original IPW model and can recover the true ATE. Lastly, we apply the new IPW model to investigate the causal effect of diversification strategy on risk-adjusted performance for U.S. public firms. The results show diversification can help firms to improve their performance even after considering the associated risk.
Keywords: Causal Inference; Diversification strategy; Risk-adjusted performance; Inverse probability weighting; Machine learning; BART.
A GROUP OF REPRESENTATIVE MEASURES OF A SET OF TIME SERIES AND ITS DECISION SUPPORT: A TRIAL FOR DENGUE INCIDENCE DATA OF SRI LANKA
by Chekhaprabha Waduge, Naleen Ganegoda, Darshana Wickramarachchi, Ravindra Lokupitiya, Ganhewalage Lanel
Abstract: Partitioning a longitudinal context of a time series into a set of subseries in a meaningful way is the first temporal concern here. Thereafter, a cross-sectional approach is implemented to design representative series of that set of time series. The series containing point-wise arithmetic means (mean series) is somewhat the simplest choice, when such a representative is required. However, it may not acquire important temporal variations. In this paper, several alternatives are proposed based on a class of measures called Ultimate Tamed Series (UTS). Here, an operation called taming is implemented upon time series and it is non-commutative and non-associative. The taming is carried out with the aid of discrete Haar wavelet, which is subscribed by both point-wise concern and local trend given by an adjacent point. Catering this adjacency is the key temporal manipulation.
UTS measures depend on the order of taming a set of time series adhering series-wise heterogeneity. We trial this with a data set of disease incidence by two predetermined orders as order of occurrence in the timeline (forward) and its reverse order (backward). Order of occurrence yields a UTS that is biased on the pattern and position of data points of earlier series than recent series. The opposite can be observed in its reverse order. We claim this biased nature as a favorable outcome in acquiring long-term trends into a representative series. Two other UTS measures are also presented containing the highest and the least point-wise deviations with the data series. In addition, two supportive indexes are designed in order to understand the variability of concerned UTS measures. Better-informed decisions are possible via proposed architecture of processing time series data.
Keywords: Time series; Haar wavelet; taming; representative series; decision support system.
Proposing a Model for Accepting Core Banking System in Iran Using Fuzzy DEMATEL Technique: A Case Study
by Ameneh Khadivar, Hamideh Nazarian, Sanaz Bodaghi
Abstract: Ever-increasing advancement and expansion of the information and communication technology in the recent decade has led to increased competition in organizations, especially financial organizations. Core banking system implementation project is a time consuming, cost-intensive, and complex task like other investments in information technology. As a result, plenty of core banking system projects have not been successful.
This research proposes a model for accepting the core banking system at Parsian Bank operating in Iran using the fuzzy DEMATEL technique. Using the fuzzy DEMATEL procedures, the involved criteria of a system are separated into the cause and effect groups for helping decision-makers focus on those criteria that provide great influence. Identified from the users' standpoint, factors affecting the acceptance of this system have been prioritized through the questionnaire.
The identified factors for accepting the core banking system in this research are: output quality of core banking system, experience of using the core banking system, voluntariness and motivation in employees to use the core banking system, perceived usefulness of core banking system by employees, perceived ease of use of the core banking system for employees, quality of core banking system, satisfaction with the core banking system, employee resistance to change and use of the new core banking system, and adequate training to employees to use core banking system.
The findings indicate that, three of the influencing factors are identified as critical ones; within the cause group, the criterion of the output quality of the core banking system is the most important factor for accepting the core banking system, whereas the quality of the core banking system has the best effect on the other criteria. By contrast, adequate training for employees to use the core banking system is the most easily improved of the effect group criteria.
Keywords: Core Banking System; Technology Acceptance; Accepting the Core Banking System; Fuzzy Dematel Technique.
A Synergy of Spatial Perspective based Non-Numeric ME-MCDM and Modified Dijkstra Algorithm for Optimal Distribution Route Selection
by Hartrisari Hardjomidjojo, Marimin Marimin, Suprihatin Suprihatin, Rindra Yusianto
Abstract: The optimal distribution routes selection is not only determined based on the shortest distance but needs to consider various alternatives and criteria that are complex and uncertain. Standard mathematical calculations cannot solve complex problems including geographic coordinates (X, Y) and spatial approaches. The contribution of this paper was a new method synergizing advanced non-numeric Multi-Expert Multi-Criteria Decision Making (ME-MCDM) and modified Dijkstra algorithm with spatial perspectives. The selected route was determined by multiplying Distance (D) in the classical Dijkstra algorithm with Alternative Values (AV) from non-numeric ME-MCDM using spatial perspective (S). We argued that spatial perspectives namely topography, road segments, multi-hazard zone indexes, and spatial-temporal congestion need to be considered. In this new method, we provided the ratio values (R) for each spatial variable. The most optimal route (Rs) was determined by calculating the Total Alternative Value (TAV) in each path that was considered conflicting multi-criteria. The smallest TAV value is selected as the most optimal route. The results showed the new method provides a more reasonable and meaningful solution compared with the classical Dijkstra algorithm results. Based on the verification and validation, this new method showed that the optimal route was not always the shortest. So, this new method can be used to determine the optimal distribution route selection which is more suitable for the agro-industrial sector. For further research, this method can be applied to optimize the supply and demand balance.
Keywords: distribution route selection; non-numeric ME-MCDM; modified Dijkstra algorithm; spatial perspectives.
Data Mining based analysis of the Human Activity in healthy subjects using smart phones
by Ankitha S, Sanjay H S
Abstract: Human-Activity-Recognition(HAR) approach provides a valuable insight about their interaction with the surrounding environment. The present work highlights an assessment of HAR using accelerometer and gyroscope sensors embedded in Samsung Galaxy-S2 smartphone to acquire 6 activities namely WAlking(WA), Walking-Upstairs(WU), Walking-Downstairs(WD), SItting(SI), STanding(ST) and Laying(LA) with data mining approaches. HAR data was pertaining to 30 healthy subjects of age 19-48 years, both males and females acquired with an informed consent. Linear Discriminant Analysis (LDA) was used for dimension reduction. Classification was performed with and without LDA using Support Vector Machine (SVM), Multiple Layer Perceptron (MLP), Decision Tree (DT), Extra Tree (ET), K Nearest Neighbor (KNN), Random Forest (RF) and Gradient Boosting Machine (GBM) approaches in python platform. The results showed an increase in accuracy with LDA based dimension reduction. SVM (with C=10, Gamma = 0.001 with RBF kernel) provided the highest accuracy for both the cases (SVM without LDA = SVM with LDA = 96%). However, the highest variation based on LDA was seen in case of DT approach (DT without LDA = 85% and DT-with LDA = 95%). Such predictions can help the subjects with limited locomotion in the field of rehabilitative engineering using Augmented as well as Virtual Reality.
Keywords: Human Activity recognition; Accelerometer; Gyroscope; Linear discriminant analysis; Rehabilitative engineering.
Multiple Criteria ABC Classification: An Accelerated Hybrid ELECTRE-PSO Method
by Ezzatollah Asgharizadeh, Amir Daneshvar, Ehsan Yadegari
Abstract: The objective of inventory management is to make decisions regarding the appropriate level of inventory. ABC classification analysis as the widely used inventory management approach, categorizes inventory items into predefined classes namely A, B and C. The limitation of the ABC management system is that only one criterion is considered, however, as generally emphasized in the literature, the inventory classification is a multi-criteria problem. So, this paper proposed a Multiple Criteria ABC Inventory Classification (MCIC) for the ABC inventory classification. Since, these models cannot handle the qualitative criteria and in many cases the classification problem isnt fully compensatory, this study integrates a non-compensative multi-criteria decision making technique (ELECTRE TRI) with a machine learning algorithm (PSO) to effectively conduct multi-criteria inventory analysis. Since, the application of ELECTRE TRI method requires to determine the preferences of decision makers (DMs) on some parameter values (e.g. prototypes pessimistic intervals and discrimination thresholds), the under consideration criteria and their related parameters are numerous and their interpretation is confusing, especially in large scale problems, the solution process is very complex and time-consuming. Tackling these difficulties, this paper proposed a method to infer all ELECTRE TRI parameters through a procedure using the previously classified inventory items determined by DMs. Then, a hybrid Particle Swarm Optimization (PSO) algorithm applied to induce parameters of ELECTRE TRI. Finally, for accelerating the PSO procedure to find the local and global optima and balance these points, the Variable Position (VP) model is proposed as an exploitation and variable exploration model with new velocity components. The findings indicate that the proposed model maximizes classification accuracy on each inventory dataset. In order to present the validity of the proposed method, it is applied to 6 inventory datasets and the results is also compared with some of the commonly used classification methods from the literature. The results also revealed high applicability of the proposed model to inventory classification problems.
Keywords: Inventory Classification; Outranking relations; PSO; ELECTRE TRI.
Inventory Policies under Fuzzy and Cloud Fuzzy Environment
by Nita Shah, Milan Patel, Pratik Shah
Abstract: This article is an attempt to extend the classical economic order quantity (EOQ) model for deteriorating items under fuzzy and cloud fuzzy environment. Inventory parameters such as holding cost, purchase cost, ordering cost, demand rate and deterioration rate are considered as triangular fuzzy numbers as well as cloud triangular fuzzy numbers to develop fuzzy and cloud fuzzy models respectively. Yagers ranking method and De and Begs ranking methods are used for defuzzification. A comparative study reveals the superiority of cloud fuzzy model over crisp and fuzzy model. Further, numerical example, graphical illustrations and sensitivity analysis are carried out for better understanding of the application of cloud fuzzy approach to inventory optimization problem.
Keywords: Inventory; EOQ; deterioration; cloud triangular fuzzy number.
E-commerce research in developing countries: A systematic review of research themes, frameworks, methods and future lines of research
by Frederick Pobee, Thuso Mphela
Abstract: AbstractrnThis paper presents a systematic review of e-commerce adoption research on developing countries with focus on the classification of literature and their associated themes, frameworks, research methodology over the period of ten years. A total of 151 articles from 35 peer reviewed journals from 2010-2019 were retrieved and used in the analysis. The findings reveal that majority of e-commerce adoption studies on developing countries tend to skew towards trust and satisfaction issues to the detriment of other under researched issue like attitude towards e-commerce adoption. Though there hasnt been a constant increase in e-commerce research on developing countries over the past 10 years, a significant number of published studies used qualitative approach as method of enquiry as compared to quantitative and mixed methodologies. Also, majority of e-commerce studies on developing countries have not been supported with theoretical frameworks and models. As contribution, this paper provides an in-depth analysis of e-commerce adoption in developing countries showing the trends of research themes, methodologies and frameworks. Implications for future research was discussed.rn
Keywords: Keywords: E-commerce; developing countries; research frameworks; methodologies; adoption.
Cybersecurity Antecedents of Trust: Toward OPS adoption in Jordan
by Yazan Alshboul, Nareman Al.Hamouri
Abstract: Online services such as online banking, particularly, the online payment system (OPS), plays an important role in modern life. In developing countries, there is a kind of resistance to adopting OPSs. Therefore, more focus is needed to understand the behavior toward OPS, especially in developing countries. This paper integrates the trust model and the theory of planned behavior and addresses the antecedents of the trust factor in the context of OPSs. Particularly, it focuses on the cybersecurity factors as antecedents to the trust model. We tested our model empirically using data gathered from 200 participants who use eFawateercom system, an online payment system used in Jordan. The results showed that cybersecurity factors like systems security, privacy, and reliability play an essential role in affecting users trust, which has a crucial impact on the attitude toward OPS adoption. This article concluded with implications for academia and practitioners.
Keywords: Online payment system; cybersecurity factors; trust; security; privacy; reliability.
A multi-view approach to multi-criteria decision making
by Francisco Santos, André Coelho
Abstract: Multi-view learning is a field of machine learning that deals specifically with data represented by distinct feature sets (known as views), possibly coming from multiple information sources. In this context, canonical correlation analysis (CCA) stands out as a representative technique, since it allows the automatic extraction of linear correlations among groups of data features in the form of canonical variables. In this paper, inspired by the great success of multi-view learning, we bring about a new perspective to multi-criteria decision making (MCDM), referred to as multi-view multi-criteria decision making (MV-MCDM), which is centered upon the application of CCA to distinct groups of judgement criteria (referred to as criteria views). By resorting to MV-MCDM, one can deal more naturally with multi-view multi-criteria problems than standard MCDM methods. Moreover, our approach enables the estimation of very reliable values for criteria weights via CCA. Another interesting advantage is that MV-MCDM entails the reduction of the dimensionality of the decision matrix by considering only one of the available views. In addition, we show that the MV-MCDM methodology permits the easy multi-view extension of well-known MCDM methods, such as SAW (simple additive weighting) and TOPSIS (technique for order of preferences by similarity to ideal solution). MV-MCDM is also generic enough to allow the adoption of different aggregation methods to generate the overall scores of the alternatives. In this regard, we show that the use of canonical variate correlation coefficients as fuzzy density measurements can make it possible the application of the Choquet integral. Besides, a new heuristic aggregation method based on radar charts is also considered. Finally, a numerical example focusing on the multi-view versions of SAW and TOPSIS demonstrates the applicability of the proposed approach.
Keywords: MCDM; Multi-view Canonical Correlation Analysis; TOPSIS; SAW; Choquet integral.
Discerning the traffic in Anonymous Communication Networks using Machine Learning: Concepts, Techniques and Future Trends
by Annapurna P. Patil, Lalitha Chinmayee MaheshKumar Hurali
Abstract: With the growing need for anonymity and privacy on the Internet, Anonymous Communication Networks (ACNs) such as Tor, I2P, JonDonym, and Freenet have risen to fame. Such anonymous networks aim to provide freedom of expression and protection against tracking to its users. Simultaneously, there is also a class of users involved in the illegal usage of these ACNs. An emerging research topic in the field of ACNs is network traffic classification, as it can improve the network security against illegal users as well as improve the Quality of Service for its legal users. In this study, we review the research works available in the literature relevant to traffic classification in ACNs based on Machine Learning and also present to the researchers the general concepts and techniques in this area. A discussion on future trends in this area is also provided to bring out the future enhancements required in ML-based network traffic classification in ACNs.
Keywords: Anonymous communication networks; Machine Learning; Traffic classification; Tor; Network security;.
Modelling Big Data Analysis Approach with Multi-agent System for Crop-yield Prediction
by Jaya Sinha, Shri Kant, Megha Saini
Abstract: Big data environment in current scenario is dealing with challenges in handling inherent complexity residing in the massive heterogeneous, multivariate and continuously evolving real time data along with offline statistics. The role of big data analytics to analyze such a highly diverse data also plays a significant role in estimating predictive performance of a system. This paper thus aims at proposing an intelligent agent based architecture that coordinates with big data analytics framework to model a system with an objective to improve the predictive performance of system by handling such diverse data. The paper also includes implementing predictive algorithm to predict crop yield in the agricultural domain. Various machine learning analytical tools have been used for data analysis to produce comprehensive and more accurate prediction using the proposed architecture.
Keywords: Multi-agent System (MAS); Big data; Data acquisition; Data analysis; Data storage; Machine learning; Intelligent agents.
The Art of Context Classification & Recognition of Text Conversation using CNN
by Sandeep Rathor, Sanket Agrawal
Abstract: This paper proposes a robust model for recognizing the context of a conversation by using CNN. Initially, preprocessing is performed on the input text conversation. It includes lowercase conversion followed by tokenization, padding, and word embedding. The embedding layer gives out a feature matrix. This feature matrix is passed to a multi-level Convolution Neural Network. The proposed model is designed in such a manner that each CNN reduces the input matrix to half of the input size. Thus, the output of the next CNN layer and the pass of the current CNN layer followed by average pooling can be added. This output is named a global pass and passed to another block of the same architecture. The output from the last CNN layer and global outputs are concatenated and finally, passed into two fully connected layers FC(512) & FC(8). The output from FC(8) gives the probability of conversation to belong to eight contexts. Finally, the context with the highest probability is taken.
Keywords: Context Recognition; Text Conversation; Text Mining; CNN; Machine learning.
In Search of Sustainable Electronic Human Resource Management in Public Organizations
by Reza Sepahvand, Khaled Nawaser, Mohammad Hossein Azadi, Ali Vafaei-Zadeh, Haniruzila Hanifah, Razieh Bagherzadeh Khodashahri
Abstract: Over the past two decades, issues such as environmental degradation, continued marginalization of large groups of people, growth of anti-globalization sentiments, and demand for innovation and creativity in public and private sectors have emerged as prominent global organizational problems. These developments have led to a growing interest in the concept of sustainability, which many Human Resource Management (HRM) researchers believe can enhance HRM capabilities and activities, leading to better organizational performance and competitive advantage. Given the significant impact of Information Technology (IT) on HRM, identification of factors affecting the implementation of sustainable Electronic Human Resource Management (SEHRM) in organizations can result in significant cost reduction and more principled organizational decision making. The present study aimed to identify and evaluate the factors affecting the implementation of sustainable EHRM in public organizations using type-2 fuzzy FMEA. After reviewing the research literature and surveying experts, 29 factors in three dimensions of social, environmental and economic were identified. After designing and distributing the questionnaire among experts, type-2 fuzzy AHP was used to determine the weight of risk factors of FMEA (occurrence probability, severity, and detectability). The identified factors were then ranked using type-2 fuzzy TOPSIS. The results showed that the social dimension is the most important dimension for the implementation of sustainable EHRM in the study area. The critical individual factors in order of significance for this implementation were found to be EHRM Reengineering, Green Performance Evaluation, IT Infrastructure, ECRM, Electronic Customer Satisfaction, Employee Safety, and Electronic Services.
Keywords: Sustainable Electronic Human Resources Management; FMEA; Type 2 Fuzzy TOPSIS; Type 2 Fuzzy AHP.
A Novel approach for energy management of Authentication Sensor Nodes in WSN
by Ramkrishna Ghosh
Abstract: In Wireless Sensor Networks(WSNs), sensor nodes(SNs) are
tremendously susceptible to various security attacks since these are typically
placed in adverse surroundings. Those power and limited hardware resource SNs,
without appropriate protections, might be adjusted by attackers. In this paper,
we represent an innovative energy efficient interval type 2 fuzzy robust selection
of verification nodes in WSNs.In particular, the focus is achieving optimal
energy savings for critical applications like monitoring applications for public
security, border control enforcement and so on.A type-2 Fuzzy Logic(T2FL)
based controller is suggested to aid superior power preservation in the existence
of 2 vital kinds of attacks: false-report injection (FRI) and false-vote injection
(FVI) attacks which drain notable quantities of energy and exhaust sustainable
records when contributing the similar immense filtering supervision of the
Probabilistic Voting Filtering Scheme (PVFS).We have chosen T2FL approach
for variation the values of the Filtering Nodes Substitution(%),Upstream Cluster
Head Exclusion selecting suitable type-2 fuzzy descriptors such as Residual
Energy level(%), Compromise Attack Attempts(%) and Verification Nodes
Proximity(%).Type-2 Fuzzy inference system (Mamdanis rule) is applied to
pick the appropriate Filtering Nodes Substitution(%) and Upstream Cluster Head Exclusion computation. We demonstrate that our suggested model attains
superior energy saving in the attending of two the aforesaid safety risks whilst
maintaining the identical immense filtering power of the PVFS. Effectiveness of
the recommended model is made by means of statistical assessment and multiple
Keywords: Wireless sensor networks; Probabilistic Voting Filtering Scheme;
Residual Energy level; Compromise Attack Attempts; Verification Node’s
Proximity; Fuzzy Inference System; Interval Type-2 Fuzzy Logic.
A Multi-Criteria Decision Support System for the Assessment of Cities based on Air Quality Indicators
by Supriya Raheja, Rakesh Garg, Aakash Gupta, Geetika Munjal
Abstract: A deterministic decision support system is developed for the assessment and ranking of various cities based on the air quality indicators in this research. The present study models such assessment of cities as a multi-criteria decision making (MCDM) problem due to the involvement of multiple air quality indicators. Further, to solve the present assessment problem, a hybrid MCDM approach, namely, Entropy- Evaluation based on distance from average solution (EEDAS) is implemented by integrating two well-known approaches such as Shannon entropy and evaluation based on distance from average solution (EDAS). Shannon entropy approach is used to calculate the priority weights of the air quality indicators whereas the EDAS method is used to get the comprehensive ranking of the cities based on the identified air quality indicators.
Keywords: Air Pollution; Air quality indicators; Multi-Criteria decision making (MCDM); Entropy; EDAS; Decision support system.
Analyzing Impact of IT Investments on Banks Performance using Multi-Stage DEA
by Ankit Mehrotra, Reeti Agarwal
Abstract: The paper studies the impact of investments made in information technology (IT) on a firms performance. The paper makes use of multi-stage Data Envelopment Analysis (DEA) to find out the marginal impact of IT investment on firms performance. The paper uses a three-stage DEA approach by introducing a new variable i.e. communication expenses to study what bearing IT has on a firms efficiency attainment. The paper also extends existing literature related to the indirect influence of IT on a firms performance by making use of a non-parametric approach, DEA. To attain the aims of the study, data of a number of banks was considered for a period of five years. The study introduced IT variables at two stages of the analysis to analyze its marginal effect on efficiency outcome.
Keywords: Data Envelopment Analysis; DEA; performance; efficiency; information technology; bank; marginal effect.
Compromise ranking based on superiority, inferiority and Euclidean normalized similarity metrics: The ESIASP method
by Moufida Hidouri
Abstract: Xiaozhan Xu introduced in 2001 the superiority and inferiority ranking (SIR) methods called SIR.TOPSIS and SIR-SAW. The SIR.TOPSIS method has two quite different variants, which we call here SR.TOPSIS and IR.TOPSIS. What is noteworthy is that each variant gives attention only to one type of indexes rather than both types, which may result in questionable ranking results because both variants ignore available relevant indexes. In addition, the SIR.TOPSIS variants (ranking) indexes have been based on the TOPSIS relative closeness coefficient, which is inflexible in the sense of not being affected by the relative importance of separations of each alternative from positive ideal solution and negative ideal solution.
The SIR.SAW method ignores the relative significances of superiority flows (overall degrees of support for alternatives) and inferiority flows (overall degrees of support against alternatives).
It is therefore worthwhile to introduce a new ranking method to overcome the flaws seen in Xus SIR methods. The crisp method proposed in this paper, called the Evaluation based on Similarity to Ideal Augmented Superiority Profile (ESIASP) method, fully exploits all the available relevant indexes and takes account of their relative importance.
Finally, a supplier selection problem is given to demonstrate the proposed method. A comparison of rankings produced by the ESIASP method, SIR.TOPSIS and SIR.SAW variants shows that the suggested method is a relevant and implementable alternative to Xus SIR methods.
Keywords: crisp method; inferiority; SIR.SAW; SIR.TOPSIS; superiority; supplier selection.
Eliciting individual risk attitudes different procedures, different findings
by Sven Grüner, Norbert Hirschauer, Felix Krüger
Abstract: We compare three procedures for eliciting individual risk attitudes: Holt-and-Laury (2002), Eck-el-and-Grossman (2002), and the general willingness-to-take-risks question of the German socio-economic panel. Using a within-subject design, we carry out a classroom experiment with stu-dents who are enrolled in the degree programs Physics, Computer Sciences, Agricultural Scienc-es, Law, and History. We find that the risk attitudes as measured by the three procedures diverge substantially. This poses a serious challenge to the validity of these measurement instruments.
Keywords: Risk-attitude; Eckel-and-Grossman procedure; Holt-and-Laury procedure; self-reported willingness-to-take-risks; bounded rationality.
Evaluation and selection of a casting process using interval type-2 fuzzy analytical hierarchy process
by Devesh Sahu, Atul Chakrawarti
Abstract: Selection of a casting process is a kind of multi criteria decision making process, in which several factors like Cost, material utilization and flexibility, geometrical complexity and flexibility and dimensional accuracy and surface finish. As cost is considered as one of the prime criteria in decision making, so it is further sub categorised into three sub criteria i.e. tooling cost, equipment cost and labour cost. Past researchers have used multi criteria decision making technique like analytical hierarchy process along with type-1 fuzzy in the evaluation and selection of casting process, which is successful in handling the fuzziness of the system, but are unable to address the issue of uncertainty associated with decision making. So, in this paper interval type-2 fuzzy analytical hierarchy process is used to evaluate and select best casting process, which is capable of handling uncertainty associated with decision making. A numerical illustration is solved using the interval type-2 fuzzy analytical hierarchy process and it is found that investment casting is evaluated as best casting process for cam carrier and is ranked number one.
Keywords: Casting; MCDM; decision support system; type-2 fuzzy; AHP.
Combined Application of Condition Based Maintenance and Reliability Centred Maintenance using PFMEA and Lean Concepts A Case Study
by Claudia Carvalho De Oliveira, José Cristiano Pereira, Nélio Pizzolato
Abstract: Productivity is an essential element for competitiveness, helping companies to be better prepared for the future, given all of today's challenges. Such productivity needs to be clearly confirmed by operational results. This paper covers assets maintenance effectiveness, one of the productivity evaluation components increasingly gaining business attention. The proposed methodology helps the achievement of the required effectiveness on asset maintenance, by combining the concepts of Reliability Centered Maintenance and Condition Based Maintenance, also supported by PFMEA, a successful risk management strategy, and Lean Manufacturing practices. A case study was conducted in a high technology enterprise, combining those referred concepts. The implementation process used Minimum Viable Product strategy. The main concept is based on suitable equipment inspection and diagnosis practices which guides interventions and cleverer behaviour towards asset management. As a result, in the first year of implementation, 5 to 10% cost reduction was obtained together with a significant increase in equipment availability which has reached a level of 95%+.
Keywords: Reliability Centered Maintenance; RCM; Condition Based Maintenance; CBM; PFMEA; Lean Maintenance; asset Management; MVP.
Special Issue on: ICDSST - PROMETHEE DAYS 2018 Decision Support for Transport Methodologies, Tools and Applications
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.
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.
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.
Special Issue on: ETMS2018 and ETMS2019 Performance Analysis and Evaluation in the Business Environment
Analysis of the relationship between sustainability and software performance
by Bersam Bolat
Abstract: Sustainability problems are getting more and more critical and increasingly threatening human life day by day. Software, which is developing rapidly and entering into every aspect of our lives, is one of the most fundamental components of the technological society. The widespread use of software applications and limited natural resources have led researchers to focus on research that will ensure sustainability in the software development process. In this study, we conducted a questionnaire study concerning the sustainability factors that affect the software development process. Then the effect of these factors and the level of education, age, and experience of the people involved in the software development process on the software performance was investigated. As a result, it has been determined that the factors affecting the software development process in terms of sustainability and the descriptive attributes of the individual have an effect on software performance.
Keywords: Sustainability; software development process; software performance; path analysis.