International Journal of Information and Decision Sciences (43 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.
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.
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.