International Journal of Information and Decision Sciences (32 papers in press)
Measuring Productivity of Steam Power-Plants using Uncertain DEA-based Malmquist Index in presence of Undesirable Outputs
by Kaveh Khalili-Damghani, Elham Haji-Sami
Abstract: Energy generation are mixed with production of emissions, called undesirable outputs. Moreover, the values of inputs and outputs of criteria are not deterministic in real productions and usually mixed with a great amount of uncertainties during planning horizon. So, measuring the productivity in presence of uncertainty and undesirable outputs is not a trivial task. In this paper, an uncertain data envelopment analysis (DEA)-based Malmquist productivity index (MPI) is developed in presence of undesirable outputs to assess the productivity of production. The theoretical properties of proposed models are discussed. The proposed method is applied on real case study in 10 steam electricity power-plants. Moreover, the changes in technical efficiencies and changes in technology during multiple periods which influence productivity are sensed using proposed approach. The regress and progress of a power-plants is demonstrated during planning horizons and the cause of these are also illustrated.
Keywords: Uncertain Data Envelopment Analysis; Malmquist productivity index; Energy Planning; Energy Productivity; Sustainable Energy Planning; Steam Power-Plant
Data Quality Assessment Using Multi-Attribute Maintenance perspective
by Mustafa Aljumaili, Ramin Karim, Phillip Tretten
Abstract: The paper proposes a model for Data quality (DQ) assessment in maintenance. Data has become an increasingly important aspect to many firms and most of the maintenance planning and implementations are based on data analysis. Poor DQ reduces customer satisfaction, leading to poor decision making, and has negative impacts on strategy execution. Several studies of DQ in different operational areas exist, but few address the assessment process. To improve DQ as well as to evaluate the current status, DQ needs to be measured following the fact that what can be measured can be improved.
A measure for DQ could be an important support for decision makers. Multi-criteria decision making (MCDM) methods can provide a framework for DQ assessment, howver, they are not used in literature for DQ assessment. In order to assess DQ, the attributes or KPIs need to be defined and their hierarchy should be designed. After defining these attributes, the assessment model is proposed to evaluate these attributes. A case study on a data provided by the Swedish Transport Administration (Trafikverket) is also presented in this paper.
The study shows that using MCDM methods could provide qualitative estimation for the quality of DQ attributes. That evaluation of DQ may help decision-makers to make the right decision.
Keywords: Data Quality; Information; Maintenance; eMaintenance; Attributes; Assessment.
The Factors with Interval Target Levels in Data Envelopment Analysis
by Zhang Mengni, Zhang Mengying
Abstract: In reality, there widely exist a kind of factors have interval target levels, such as blood pressure and gender ratio. Traditional data envelopment analysis (DEA) holds that higher output levels and/or lower input levels mean better efficiency scores to DMUs. Differing from traditional DEA, the factors are demanded to achieve the interval target levels for better performance. Above the upper bound value or below the lower bound value of the interval both indicate inefficiency. However, the studies about these factors are very few in current papers and traditional DEA is limited to deal with them. Some researchers have discussed the case of factors with fixed target values. Motivated by this, the paper gives a supplement to the situation that all target levels are able to reach by DMUs. Furthermore, we explore the study that factors with interval target levels though formulating new production possibility sets based on previous papers. Our study on factors with interval target levels has great advantages in decision-making and applications. For example, they are much more flexible and inclusive than fixed target levels. Besides, they are applicable to many areas, such as education and resource allocation. Decision makers can use our studies on interval target levels to develop reasonable goals in practice. Graphs and examples are given to illustrate our thought.
Keywords: Data envelopment analysis (DEA); Target levels; Interval; Production possibility set; Efficiency
A Hybrid Multi-Criteria Assessment Framework to Prioritize Power Generation Technologies in Iran
by Ehsan Noorollahi, Dawud Fadai, Seyed Hassan Ghodsipour
Abstract: This paper develops a hybrid multi-criteria assessment framework to determine Irans energy status and prioritize different alternatives of power generation technologies based on both renewable and non-renewable resources with emphasis on sustainable development criteria. This issue is investigated by a hybrid model named as FANP-BOCR that integrates various concepts including analytic network process (ANP), benefits, opportunities, costs and risks analysis (BOCR) and fuzzy sets theory. Comprehensive analysis of various criteria including four strategic criteria of economic, environmental, social, political & security of supply and 17 sub-criteria under benefits, opportunities, costs and risks sub-network is done to evaluate 12 identified alternatives as the most suitable power generation technologies in Iran. The calculation has been done by Super decision software. The obtained results indicate the differences with past researches and official reports, because of considering the factors such as sustainable development, political condition and security of supply. The results show that, in the benefits, opportunities and costs networks, the most preferred three alternatives are hydroelectric, wind and natural gas combined cycle (NGCC). But in the risk network; NGCC, natural gas combustion turbine (NGCT) and natural gas steam turbine (NGST) have the highest priority.
Keywords: ANP; BOCR; Fuzzy; Renewable Energy; Strategic Planning; Sustainable Development;
A Review of Semantic Similarity Approach for Multiple Ontologies
by Nurul aswa omar, Shahreen Kasim, Mohd Farhan Md Fudzee
Abstract: Measuring semantic similarity between concepts is an important step in information retrieval and information integration which requires semantic content matching. Semantic similarity has attracted great concern for a long time in artificial intelligence, psychology and cognitive science. Many methods have been proposed. This paper contains a review on the state of art approaches including structure-based approach, information content-based approach, feature-based approach and hybrid-based approach. We also discussed the similarity according to their advantages, disadvantages and issues related to multiple ontologies. Besides that, we also concentrated on methods in feature-based approach which we will be using as a mechanism to measure the similarity for multiple ontologies.
Keywords: Semantic similarity; feature-based; ontology; multiple ontology; cross ontology; heterogeneous sources.
Measuring Economic and Environmental Efficiency for Agricultural Zones in Iraq Using Data Envelopment Analysis
by Ibrahim Chaloob, Razamin Ramli, Mohd Kamal Mohd Nawawi
Abstract: Data envelopment analysis (DEA) is a non-parametric linear programming based on method for evaluating performance of similar production units such as agricultural firms. Although the method is already extensively applied in many areas of economics, its use in environmental economics and related fields is still limited. The productivity of the agriculture sector in Iraq has yet to reach an acceptable level to control resources and increase production to meet the modern century requirements. The concept recognizes the need to simultaneously raise yields, increase input use efficiency and reduce the negative environmental impacts of farming systems to secure future food production and to sustainably use the limited resources for agriculture. Accordingly, this paper proposes a novel approach to measure using DEA to evaluate five zones in production strategic crops. The significance of this objective lies in the fact that some of the zones have limitations while others adversely impact their environment. This paper also employs a model to determine the efficiency of one over zone over the others and to improve optimal mix of the resources and paths to improve the index of technical efficiency and eco-efficiency. Incorporating provision of environmental goods as one of the outputs of the farm and reducing environmental pressures are also outlined.
Keywords: Strategic Crops; Data Envelopment Analysis; Technical Efficiency; Economic Efficiency; Environmental Efficiency and Eco-Efficiency; Undesirable Output.
Computational MADM Evaluation and Ranking of Cloud Service providers using Distance Based Approach
by Rakesh Garg, R.K. Garg
Abstract: The advancements in the information technology have emerged a new approach in the field of distributed computing referred as cloud computing that has garnered tremendous popularity in the short time span. Cloud computing is one type of internet based computing in which the service providers provide the various services and resources in the sharable mode to the cloud customers on demand. As the result of the rapid growth of this approach, most of the IT companies such as IBM, HP, Microsoft, Amazon, Google have started to offer various cloud services to the users. Due to the huge availability of the cloud service providers, it becomes extremely challenging from the customers point of view to select the most suited cloud service provider (CSP). In this research, the problem of the CSP selection is formulated as multi-attribute decision making problem and distance based approximation (DBA) method is anticipated to solve the problem. In order to validate the applicability of the proposed method, the results are compared with the well known methodology, namely Analytical Hierarchy Process (AHP) and Fuzzy-AHP.
Keywords: Multi-attribute decision making (MADM); DBA; Selection indexes; Cloud service provider (CSP).
Geo-Statistical Index for Reshaping the Pattern of Households Emerging over the Low Lying Areas of the Colombo Metropolitan Region
by GPTS Hemakumara, Ruslan Rainis
Abstract: The Colombo Metropolitan Region in Sri Lanka consists of land of which about 20% is taken up by low lying areas. With the CMR forming the economic hub of the entire country, its low lying areas have been undergoing drastic changes over the recent decades due to several factors. This study examines how individual households have been emerging across the low lying areas during the period 2005-2012 in the core study area of CMR and also the process through which they have gradually established themselves as either stable or unstable households. Mass manipulation of Geo-spatial factors in innumerable land plots has inevitably led to an increase in harmful environmental effects in the region, such as flooding and micro climate changes because the collective strength of human interference in the region is very high. Hence, in this study, it is attempted to build a Geo-spatial model that can be used as a guiding index to help understand how low lying area conversion caused by the unending process of individual households emerging in the CMR has brought about these changes. The typical individual household plot has been chosen as the unit of analysis. Information from 294 households has been collected from the core study area and the data tested with a logistic regression model. The model indicates an accuracy of about 92.2% together with high significance levels for 8 variables out of the total 19 variables. Predicted probability value of each housing plot mapped with GIS can be seen with the spatial distribution displayed clearly. Predicted probability value of each household indicates the conversion ratio correctly, based on the hypothesis of this study. The Geo-spatial information system and the generated maps can be used as guides and index to monitor, manage and shape the low lying areas during urban planning stages. Further, results of this study suggest that the conversion ratio must be consistent with the planning bodys objectives together with the publics aspirations for these areas.
Keywords: Geo-Statistics; Low Lying Areas; GIS; Spatial Logistic Model; Spatial Index; Urban Planning; Urban Housing.
Multi Criteria Decision Making Approach for Evaluation of Supplier Performance with MACBETH Method
by Gökhan Akyüz, Ömür Tosun, Salih Aka
Abstract: Supply chain consists of all the processes from obtaining the inputs to shipping the final goods to customers. To survive in a competitive environment firms must efficiently manage this chain, minimize the supply risk, decrease their costs, and optimize their inventory level and response customer demands quickly. Measuring and evaluation of supplier performance is also as important as selecting the right supplier. In this study, a multi criteria decision-making method is proposed ranking the suppliers of an international company in Turkey. In the proposed model quantitative criteria (acceptable product rate, major fault, return rate and delivery performance) and qualitative criteria (production flexibility, capacity management, reliability, communication and ecological awareness) are used and it is solved with MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) method. Performances of the suppliers are ranked with M-MACHBET software and sensitivity analysis are also given to discuss further the solution.
Keywords: MACBETH; supplier performance; multi-criteria decision making.
Population Size vs. Number of Crimes - Is the Relationship Superlinear?
by Yu Sang Chang, SungSup Brian Choi, Jinsoo Lee, Won Chang Jin
Abstract: Do large cities suffer from an even greater incidence of crime? According to the Urban Scaling Theory, the number of crimes committed may follow a superlinear relationship as a function of the population size of city. For example, if the population size increases by 100%, the incidence of crime may increase by 120%. We analyzed a total of 11 types of crimes which had occurred in about 250 cities with more than 100,000 inhabitants in the United States during the period of 1995-2010. We found that the relationship between the number of crimes counts and the population size of cities have followed a superlinear power function without exception in all 176 cases. However, significant variations exist among the superlinear relations by types of crime. We also found that the values of scale exponents display time-invariant pattern during the 16-year period.
Keywords: Crime counts; Population size of city; Superlinear relationship; Time-invariant distribution; Sublinear relationship.
Improving QoS in Real-Time Data Warehouses by using Feedback Control Scheduling
by Issam Hamdi, Emna Bouazizi, Jamel Feki
Abstract: Nowadays the update frequency for traditional data warehouses cannot meet the objectives of real-time data analysis relying on data freshness. To alleviate this problem, the Real-Time Data Warehouse (RTDW) technology has emerged. A RTDW allows decision makers to access and analyze fresh data as fast as possible in order to support real-time decision processes. The RTDW must often deal with transient usage charges, due to the unpredictability of access to data. The purpose of this paper is twofold: to maintain the behaviour of the RTDW at a stable state, as well as the reduction of the number of transactions responsible for not meeting their deadline. Moreover, we focus on optimization techniques to speed up query processing; in particular, a query response time optimization and storage space optimization. This paper proposes our FCSA-RTDW architecture (Feedback Control Scheduling Architecture for RTDW) which deals with Quality of Service management by optimizing the resources used and reducing significantly the RTDW overloads.
Keywords: Real-Time Data Warehouse; Real-Time Transactions; Quality of Service; Materialized views; Data partitioning.
DEVELOPMENT OF DECISION SUPPORT SYSTEMS THROUGH THE CONTRADICTIONS OF INFORMATIONAL SOCIETY
by Andrey Larionov
Abstract: In this article authors have investigated the development of existing methods and computer applications used to support decision-making at different levels of a corporate management to set up controlling systems. An increased number of challenges when doing business and inadequate management reaction leads to accumulation of contradictions and issues which could be solved by using Decision Support Systems (DSS) - the set of tools that support the process of decision making. Development of the informational systems and applications, globalization of economy, and organizational maturity create background to new tools and methods used by DSS. Informational society sets up pre-requisites to the learning module incorporated into DSS structure to help to identify competency of the user, give recommendation to the more advanced use of it and allow to share experience collected by other users. Authors have presented original classification of the DSS types and stated paradigm of DSS development in informational society in the future.
Keywords: Operational decisions; Tactical decisions; Strategic decisions; Decision maker; DSS; thinking processes; settlement systems; diagnostic systems; monitoring systems; DSS of calculation; DSS Settlement; diagnostic DSS; Approximate reasoning (or expert) DSS; explicit knowledge; implicit knowledge.
A data fusion approach for business partners selection
by Leonilde Varela, António Castro, Rita Ribeiro
Abstract: Strategies of market diversification push companies to provide novel products and services to customers, belonging to new geographic and demographic segments. Additionally, market development strategies targeting non-buying customers in selected segments or new buyers in new segments may be paired with increased product diversification and improved business agility. To fulfill the requirements associated with manufacturing, a wider range of products and increased customized demands imply having a wider set of competences available. Most companies find it increasingly difficult to have all required competencies in their internal structures; therefore, they need to rely on strategic business partnerships and suppliers to be successful. In this paper, we discuss a data fusion decision approach for supplier and business partner evaluation, which includes the past, current and forecast information about business partners. This approach may prove vital for companies to establish strong collaborative business networks.
Keywords: Collaborative Networks; Supplier evaluation; business strategies; dynamic multicriteria.
A comparison of Generalized Maximum Entropy and Ordinary Least Square
by G.R. Mohtashami Borzadaran, M. Sanei Tabass
Abstract: The Generalized Maximum Entropy (GME) estimation method is based on the classic maximum entropy approach of Jaynes (1957). It has the ability to estimate the parameters of a regression model without imposing any constraints on the probability distribution of errors and it is robust even when we have ill-posed problems. In this paper, we simulate two sets of data from regression model with different distribution for disturbance, standard normal and Cauchy distributions respectively. For this data set, regression coefficients are obtained by GME and OLS methods and these techniques are compared with each other for some sample sizes. Moreover, we have used some prior information on parameters to obtain GME estimators. The estimation results of GME in the case of non-normal distribute, are discussed here.
Keywords: Regression model; Generalized Maximum Entropy; Monte Carlo experiment; Ordinary Least Square.
Fuzzy Rule Base Optimization using Genetic Algorithm for Mobile Web Page Adaptation
by Neetu Narwal
Abstract: There is a global rise in use of mobile devices like mobile phones, PDA, palmtop etc. for web browsing. Web page usually includes the scrolling that makes web browsing time-consuming. In this work we used genetic algorithm based fuzzy inference system and utilized the power of genetic algorithm to optimize the fuzzy rules base web content classification. The content of the web page is partitioned into blocks and applies the genetic based fuzzy inference system to discriminate the main block. The filtered main blocks are then reorganized on the device. As a result of our approach the mobile web user is presented with the filtered web page content without noise which results in persistent content, fast accessing, and better utilization of limited space. We implemented the system and result shows that the hybrid genetic based fuzzy inference system provides better classification accuracy (93.57%) as compared with Fuzzy Inference System (78.55%) accuracy of classification.
Keywords: Genetic Algorithm; Fuzzy Inference System; Web Page Visual Blocks.
The impact of using new significant reference point with TOPSIS methods: study and application
by Zhor CHERGUI
Abstract: In this paper, the impact of Pareto optimality concept on revised TOPSIS method is studied. In particular, we study theoretically the cases in which a preference relation changes when delimiting the choice of the best alternative(s) in an efficient restrictive area. In order to define the most reliable approach a comparative study is established. On this basis, an accurate new method called TOPSIS Nadir is introduced. Furthermore, an adaptation for Interval data area is carried out in which we discuss some forms of normalization. By following the same steps of the TOPSIS methods for Group Decision Makers, we develop and compare two new procedures.
Keywords: Group Decision Makers; TOPSIS methods; Reference points; Crisp data & Interval data; Forms of normalization.
Fuzzy classification as a decision making problem in hesitant environments
by Mahdi Ranjbar, Ali Vahidian Kamyad, Sohrab Effati
Abstract: This paper presents a new approach for fuzzy classification in the hesitant environments by decision making process. Our intention of the hesitant environments is situations, which there are different evaluations of experts for one problem. Typically, the classifier learns to predict class labels using a training algorithm and a training data set, when the training data set is not available, a classifier can be designed from prior knowledge and expertise. In this paper, we focus on cases that a classifier can be designed by knowledge of experts while each expert can classify data with a feature, independently, by linguistic terms. In this paper, we assume the classification task as a decision making problem in which, each feature as an attribute, each class as an alternative and each expert as a decision maker are considered. In the new classifier, we can use different score functions and aggregation operators in hesitant fuzzy sets for fuzzy classification in various viewpoints. Finally, our new approach is applied to a practical problem in economics, then for validation of the proposed model, we use Iris data from the UCI repository.
Keywords: Decision making problem; Fuzzy classification; Hesitant fuzzy set; Aggregation operator; Score function.
Inverse fuzzy soft set and its application in decision making
by Nasruddin Hassan, Ahmed Khalil
Abstract: Molodtsov introduced the theory of soft sets, which can be seen as a new mathematical approach to vagueness. In this paper, we introduce a new soft set called an inverse fuzzy soft set, along with its properties, characteristics, and operations. Then we construct an algorithm using max-min and min-max decision of inverse fuzzy soft set for a fuzzy decision making problem. Finally, we apply the algorithm to two decision making problems to illustrate its applicability. It is
shown that our proposed approach is viable and provide decision makers a more mathematical insight before making decisions on their options.
Keywords: algorithm; decision making; inverse soft set; inverse fuzzy soft set.
An interpretive structural modelling of enablers for Collaborative Planning, Forecasting and Replenishment implementation in high-tech industries
by Farhad Panahifar, Sajjad Shokouhyar
Abstract: The Collaborative Planning, Forecasting and Replenishment (CPFR) initiative is an increasingly popular approach that helps firms better collaborate with others within supply chain and coordinate activities to serve customers with improved service level and precise demand forecasting. It is highlighted in the literature that firms for successful CPFR implementation need to identify its Critical Factors (CF) consisting of enablers and barriers. Thus, the aim of this paper is to identify effective enablers and their relationships which enable firms to successful implementation of CPFR through the development of a structural model. To complete this task, ISM approach is applied by following a set of structured steps with a group of CPFR experts from industry/academia and Matrice dImpacts Crois
Keywords: CPFR; implementation enablers; supply chain management; collaboration; high-tech industries.
Pressure and Ethical Decision-making
by Cheryl Stenmark, Crystal Kreitler
Abstract: Performance pressure degrades performance on many types of tasks (Beilock & Carr, 2001). Mounting evidence, however, suggests that pressure may not affect ethical decision-making (EDM; Removed for review et al., 2010; 2011). It is important to determine whether pressure impacts EDM, so that training interventions can address pressure effectively. For the present study, participants analyzed an ethical dilemma using a cognitive tool (ACED IT), expressive writing, or a control task, and their decisions were compared for participants in high and low pressure conditions. Results revealed that The ACED IT group performed better on EDM indices than did participants in the other groups. Neither the main effect for pressure nor the interaction of the two IVs was significant with regard to the cognitive processes involved. Pressure did, however, have a significant main effect on perceptions of the problem, as measured by the PMIS. Implications of this pattern of results is discussed.
Keywords: ethical decision-making; cognitive tool; ACED IT; pressure; moral intensity.
Spatiotemporal Assessment of Water Quality in the Distribution Network of City of Sharjah, UAE
by Maruf Mortula, Kazi Fattah, Tarig Ali, Alaeldin Idris, Mayyada AlBardan
Abstract: Maintaining a healthy water distribution network (WDN) is key to providing good quality services to the consumers in a sustainable manner. WDN in the City of Sharjah, United Arab Emirates (UAE), has more than 3000km of pipeline and receives water from different sources. Understanding the variation of water quality over time is critical to appropriate management. The objective of this paper was to assess the variability of the water quality in Sharjah WDN. Monitoring data for residual chlorine, iron, and fluoride were collected from 46 different locations throughout the distribution system. Graphical and GIS-based analyses were conducted to understand the temporal (for three source waters and three locations in the WDN) and spatial variability (all locations) of water quality throughout the distribution network. Temporal variations indicated seasonal water quality variations throughout the three-year period (2012-2014). The spatial variability indicated that the old part of the city was susceptible to water quality degradation.
Keywords: water distribution network; water quality; infrastructure integrity; geographic information system; spatial variability; infrastructure management.
A non-stationary NDVI time series modelling using Triplet Markov Chain
by Ali Ben Abbes, Imed Riadh Farah, Vincent Barra
Abstract: Nowadays, vegetation monitoring using remotely sensed data is an
important far-reaching real-world issue.The main purpose of this study is to
build a Triplet Markov Chain (TMC) to model and analyse vegetation dynamics
on large scales using non-stationary Normalised Difference Vegetation Index
(NDVI) time series.TMC is a generalisation of Hidden Markov Models (HMMs),
which have been widely used to represent Satellite Time Series Images but
which they proved to be inefficient for non-stationary data. The TMC model
proposed in this paper overcomes this limit by adding an auxiliary process
which allows modelling non-stationarity. In order to assess the performance of
the proposed model, experimentation is carried out using Moderate Resolution
Imaging Spectroradiometer (MODIS) NDVI time series of the northwestern
region of Tunisia. The TMC model is compared to standard HMM and Seasonal
Auto Regressive Integrated Moving Average model (SARIMA) and proved to
achieve the best performance with an overall accuracy prediction rate of 92.8%
and a kappa coefficient of 0.885.
Keywords: NDVI Time Series; Vegetation Dynamics; TMC; HMM; Non-Stationarity; Remote Sensing.
OFFICE LOCATION SELECTION BY FUZZY AHP AND VIKOR
by TAYFUN ARAR, SERHAT KARAOĞLAN, CEREN DİRİK
Abstract: In a globalized business world, reaching the basic goal of companies which is profit maximization has become such competitive for both manufacturing and service firms. While reaching this basic goal, increasing income is not sufficient, but also minimizing the costs is required. One of the long-term cost decisions, location selection, needs to be considered in detailed by business firms. Especially for a firm in service sector, there are other goals and responsibilities such as satisfying permanently changing needs and expectations of customers. As the wedding sector has a growing share in the economy, the choice of office location for companies operating in this sector has gained great importance. In the light of this purpose, there are some criteria in location selection decision for a business firm which operates in wedding sector. In this research, the criteria used in office location selection have been chosen by literature review and experts views. These criteria are weighted by Fuzzy AHP (Analytic Hierarchy Process). Then, using the weighted criteria, the best option among the alternative offices from different locations is chosen by VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) technique.
Keywords: Office Selection; Location Selection; Wedding Sector; Fuzzy AHP; VIKOR.
A novel architecture based on fuzzy cognitive maps and holonic systems for decision making in a cooperative context
by Asma Maziz, Nacereddine Zarour
Abstract: Ensuring consistency and good decision making is one of the most topical problems in an information system; it becomes more difficult in a cooperative context. In this paper, we propose an architecture based on fuzzy cognitive maps (FCM) tool and holonic multi-agent paradigm that enhance the decision making process in cooperative information system (CIS). Furthermore, the concept of ontology is used for semantically enrich our architecture. We modeled each sub-CIS by a holonic agents where everyone used a FCM for a more precise analysis of complex dynamic system decisions. This group will try to make a collective decision to solve any given distributed problem. To put our approach into practice, we considered road safety field to see how to educate people in order to reduce the fatal accidents number. Finally, we validated our proposition through experiments to show how it improves the decision making process in a cooperative context.
Keywords: Decision Support; Cooperative Information Systems; Fuzzy Cognitive Maps; Multi-agent Systems; Holonic systems; Ontology; Road safety.
Special Issue on: DASA'16 Decision and Logistics
Modeling Time Complexity of Micro-Genetic Algorithms for Online Traffic Control Decisions
by Ghassan Abu-Lebdeh, Kenan Hazirbaba, Omer Mughieda, Bassant Abdelrahman
Abstract: Genetic Algorithms are especially effective optimization tool when (near) optimal solutions for traffic control decisions for large-scale combinatorial problems are sought in real time. Optimizing online traffic control in urban networks is such a problem where online control decisions are recurrently sought based on real-time traffic information input. Limited time is available to reach near optimal solutions hence the best-thus-far solution (BTFS) is often the best one can do. It is thus critical that the best of BTFSs is identified. For that, offline evaluation is necessary to ensure appropriate combination of GA parameters and operators are selected for the real time online implementation in a real world setting. This paper describes an experimental approach to test the suitability of micro-Genetic Algorithms (m-GAs) to solve very large combinatorial traffic control problems and establishes relationships between time to convergence and problem size. A discrete time dynamical traffic control problem with different sizes and levels of complexity was used as a test-bed. The results showed that m-GAs can tackle computationally demanding problems. Upon appropriately sizing the m-GA population, the m-GA converged to a near-optimal solution in a number of generations equal to the string length. The results also demonstrated that with the selection of appropriate number of generations, it is possible to get most of the worth of the theoretically optimal solution but with only a fraction of the computation cost. The results showed that as the size of the optimization problem grew exponentially, the time requirements of m-GA grew only linearly thus making m-GAs especially suited for optimizing large scale and combinatorial problems for on-line optimization.
Keywords: On-line Traffic Control Decisions; Genetic Algorithms; Sustainable Transport; Genetic Algorithms Convergence.
Special Issue on: DASA'16 Decision and Logistics
Decision Aid System for Omani medical herb leaves recognition using computer vision and artificial intelligence
by Majed Bouchahma, Mohsin Al- Balushi, Sheikha Al-Housni, Hamood Al-Wardi
Abstract: Herbs have been widely used in food preparation, medicine and cosmetic industry. Knowing which herbs to be used would be very critical in these applications. This research aims to define a method to classify the herbs plants based on their leaves colors and shapes. An Open Source Decision Aid System is designed and developed especially for helping scientist. The proposed system employs artificial and image processing techniques to perform recognition on a number of Omani species of medical herbs.
Keywords: Decision Aid System; medical herbs; computer vision; artificial intelligence;SURF.
Special Issue on: Analytics for Decision Making
RAID-B2K, Transforming BPMN Conceptual Schemas into Kettle Execution Primitives
by Orlando Belo, Vasco Santos, Bruno Oliveira, Cláudia Gomes, Ricardo Marques
Data Normalization Techniques in Decision Making: Case Study with TOPSIS Method
by Nazanin Vafaei, Rita A. Ribeiro, Luis M. Camarinha-Matos
Communication Features in a DSS for Conflict Resolution based on the Graph Model
by Rami A. Kinsara, D. Marc Kilgour, Keith W. Hipel
A new non-parametric classifier to predict Small-business failures in Italy via performance ratios
by Francesca di Donato, Luciano Nieddu
Decision framework for selecting last mile delivery performance in Indian e-commerce companies
by Partha Priya Datta
Robustness of Retrospective Weighted Control Charts
by Shih-Chou Kao