Search results for: Evolutionary decision trees

Authors: Radomir Perzina, Jaroslav Ramik

Abstract:

The Analytic Hierarchy Process is frequently used approach for solving decision making problems. There exists wide range of software programs utilizing that approach. Their main disadvantage is that they are relatively expensive and missing intermediate calculations. This work introduces a Microsoft Excel add-in called DAME – Decision Analysis Module for Excel. Comparing to other computer programs DAME is free, can work with scenarios or multiple decision makers and displays intermediate calculations. Users can structure their decision models into three levels – scenarios/users, criteria and variants. Items on all levels can be evaluated either by weights or pair-wise comparisons. There are provided three different methods for the evaluation of the weights of criteria, the variants as well as the scenarios – Saaty’s Method, Geometric Mean Method and Fuller’s Triangle Method. Multiplicative and additive syntheses are supported. The proposed software package is demonstrated on couple of illustrating examples of real life decision problems.

Keywords: Analytic hierarchy process, multi-criteria decision making, pair-wise comparisons, Microsoft Excel, Scenarios.

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Authors: Khodamorad Nabaki, Jianhu Shen, Xiaodong Huang

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This paper develops a method for considering the critical fatigue stress as a constraint in the Bi-directional Evolutionary Structural Optimization (BESO) method. Our aim is to reach an optimal design in which high cycle fatigue failure does not occur for a specific life time. The critical fatigue stress is calculated based on modified Goodman criteria and used as a stress constraint in our topology optimization problem. Since fatigue generally does not occur for compressive stresses, we use the p-norm approach of the stress measurement that considers the highest tensile principal stress in each point as stress measure to calculate the sensitivity numbers. The BESO method has been extended to minimize volume an object subjected to the critical fatigue stress constraint. The optimization results are compared with the results from the compliance minimization problem which shows clearly the merits of our newly developed approach.

Keywords: Topology optimization, BESO method, p-norm, fatigue constraint.

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Authors: S. Sabbour, H. Lasi, P. von Tessin

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The purpose of this study is two-fold. First, it attempts to explore potential opportunities for utilizing visual interactive simulations along with Business Intelligence (BI) as a decision support tool for strategic decision making. Second, it tries to figure out the essential top-level managerial requirements that would transform strategic decision simulation into an integral component of BI systems. The domain of particular interest was the application of visual interactive simulation capabilities in the field of supply chains. A qualitative exploratory method was applied, through the use of interviews with two leading companies. The collected data was then analysed to demonstrate the difference between the literature perspective and the practical managerial perspective on the issue. The results of the study suggest that although the use of simulation particularly in managing supply chains is very evident in literature, yet, in practice such utilization is still in its infancy, particularly regarding strategic decisions. Based on the insights a prototype of a simulation based BI-solution-extension was developed and evaluated.

Keywords: Business Intelligence, decision support, strategic decisions, simulation, SCM.

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Authors: C. Ardil

Abstract:

The paper presents a multiple criteria decision making analysis process to determine the most suitable regional aircraft type according to a set of evaluation criteria. The main purpose of this study is to use different decision making methods to determine the most suitable regional aircraft for aviation operators. In this context, the nine regional aircraft types were analyzed using multiple criteria decision making analysis methods. Preference analysis for reference ideal solution (PARIS) was used in regional aircraft selection process. The findings of the proposed model show that the ranking results of the multiple criteria decision making models are consistent with each other, and the proposed method is efficient, and the results are valid. Finally, the Embraer E195-E2 model regional aircraft is chosen as the most suitable aircraft type.

Keywords: aircraft, regional aircraft selection, multiple criteria decision making, multiple criteria decision making analysis, mean weight, entropy weight, MCDMA, PARIS

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Authors: Adriana Ávila Zúñiga Nordfjeld

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This paper provides a literature review to document the state of the art with respect to handling “human bias” in decision making within the established quality management systems (QMS) and LEAN theory, in the context of shipbuilding. Previous research shows that in shipbuilding there is a huge deviation from the planned man-hours under the project management to the actual man-hours used because of errors in planning and reworks caused by human bias in the information flows, among others. This reduces the efficiency, and increases operational costs. Thus, the research question is how QMS and LEAN handle biases. The findings show the gap in studying the integration of methods to handle human bias in decision making into QMS and lean, not only within shipbuilding, but in general. Theoretical and practical implications are discussed for researchers and practitioners in the areas of decision making, QMS and LEAN, and future research is suggested.

Keywords: Human bias, decision making, LEAN Shipbuilding, quality management systems.

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Authors: B. Chouhan, A. Denesyuk, J. Heino, M. S. Johnson, K. Denessiouk

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Integrins are a large family of multidomain α/β cell signaling receptors. Some integrins contain an additional inserted I domain, whose earliest expression appears to be with the chordates, since they are observed in the urochordates Ciona intestinalis (vase tunicate) and Halocynthia roretzi (sea pineapple), but not in integrins of earlier diverging species. The domain-s presence is viewed as a hallmark of integrins of higher metazoans, however in vertebrates, there are clearly three structurally-different classes: integrins without I domains, and two groups of integrins with I domains but separable by the presence or absence of an additional αC helix. For example, the αI domains in collagen-binding integrins from Osteichthyes (bony fish) and all higher vertebrates contain the specific αC helix, whereas the αI domains in non-collagen binding integrins from vertebrates and the αI domains from earlier diverging urochordate integrins, i.e. tunicates, do not. Unfortunately, within the early chordates, there is an evolutionary gap due to extinctions between the tunicates and cartilaginous fish. This, coupled with a knowledge gap due to the lack of complete genomic data from surviving species, means that the origin of collagen-binding αC-containing αI domains remains unknown. Here, we analyzed two available genomes from Callorhinchus milii (ghost shark/elephant shark; Chondrichthyes – cartilaginous fish) and Petromyzon marinus (sea lamprey; Agnathostomata), and several available Expression Sequence Tags from two Chondrichthyes species: Raja erinacea (little skate) and Squalus acanthias (dogfish shark); and Eptatretus burgeri (inshore hagfish; Agnathostomata), which evolutionary reside between the urochordates and osteichthyes. In P. marinus, we observed several fragments coding for the αC-containing αI domain, allowing us to shed more light on the evolution of the collagen-binding integrins.

Keywords: Integrin αI domain, integrin evolution, collagen binding, structure, αC helix

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Authors: S. Madhavi, K. Baala Srinivas, G. Bharath, R. K. Indhuja, M. Kowser Chandini

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Game theory is the study of how people interact and make decisions to handle competitive situations. It has mainly been developed to study decision making in complex situations. Humans routinely alter their behaviour in response to changes in their social and physical environment. As a consequence, the outcomes of decisions that depend on the behaviour of multiple decision makers are difficult to predict and require highly adaptive decision-making strategies. In addition to the decision makers may have preferences regarding consequences to other individuals and choose their actions to improve or reduce the well-being of others. Nash equilibrium is a fundamental concept in the theory of games and the most widely used method of predicting the outcome of a strategic interaction in the social sciences. A Nash Equilibrium exists when there is no unilateral profitable deviation from any of the players involved. On the other hand, no player in the game would take a different action as long as every other player remains the same.

Keywords: Game Theory, Nash Equilibrium, Rules of Dominance.

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Authors: S. Housseno, A. Simonet, M. Simonet

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Semantic query optimization consists in restricting the search space in order to reduce the set of objects of interest for a query. This paper presents an indexing method based on UB-trees and a static analysis of the constraints associated to the views of the database and to any constraint expressed on attributes. The result of the static analysis is a partitioning of the object space into disjoint blocks. Through Space Filling Curve (SFC) techniques, each fragment (block) of the partition is assigned a unique identifier, enabling the efficient indexing of fragments by UB-trees. The search space corresponding to a range query is restricted to a subset of the blocks of the partition. This approach has been developed in the context of a KB-DBMS but it can be applied to any relational system.

Keywords: Index, Range query, UB-tree, Space Filling Curve, Query optimization, Views, Database, Integrity Constraint, Classification.

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Authors: Ehsan Akhlaghi

Abstract:

Effective employee selection is a critical component of a successful organization. Many important criteria for personnel selection such as decision-making ability, adaptability, ambition, and self-organization are naturally vague and imprecise to evaluate. The rough sets theory (RST) as a new mathematical approach to vagueness and uncertainty is a very well suited tool to deal with qualitative data and various decision problems. This paper provides conceptual, descriptive, and simulation results, concentrating chiefly on human resources and personnel selection factors. The current research derives certain decision rules which are able to facilitate personnel selection and identifies several significant features based on an empirical study conducted in an IT company in Iran.

Keywords: Decision Making, Expert System, PersonnelSelection, Rough Set Theory

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Authors: C. Ardil

Abstract:

Uncertainty set theory is a generalization of fuzzy set theory and intuitionistic fuzzy set theory. It serves as an effective tool for dealing with inconsistent, imprecise, and vague information. The technique for order preference by similarity to ideal solution (TOPSIS) method is a multiple-attribute method used to identify solutions from a finite set of alternatives. It simultaneously minimizes the distance from an ideal point and maximizes the distance from a nadir point. In this paper, an extension of the TOPSIS method for multiple attribute group decision-making (MAGDM) based on uncertainty sets is presented. In uncertainty decision analysis, decision-makers express information about attribute values and weights using uncertainty numbers to select the best stealth combat aircraft.

Keywords: Uncertainty set, stealth combat aircraft selection multiple criteria decision-making analysis, MCDM, uncertainty decision analysis, TOPSIS

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Authors: Saadoudi Mouni, Hamebaba Leila, Abdeddaim Mohamed

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In Algeria, some fruit trees produce fruits in free nature. Such trees are Celtis australis, Crataegus azarolus, Crataegus monogyna and Zizyphus lotus. In spite of their appreciable consumption, their nutritional value remains unknown. The objective of this study is the determination of sugars in the pulpe and almond of the above fruits. The biochemical analysis shows that these fruits present interesting contents of soluble sugars which confers significant caloric intakes to them. As well as significant fibres which give them therapeutic and industrial benefits? The analysis of the almonds shows that it contains considerable contents of sugars which enable them to be an energetic food.

Keywords: Celtis australis, Crataegus azarolus, Crataegus monogyna, Zizyphus lotus, Fibres, Soluble sugars.

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Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri

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This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well. 

Keywords: Data mining technique, the decision support system, knowledge and decision rules.

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Authors: R. Sammoura

Abstract:

In Lebanon, public construction projects are awarded to the contractor submitting the lowest bid price based on a competitive bidding process. The contractor has to make a strategic decision in choosing the appropriate bid price that will offer a satisfactory profit with a greater probability to win. A simulation model for bid price decision making based on the lowest bid price evaluation is developed. The model, built using Crystal Ball decisionengineering software, considers two main factors affecting the bidding process: the number of qualified bidders and the size of the project. The validity of the model is tested on twelve separate projects. The study also shows how to use the model to conduct risk analysis and help any specific contractor to decide on his bid price with associated certainty level in a scientific method.

Keywords: Bid price, Competition, Decision making, Simulation.

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Authors: C. Oprean, C. V. Kifor, S. C. Negulescu, C. Candea, L. Oprean, C. Oprean, S. Kifor

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This paper presents an innovative approach within the area of Group Decision Support System (GDSS) by using tools based on intelligent agents. It introduces iGDSS, a software platform for decision support and collaboration and an application of this platform - eCollaborative Decisions - for academic environment, all these developed within a framework of a research project.

Keywords: Group Decision Support System, Managerial Academic Decisions, Computer Interaction.

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Authors: Jan Zeman

Abstract:

The paper describes the futures trading and aims to design the speculators trading strategy. The problem is formulated as the decision making task and such as is solved. The solution of the task leads to complex mathematical problems and the approximations of the decision making is demanded. Two kind of approximation are used in the paper: Monte Carlo for the multi-step prediction and iteration spread in time for the optimization. The solution is applied to the real-market data and the results of the off-line experiments are presented.

Keywords: futures trading, decision making

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Authors: Mahesh Pal

Abstract:

In recent years, a number of works proposing the combination of multiple classifiers to produce a single classification have been reported in remote sensing literature. The resulting classifier, referred to as an ensemble classifier, is generally found to be more accurate than any of the individual classifiers making up the ensemble. As accuracy is the primary concern, much of the research in the field of land cover classification is focused on improving classification accuracy. This study compares the performance of four ensemble approaches (boosting, bagging, DECORATE and random subspace) with a univariate decision tree as base classifier. Two training datasets, one without ant noise and other with 20 percent noise was used to judge the performance of different ensemble approaches. Results with noise free data set suggest an improvement of about 4% in classification accuracy with all ensemble approaches in comparison to the results provided by univariate decision tree classifier. Highest classification accuracy of 87.43% was achieved by boosted decision tree. A comparison of results with noisy data set suggests that bagging, DECORATE and random subspace approaches works well with this data whereas the performance of boosted decision tree degrades and a classification accuracy of 79.7% is achieved which is even lower than that is achieved (i.e. 80.02%) by using unboosted decision tree classifier.

Keywords: Ensemble learning, decision tree, remote sensingclassification.

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Authors: Ayed Salman, Fawaz Al-Anzi, Aseel Al-Minayes

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Heuristics-based search methodologies normally work on searching a problem space of possible solutions toward finding a “satisfactory" solution based on “hints" estimated from the problem-specific knowledge. Research communities use different types of methodologies. Unfortunately, most of the times, these hints are immature and can lead toward hindering these methodologies by a premature convergence. This is due to a decrease of diversity in search space that leads to a total implosion and ultimately fitness stagnation of the population. In this paper, a novel Decision Maturity framework (DMF) is introduced as a solution to this problem. The framework simply improves the decision on the direction of the search by materializing hints enough before using them. Ideas from this framework are injected into the particle swarm optimization methodology. Results were obtained under both static and dynamic environment. The results show that decision maturity prevents premature converges to a high degree.

Keywords: Heuristic Search, hints, Particle Swarm Optimization, Decision Maturity Framework.

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Authors: Wided GuezGuez, Nahla Ben Amor, Khaled Mellouli

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Influence diagrams (IDs) are one of the most commonly used graphical decision models for reasoning under uncertainty. The quantification of IDs which consists in defining conditional probabilities for chance nodes and utility functions for value nodes is not always obvious. In fact, decision makers cannot always provide exact numerical values and in some cases, it is more easier for them to specify qualitative preference orders. This work proposes an adaptation of standard IDs to the qualitative framework based on possibility theory.

Keywords: decision making, influence diagrams, qualitative utility, possibility theory.

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Authors: Ph. Fauquet-Alekhine

Abstract:

Decision-making occurs several times an hour when working in high risk industry and an erroneous choice might have undesirable outcomes for people and the environment surrounding the industrial plant. Industrial decisions are very often made in a context of acute stress. Time pressure is a crucial stressor leading decision makers sometimes to boost up the decision-making process and if it is not possible then shift to the simplest strategy. We thus found it interesting to update the characterization of the stress factors impairing decision-making at Chinon Nuclear Power Plant (France) in order to optimize decision making contexts and/or associated processes. The investigation was based on the analysis of reports addressing safety events over the last 3 years. Among 93 reports, those explicitly addressing decision-making issues were identified. Characterization of each event was undertaken in terms of three criteria: stressors, biases impairing decision making and weaknesses of the decision-making process. The statistical analysis showed that biases were distributed over 10 possibilities among which the hypothesis confirmation bias was clearly salient. No significant correlation was found between criteria. The analysis indicated that the main stressor was time pressure and highlights an unexpected form of stressor: the trust asymmetry principle of the expert. The analysis led to the conclusion that this stressor impaired decision-making from a psychological angle rather than from a physiological angle: it induces defensive bias of self-esteem, self-protection associated with a bias of confirmation. This leads to the hypothesis that this stressor can intervene in some cases without being detected, and to the hypothesis that other stressors of the same kind might occur without being detected too. Further investigations addressing these hypotheses are considered. The analysis also led to the conclusion that dealing with these issues implied i) decision-making methods being well known to the workers and automated and ii) the decision-making tools being well known and strictly applied. Training was thus adjusted.

Keywords: Bias, expert, high risk industry, stress.

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Authors: Radoslav Delina, Michal Tkáč

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Globalization, supported by information and communication technologies, changes the rules of competitiveness and increases the significance of information, knowledge and network cooperation. In line with this trend, the need for efficient trust-building tools has emerged. The absence of trust building mechanisms and strategies was identified within several studies. Through trust development, participation on e-business network and usage of network services will increase and provide to SMEs new economic benefits. This work is focused on effective trust building strategies development for electronic business network platforms. Based on trust building mechanism identification, the questionnairebased analysis of its significance and minimum level of requirements was conducted. In the paper, we are confirming the trust dependency on e-Skills which play crucial role in higher level of trust into the more sophisticated and complex trust building ICT solutions.

Keywords: Correlation analysis, decision trees, e-marketplace, trust building

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Authors: Raja Muhammad Asif Zahoor, Junaid Ali Khan, I. M. Qureshi

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In this article an evolutionary technique has been used for the solution of nonlinear Riccati differential equations of fractional order. In this method, genetic algorithm is used as a tool for the competent global search method hybridized with active-set algorithm for efficient local search. The proposed method has been successfully applied to solve the different forms of Riccati differential equations. The strength of proposed method has in its equal applicability for the integer order case, as well as, fractional order case. Comparison of the method has been made with standard numerical techniques as well as the analytic solutions. It is found that the designed method can provide the solution to the equation with better accuracy than its counterpart deterministic approaches. Another advantage of the given approach is to provide results on entire finite continuous domain unlike other numerical methods which provide solutions only on discrete grid of points.

Keywords: Riccati Equation, Non linear ODE, Fractional differential equation, Genetic algorithm.

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Authors: Praveen Boinee, Alessandro De Angelis, Gian Luca Foresti

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Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques with classifiers such as random forests, neural networks and support vector machines. The data sets are from MAGIC, a Cherenkov telescope experiment. The task is to classify gamma signals from overwhelmingly hadron and muon signals representing a rare class classification problem. We compare the individual classifiers with their ensemble counterparts and discuss the results. WEKA a wonderful tool for machine learning has been used for making the experiments.

Keywords: Ensembles, WEKA, Neural networks [NN], SupportVector Machines [SVM], Random Forests [RF].

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Authors: Daniel Rivero, Julián Dorado, Juan R. Rabuñal, Alejandro Pazos, Javier Pereira

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The development of Artificial Neural Networks (ANNs) is usually a slow process in which the human expert has to test several architectures until he finds the one that achieves best results to solve a certain problem. This work presents a new technique that uses Genetic Programming (GP) for automatically generating ANNs. To do this, the GP algorithm had to be changed in order to work with graph structures, so ANNs can be developed. This technique also allows the obtaining of simplified networks that solve the problem with a small group of neurons. In order to measure the performance of the system and to compare the results with other ANN development methods by means of Evolutionary Computation (EC) techniques, several tests were performed with problems based on some of the most used test databases. The results of those comparisons show that the system achieves good results comparable with the already existing techniques and, in most of the cases, they worked better than those techniques.

Keywords: Artificial Neural Networks, Evolutionary Computation, Genetic Programming.

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Authors: Mikel Alonso López

Abstract:

In order to begin the process of studying the task of making consumer decisions, the main decision models must be analyzed. The objective of this task is to see if there is a presence of emotions in those models, and analyze how authors that have created them consider their impact in consumer choices. In this paper, the most important models of consumer behavior are analysed. This review is useful to consider an unproblematic background knowledge in the literature. The order that has been established for this study is chronological.

Keywords: Consumer behaviour, emotions, decision making, consumer psychology.

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Authors: Adesoji T. Jaiyeola, Josiah Adeyemo

Abstract:

This paper presents an extensive review of literature relevant to the modelling techniques adopted in sediment yield and hydrological modelling. Several studies relating to sediment yield are discussed. Many research areas of sedimentation in rivers, runoff and reservoirs are presented. Different types of hydrological models, different methods employed in selecting appropriate models for different case studies are analysed. Applications of evolutionary algorithms and artificial intelligence techniques are discussed and compared especially in water resources management and modelling. This review concentrates on Genetic Programming (GP) and fully discusses its theories and applications. The successful applications of GP as a soft computing technique were reviewed in sediment modelling. Some fundamental issues such as benchmark, generalization ability, bloat, over-fitting and other open issues relating to the working principles of GP are highlighted. This paper concludes with the identification of some research gaps in hydrological modelling and sediment yield.

Keywords: Artificial intelligence, evolutionary algorithm, genetic programming, sediment yield.

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Authors: Prakash G L, Thejaswini M, S H Manjula, K R Venugopal, L M Patnaik

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Computing and maintaining network structures for efficient data aggregation incurs high overhead for dynamic events where the set of nodes sensing an event changes with time. Moreover, structured approaches are sensitive to the waiting time that is used by nodes to wait for packets from their children before forwarding the packet to the sink. An optimal routing and data aggregation scheme for wireless sensor networks is proposed in this paper. We propose Tree on DAG (ToD), a semistructured approach that uses Dynamic Forwarding on an implicitly constructed structure composed of multiple shortest path trees to support network scalability. The key principle behind ToD is that adjacent nodes in a graph will have low stretch in one of these trees in ToD, thus resulting in early aggregation of packets. Based on simulations on a 2,000-node Mica2- based network, we conclude that efficient aggregation in large-scale networks can be achieved by our semistructured approach.

Keywords: Aggregation, Packet Merging, Query Processing.

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Authors: Tony Austin, Shanghua Sun, Nathan Lea, Steve Iliffe, Dipak Kalra, David Ingram, David Patterson

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Computer-based decision support (CDSS) systems can deliver real patient care and increase chances of long-term survival in areas of chronic disease management prone to poor control. One such CDSS, for the management of warfarin, is described in this paper and the outcomes shown. Data is derived from the running system and show a performance consistently around 20% better than the applicable guidelines.

Keywords: "Decision Support", "Anticoagulant Control"

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Authors: Gizelle K. Vianna, Gustavo S. Oliveira, Gabriel V. Cunha

Abstract:

The use of decision support systems in agriculture may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. In our work, we designed and implemented a decision support system for small tomatoes producers. This work investigates ways to recognize the late blight disease from the analysis of digital images of tomatoes, using a pair of multilayer perceptron neural networks. The networks outputs are used to generate repainted tomato images in which the injuries on the plant are highlighted, and to calculate the damage level of each plant. Those levels are then used to construct a situation map of a farm where a cellular automata simulates the outbreak evolution over the fields. The simulator can test different pesticides actions, helping in the decision on when to start the spraying and in the analysis of losses and gains of each choice of action.

Keywords: Artificial neural networks, cellular automata, decision support system, pattern recognition.

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Authors: Nazar Zaki, Safaai Deris

Abstract:

The amount of the information being churned out by the field of biology has jumped manifold and now requires the extensive use of computer techniques for the management of this information. The predominance of biological information such as protein sequence similarity in the biological information sea is key information for detecting protein evolutionary relationship. Protein sequence similarity typically implies homology, which in turn may imply structural and functional similarities. In this work, we propose, a learning method for detecting remote protein homology. The proposed method uses a transformation that converts protein sequence into fixed-dimensional representative feature vectors. Each feature vector records the sensitivity of a protein sequence to a set of amino acids substrings generated from the protein sequences of interest. These features are then used in conjunction with support vector machines for the detection of the protein remote homology. The proposed method is tested and evaluated on two different benchmark protein datasets and it-s able to deliver improvements over most of the existing homology detection methods.

Keywords: Protein homology detection; support vectormachine; string kernel.

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Authors: Norziana Aminudin, Titik Khawa Abdul Rahman, Ismail Musirin

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One of the factors to maintain system survivability is the adequate reactive power support to the system. Lack of reactive power support may cause undesirable voltage decay leading to total system instability. Thus, appropriate reactive power support scheme should be arranged in order to maintain system stability. The strength of a system capacity is normally denoted as system loadability. This paper presents the enhancement of system loadability through optimal reactive power planning technique using a newly developed optimization technique, termed as Multiagent Immune Evolutionary Programming (MAIEP). The concept of MAIEP is developed based on the combination of Multiagent System (MAS), Artificial Immune System (AIS) and Evolutionary Programming (EP). In realizing the effectiveness of the proposed technique, validation is conducted on the IEEE-26-Bus Reliability Test System. The results obtained from pre-optimization and post-optimization process were compared which eventually revealed the merit of MAIEP.

Keywords: Load margin, MAIEP, Maximum loading point, ORPP.

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