Geometrical Analysis For Interactive Aid Research Articles

Performance evaluation of Indian states in tourism using an integrated PROMETHEE-GAIA approach

Tourism development has a unique responsibility in regional policy of almost all countries. It is a well known fact that tourism has a remarkable contribution in sustainable development, economic growth and social benefits for a country, if planned methodically. This is due to its unquestionable advantages and benefits for the local community with respect to economic, social and environmental aspects. Since few decades, it has become a driving area in Indian economic planning strategy to deal with tourism issues for effective utilization of its wide range of destination resources and also optimize the intensity of financial involvement for developing tourist infrastructure in a restraint economic province. This paper applies a simple methodology to quantify the tourism potential of Indian states using an integrated visual decision aid model of preference ranking organization method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA). Several social and physical attributes are considered to evaluate and rank the Indian states with respect to their performance in tourism. The adoption of this integrated visual decision aid model identifies Jammu and Kashmir, and Jharkhand as the best and the worst performing states respectively. In GAIA plane, the position of Jammu and Kashmir is the farthest from origin, followed by Madhya Pradesh. A close comparison between these two top performing states reveals that Jammu and Kashmir mainly outperforms Madhya Pradesh with respect to budget allocation, population density, pollution index and cost of living index criteria. The performance of Jharkhand is not at all the best with respect to even a single criterion. In GAIA plane, it is also observed that the considered beneficial and non-beneficial criteria form two different clusters, as expected. This performance evaluation problem is identified to be not at all a hard problem to solve.

Evaluating vehicle painting plans in an automobile assembly plant using an integrated AHP‐PROMETHEE approach

AbstractThe painting activity is one of the most complex and important activities in automobile manufacturing. The inherent complexity of the painting activity and the frequent need for repainting usually turn the painting process into a bottleneck in automobile assembly plants, which is reflected in higher operating costs and longer overall cycle times. One possible approach for optimizing the performance of the paint shop is to improve the efficiency of the color planning. This can be accomplished by evaluating the relative merits of a set of vehicle painting plans. Since this problem has a multicriteria nature, we resort to the multicriteria decision analysis (MCDA) methodology to tackle it. A recent trend in the MCDA field is the development of hybrid approaches that are used to achieve operational synergies between different methods. Here we apply, for the first time, an integrated approach that combines the strengths of the analytic hierarchy process (AHP) and the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE), aided by Geometrical Analysis for Interactive Aid (GAIA), to the problem of assessing alternative vehicle painting plans. The management of the assembly plant found the results of value and is currently using them in order to schedule the painting activities such that an enhancement of the operational efficiency of the paint shop is obtained. This efficiency gain has allowed the management to bid for a new automobile model to be assembled at this specific plant.

Performance Evaluation of Indian Technical Institutions Using PROMETHEE-GAIA Approach

It has now become an important issue to evaluate the performance of technical institu- tions to develop better research and enrich the existing teaching processes. The results of such per- formance appraisal would serve as a reference point for decisions to choose a particular institution, hire manpower, and provide financial support for the betterment of students and underperforming institutions. The performance of institutions responsible for promotion of technical education in a vast country, like India also needs to be assessed for its journey to share a major role in global economy. In this paper, an integrated approach combining PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) and GAIA (Geometrical Analysis for Interac- tive Aid) methods is applied for evaluating the performance of 20 National Institutes of Technol- ogy (NITs) in India with respect to nine pivotal criteria. It is observed that NIT, Tiruchirappalli and NIT, Warangal are the two best performers based on the last five years data. On the other hand, NIT, Patna takes the last position in the ranking list of 20 NITs suggesting allocation of more resources for its efficient management and subsequent development. Placement of the graduated students from those NITs is found to be the most important criterion.

Application of Multicriteria Decision Making Methods to Compression Ignition Engine Efficiency and Gaseous, Particulate, and Greenhouse Gas Emissions

Compression ignition (CI) engine design is subject to many constraints, which present a multicriteria optimization problem that the engine researcher must solve. In particular, the modern CI engine must not only be efficient but must also deliver low gaseous, particulate, and life cycle greenhouse gas emissions so that its impact on urban air quality, human health, and global warming is minimized. Consequently, this study undertakes a multicriteria analysis, which seeks to identify alternative fuels, injection technologies, and combustion strategies that could potentially satisfy these CI engine design constraints. Three data sets are analyzed with the Preference Ranking Organization Method for Enrichment Evaluations and Geometrical Analysis for Interactive Aid (PROMETHEE-GAIA) algorithm to explore the impact of (1) an ethanol fumigation system, (2) alternative fuels (20% biodiesel and synthetic diesel) and alternative injection technologies (mechanical direct injection and common rail injection), and (3) various biodiesel fuels made from 3 feedstocks (i.e., soy, tallow, and canola) tested at several blend percentages (20-100%) on the resulting emissions and efficiency profile of the various test engines. The results show that moderate ethanol substitutions (~20% by energy) at moderate load, high percentage soy blends (60-100%), and alternative fuels (biodiesel and synthetic diesel) provide an efficiency and emissions profile that yields the most "preferred" solutions to this multicriteria engine design problem. Further research is, however, required to reduce reactive oxygen species (ROS) emissions with alternative fuels and to deliver technologies that do not significantly reduce the median diameter of particle emissions.

Ranking municipal solid waste treatment alternatives based on ecological footprint and multi-criteria analysis

The selection of a municipal solid waste (MSW) treatment alternative is a complex task in which a widespread set of criteria must be taken into account. Additionally to economic or social aspects, the decision process should consider the environmental perspective. With the purpose of quantifying the environmental burdens, a wide variety of environmental and sustainability indicators have been developed in the last years. Furthermore, integrative frameworks have been highlighted as the best option to achieve more comprehensive assessments.In this work, four different options of MSW treatment were ranked from an environmental point of view applying two methods: (1) the ecological footprint (EF) as single composite indicator and (2) multi-criteria analysis (MCA) integrating the EF together with other material flow indicators related to water consumption, emissions to air and water and occupied landfill volume. The MCA methods selected were a combination of Analytic Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) aided by Geometrical Analysis for Interactive Aid (GAIA). The objective was twofold: on the one side, the identification of the most beneficial waste treatment alternative (including thermal plasma gasification which as yet has not been assessed systematically) from an environmental perspective and, on the other side, the comparison of the results yielded by the two ranking methods proposed.The ranking obtained in both cases was (from best to worst): thermal plasma gasification, biological treatment of organic fraction with energy recovery from refuse derived fuel, incineration with energy recovery and landfilling. Hence, the EF proved to be a good screening indicator although it did not provide a comprehensive measure of environmental impacts associated to the waste treatment options considered. Besides, the combined application of AHP and PROMETHEE/GAIA as MCA methodology was found to be a suitable way, not very complex at user level, to integrate the information provided by a set of environmental criteria and to aid decision making.

Application of PROMETHEE-GAIA method for non-traditional machining processes selection

Article history: Received April 17, 2012 Accepted 11 June 2012 Available online June 12 2012 With ever increasing demand for manufactured products of hard alloys and metals with high surface finish and complex shape geometry, more interest is now being paid to non-traditional machining (NTM) processes, where energy in its direct form is used to remove material from workpiece surface. Compared to conventional machining processes, NTM processes possess almost unlimited capabilities and there is a strong believe that use of NTM processes would go on increasing in diverse range of applications. Presence of a large number of NTM processes along with complex characteristics and capabilities, and lack of experts in NTM process selection domain compel for development of a structured approach for NTM process selection for a given machining application. Past researchers have already attempted to solve NTM process selection problems using various complex mathematical approaches which often require a profound knowledge in mathematics/artificial intelligence from the part of process engineers. In this paper, four NTM process selection problems are solved using an integrated PROMETHEE (preference ranking organization method for enrichment evaluation) and GAIA (geometrical analysis for interactive aid) method which would act as a visual decision aid to the process engineers. The observed results are quite satisfactory and exactly match with the expected solutions. © 2012 Growing Science Ltd. All rights reserved.

Selection of logistic service provider using fuzzy PROMETHEE for a cement industry

PurposeThe purpose of this paper is to develop a method to select the best alternative in a multi‐criteria decision making (MCDM) environment when the decision is taken by a group of members in an uncertain environment.Design/methodology/approachIn this paper, Fuzzy Preference Ranking Organization Method for Enrichment Evaluations (Fuzzy PROMETHEE) technique has been used for MCDM problems. The team of decision makers is constituted to integrate their opinion. The analysis is done using Geometrical Analysis for Interactive Aid (GAIA) plane, available in Decision Lab 2000 software, which provides valuable help in understanding the conflicts among criteria.FindingsThe selection of best alternative is done on the basis of generally conflicting criteria. Fuzzy PROMETHEE technique has been proposed and the same is demonstrated using Decision Lab 2000 software. This software can be used for as many criteria as possible and also in a fuzzy environment, where the crisp data for criteria comparison are not available. It is found that the analysis of the results becomes very easy and effective with this software. A case study is conducted for a cement company to select the logistic service providers (LSPs) to demonstrate its ease and effectiveness of use.Originality/valueThe research provides a model to choose the best alternative using Decision Lab 2000 software for Fuzzy PROMETHEE technique. The proposed methodology can be used in a fuzzy environment with ease and effectiveness. In the competitive scenario, this could help the industry in prompt and efficient decision making in MCDM problems.

Solid Phase Excitation–Emission Fluorescence Method for the Classification of Complex Substances: Cortex Phellodendri and Other Traditional Chinese Medicines as Examples

A novel, simple and direct fluorescence method for analysis of complex substances and their potential substitutes has been researched and developed. Measurements involved excitation and emission (EEM) fluorescence spectra of powdered, complex, medicinal herbs, Cortex Phellodendri Chinensis (CPC) and the similar Cortex Phellodendri Amurensis (CPA); these substances were compared and discriminated from each other and the potentially adulterated samples (Caulis mahoniae (CM) and David poplar bark (DPB)). Different chemometrics methods were applied for resolution of the complex spectra, and the excitation spectra were found to be the most informative; only the rank-ordering PROMETHEE method was able to classify the samples with single ingredients (CPA, CPC, CM) or those with binary mixtures (CPA/CPC, CPA/CM, CPC/CM). Interestingly, it was essential to use the geometrical analysis for interactive aid (GAIA) display for a full understanding of the classification results. However, these two methods, like the other chemometrics models, were unable to classify composite spectral matrices consisting of data from samples of single ingredients and binary mixtures; this suggested that the excitation spectra of the different samples were very similar. However, the method is useful for classification of single-ingredient samples and, separately, their binary mixtures; it may also be applied for similar classification work with other complex substances.

Analysis of complex, processed substances with the use of NIR spectroscopy and chemometrics: Classification and prediction of properties — The potato crisps example

An NIR spectroscopic method was researched and developed for the analysis of potato crisps (chips) chosen as an example of a common, cheap but complex product. Four similar types of the ‘original flavour’ potato chips from different manufacturers were analysed by NIR spectroscopy; as well, the quality parameters — fat, moisture, acid and peroxide values of the extracted oil were predicted. Principal component analysis (PCA) of the NIR data displayed the clustering of objects with respect to the type of chips. NIR spectra were rank-ordered with the use of the sparingly applied multiple criteria decision making (MCDM) ranking methods, PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluation) and GAIA (Geometrical Analysis for Interactive Aid), and a comprehensive quantitative description of the data was obtained. The four traditional parameters were predicted on the basis of the NIR spectra; the performance of the Partial Least Squares (PLS), and Kernel Partial Least Squares (KPLS) calibrations was compared with those from Least Squares-Support Vector Machines (LS-SVM) method. The LS-SVM calibrations, which model better data linearity and non-linearity, successfully predicted all four parameters. This work has demonstrated that NIR methodology with the use of chemometrics can describe comprehensively qualitative and quantitative properties of complex, processed substances as illustrated by the potato chips example, and indicated that this approach may be applied to other similar complex samples.

PROMETHEE: A comprehensive literature review on methodologies and applications

Abstract In recent decades, several Multi-Criteria Decision Aid (MCDA) methods have been proposed to help in selecting the best compromise alternatives. In the meantime, the PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) family of outranking methods and their applications has attracted much attention from academics and practitioners. In this paper, a classification scheme and a comprehensive literature review are presented in order to uncover, classify, and interpret the current research on PROMETHEE methodologies and applications. Based on the scheme, 217 scholarly papers from 100 journals are categorized into application areas and non-application papers. The application areas include the papers on the topics of Environment Management, Hydrology and Water Management, Business and Financial Management, Chemistry, Logistics and Transportation, Manufacturing and Assembly, Energy Management, Social, and Other Topics. The last area covers the papers published in several fields: Medicine, Agriculture, Education, Design, Government and Sports. The scholarly papers are also classified by (1) year of publication, (2) journal of publication, (3) authors’ nationality, (4) PROMETHEE as applied with other MCDA methods, and (5) PROMETHEE as applied with GAIA (Geometrical Analysis for Interactive Aid) plane. It is hoped that the paper can meet the needs of researchers and practitioners for easy references of PROMETHEE methodologies and applications, and hence promote the future of PROMETHEE research.

Yield performance and adaptation of some Australian-grown rice varieties through multivariate analysis

Rice breeders are involved in developing new varieties for a diverse range of production environments to increase average yields. Interpretation of performance of several varieties evaluated in a broad range of environments is usually affected by variety × environment interactions. Application of multivariate analyses, especially multi-criteria decision making (MCDM) methods in variety × environment interaction studies can benefit interpretation of yield performance and adaptation of varieties in response to different environments. The case study discussed in this paper highlights the significant advantages of using MCDM methods to overcome constraints imposed by having to investigate a large number of variables inherent in such analysis. The grain yield of 13 rice varieties with varying characteristics, grown in 4 different regions in southern New South Wales (NSW), Australia, during the 2000–06 growing seasons was analysed using classification techniques and preference ranking organization method for enrichment evaluation (PROMETHEE) analysis. Significant variety × environment interaction was found to influence the yield performance of individual varieties across a range of environmental conditions. Classification of environments and geometrical analysis for interactive aid (GAIA) plot of PROMETHEE analysis identified the Murrumbidgee Irrigation Area (MIA) and Coleambally Irrigation Area (CIA) as higher yielding regions compared with the Eastern Murray Valley (EMV) and Western Murray Valley (WMV). Turbid water and cold weather conditions are suggested to explain the lower yield obtained in the WMV environment. In terms of varieties, Amaroo and Opus were identified as widely adaptable to most of the environments, while Jarrah was the least adaptable. Illabong can be considered as best adapted to the EMV or WMV regions, while Paragon and Reiziq can be regarded as best adapted to the MIA and CIA regions. Partial and complete ranking showed the interrelationships between the varieties for their yield performance and adaptation across all environments. Amaroo, Illabong, and Opus were ranked as the most preferred varieties, while Koshihikari, Kyeema, and Jarrah were ranked as the least preferred ones. Partial pre-order with 13 classes from PROMETHEE I analysis identified varieties with similar characteristics and aided the selection of suitable alternative varieties. The outcomes from the analyses reported here allow rice varieties or genotypes to be rated for yield stability for a specific or a range of different environments.

A comparative study of the elemental composition of the exhaust emissions of cars powered by liquefied petroleum gas and unleaded petrol

Elements emitted from the exhausts of new Ford Falcon Forte cars powered by unleaded petrol (ULP) and liquefied petroleum gas (LPG) were measured on a chassis dynamometer. The measurements were carried out in February, June and August 2001, and at two steady state driving conditions (60 and 80 km h −1). Thirty seven elements were quantified in the exhaust samples by inductively coupled plasma mass spectrometry (ICPMS). The total emission factors of the elements from the exhausts of ULP cars were higher than those of LPG cars at both engine speeds even though high variability in the exhaust emissions from different cars was noted. The effect of the operating conditions such as mileage of the cars, engine speed, fuel and lubricating oil compositions on the emissions was studied. To investigate the effects of these conditions, multivariate data analysis methods were employed including exploratory principal component analysis (PCA), and the multi-criteria decision making methods (MCDM), preference ranking organization method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA), for ranking the cars on the basis of the emission factors of the elements. PCA biplot of the complete data matrix showed a clear discrimination of the February, June and August emission test results. In addition, (i) platinum group elements (PGE) emissions were separated from each other in the three different clusters viz. Pt with February, Pd with June and Rh with August; (ii) the motor oil related elements, Zn and P, were particularly associated with the June and August tests (these vectors were also grouped with V, Al and Cu); and (iii) highest emissions of most major elements were associated with the August test after the cars have recorded their highest mileage. Extensive analysis with the aid of the MCDM ranking methods demonstrated clearly that cars powered by LPG outperform those powered by ULP. In general, cars tested in June perform better than those tested in August, which suggested that mileage was the key criterion of car performance on the basis of elemental emission factors.

Spectrophotometric determination of metal ions in electroplating solutions in the presence of EDTA with the aid of multivariate calibration and artificial neural networks

Metal ions such as Co(II), Ni(II), Cu(II), Fe(III) and Cr(III), which are commonly present in electroplating baths at high concentrations, were analysed simultaneously by a spectrophotometric method modified by the inclusion of the ethylenediaminetetraacetate (EDTA) solution as a chromogenic reagent. The prediction of the metal ion concentrations was facilitated by the use of an orthogonal array design to build a calibration data set consisting of absorption spectra collected in the 370–760 nm range from solution mixtures containing the five metal ions earlier. With the aid of this data set, calibration models were built based on 10 different chemometrics methods such as classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), artificial neural networks (ANN) and others. These were tested with the use of a validation data set constructed from synthetic solutions of the five metal ions. The analytical performance of these chemometrics methods were characterized by relative prediction errors and recoveries (%). On the basis of these results, the computational methods were ranked according to their performances using the multi-criteria decision making procedures preference ranking organization method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA). PLS and PCR models applied to the spectral data matrix that used the first derivative pre-treatment were the preferred methods. They together with ANN-radial basis function (RBF) and PLS were applied for analysis of results from some typical industrial samples analysed by the EDTA-spectrophotometric method described. DPLS, DPCR and the ANN-RBF chemometrics methods performed particularly well especially when compared with some target values provided by industry.

The Applications of Multicriteria Analysis for Estimating Pollution in Urban and Coastal Zones

ABSTRACT This paper presents the possibility of using methods of multicriterional analysis for estimating and ranking pollution in certain urban and suburban zones and along the coast, based on monitoring air and sea quality. Certain parameters which define the environmental characteristics are treated as criteria to be used for the estimating and ranking by applying a group of PROMETHEE methods. In order to get a complete insight into the “behaviour” of the pollution parameters, a programme package GAIA (Geometrical Analysis for Interactive Aid) is used, which enables the geometrical presentation of criteria and makes a visual display of clustering and “two dimensional behaviour” of pollution parameters. It is also possible to follow the change of ranks for each month of the year through graphics interpretation, with the possibility of changing criteria weight. Direct interpretation of the degree of pollution on the geographical maps which is realized by using pcArc/Info software, makes possible on—line use of this system for the needs of the city authorities so that decisions on wether or not to ban swimming or reduce certain industrial activities can be made. Thus it is conceptually suitable to DSS.