scholarly journals ANALYSIS OF THE PRE-INJECTION CONFIGURATION IN A MARINE ENGINE THROUGH SEVERAL MCDM TECHNIQUES

Brodogradnja ◽  
2021 ◽  
Vol 72 (4) ◽  
pp. 1-17
Author(s):  
Carlos Gervasio Rodríguez ◽  
◽  
María Isabel Lamas ◽  
Juan de Dios Rodríguez ◽  
Claudio Caccia ◽  
...  
Keyword(s):  
Multiple Criteria Decision Making ◽  
Solution Technique ◽  
Weighted Sum Method ◽  
Injection Duration ◽  
Injection Quantity ◽  
Performance And Emissions ◽  
Marine Diesel ◽  
Order Preference ◽  
Topsis Technique ◽  
Sum Vector

The present manuscript describes a computational model employed to characterize the performance and emissions of a commercial marine diesel engine. This model analyzes several pre-injection parameters, such as starting instant, quantity, and duration. The goal is to reduce nitrogen oxides (NOx), as well as its effect on emissions and consumption. Since some of the parameters considered have opposite effects on the results, the present work proposes a MCDM (Multiple-Criteria Decision Making) methodology to determine the most adequate pre-injection configuration. An important issue in MCDM models is the data normalization process. This operation is necessary to convert the available data into a non-dimensional common scale, thus allowing ranking and rating alternatives. It is important to select a suitable normalization technique, and several methods exist in the literature. This work considers five well-known normalization procedures: linear max, linear max-min, linear sum, vector, and logarithmic normalization. As to the solution technique, the study considers three MCDM models: WSM (Weighted Sum Method), WPM (Weighted Product Method) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The linear max, linear sum, vector, and logarithmic normalization procedures brought the same result: -22º CA ATDC pre-injection starting instant, 25% pre-injection quantity and 1-2º CA pre-injection duration. Nevertheless, the linear max min normalization procedure provided a result, which is different from the others and not recommended.

scholarly journals A Comparative Study on Decision Making Methods with Interval Data

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Aditya Chauhan ◽  
Rahul Vaish
Keyword(s):  
Decision Making ◽  
Input Data ◽  
Multiple Criteria Decision Making ◽  
Interval Data ◽  
Cross Entropy ◽  
Ideal Solution ◽  
Order Preference ◽  
Topsis Technique ◽  
Selection Of ◽  
Novel Algorithm

Multiple Criteria Decision Making (MCDM) models are used to solve a number of decision making problems universally. Most of these methods require the use of integers as input data. However, there are problems which have indeterminate values or data intervals which need to be analysed. In order to solve problems with interval data, many methods have been reported. Through this study an attempt has been made to compare and analyse the popular decision making tools for interval data problems. Namely, I-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), DI-TOPSIS, cross entropy, and interval VIKOR (VlseKriterijumska Optimiza-cija I Kompromisno Resenje) have been compared and a novel algorithm has been proposed. The new algorithm makes use of basic TOPSIS technique to overcome the limitations of known methods. To compare the effectiveness of the various methods, an example problem has been used where selection of best material family for the capacitor application has to be made. It was observed that the proposed algorithm is able to overcome the known limitations of the previous techniques. Thus, it can be easily and efficiently applied to various decision making problems with interval data.

scholarly journals A Multiple Criteria Decision Making Method Based on Relative Value Distances

2015 ◽  
Vol 40 (4) ◽  
pp. 299-315 ◽  
Author(s):  
Huan-jyh Shyur ◽  
Liang Yin ◽  
Hsu-shih Shih ◽  
Chi-bin Cheng
Keyword(s):  
Decision Making ◽  
Solution Method ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Relative Value ◽  
Seeking Behavior ◽  
Order Preference ◽  
Topsis Technique ◽  
The Ideal

Abstract This paper proposes a new multiple criteria decision-making method called ERVD (election based on relative value distances). The s-shape value function is adopted to replace the expected utility function to describe the risk-averse and risk-seeking behavior of decision makers. Comparisons and experiments contrasting with the TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution) method are carried out to verify the feasibility of using the proposed method to represent the decision makers’ preference in the decision making process. Our experimental results show that the proposed approach is an appropriate and effective MCDM method.

scholarly journals Multi-Criteria Decision Making Using TOPSIS Method Under Fuzzy Environment. Application in Spillway Selection

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 637 ◽  
Author(s):  
Vasiliki Balioti ◽  
Christos Tzimopoulos ◽  
Christos Evangelides
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Topsis Method ◽  
Northern Greece ◽  
Analytic Hierarchy ◽  
Order Preference ◽  
Optimum Type ◽  
Topsis Technique ◽  
Selection Of

The selection of an appropriate spillway has a significant effect to the construction of a dam and several procedures and considerations are needed. In the past, this selection of the type of the spillway was arbitrary and sometimes with bad results. Recently the Multiple Criteria Decision Making theory has given the possibility to make a decision about the optimum form of a spillway under complex circumstances. In this paper, the above method is used and especially the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for the selection of a spillway for a dam in the district of Kilkis in Northern Greece—‘Dam Pigi’. As the criteria were fuzzy and uncertain, the Fuzzy TOPSIS method is introduced together with the AHP (Analytic Hierarchy Process), which is used for the evaluation of criteria and weights. Five types of spillways were selected as alternatives and nine criteria. The criteria are expressed as triangular fuzzy numbers in order to formulate the problem. Finally, using the Fuzzy TOPSIS method, the alternatives were ranked and the optimum type of spillway was obtained.

scholarly journals Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative Study

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 335 ◽  
Author(s):  
Alireza Arabameri ◽  
Thomas Blaschke ◽  
Biswajeet Pradhan ◽  
Hamid Reza Pourghasemi ◽  
John P. Tiefenbacher ◽  
...  
Keyword(s):  
Low Income ◽  
Multiple Criteria Decision Making ◽  
Predictive Performance ◽  
Gully Erosion ◽  
Susceptibility Mapping ◽  
Rate Curve ◽  
Conditioning Factors ◽  
Soft Computing Techniques ◽  
Order Preference ◽  
Topsis Technique

Gully erosion is a problem; therefore, it must be predicted using highly accurate predictive models to avoid losses caused by gully development and to guarantee sustainable development. This research investigates the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML)-based models and their ensembles for gully erosion susceptibility mapping (GESM). A case study of the Dasjard River watershed, Iran uses a database of 306 gully head cuts and 15 conditioning factors. The database was divided 70:30 to train and verify the models. Their performance was assessed with the area under prediction rate curve (AUPRC), the area under success rate curve (AUSRC), accuracy, and kappa. Results show that slope is key to gully formation. The maximum entropy (ME) ML model has the best performance (AUSRC = 0.947, AUPRC = 0.948, accuracy = 0.849 and kappa = 0.699). The second best is the random forest (RF) model (AUSRC = 0.965, AUPRC = 0.932, accuracy = 0.812 and kappa = 0.624). By contrast, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model was the least effective (AUSRC = 0.871, AUPRC = 0.867, accuracy = 0.758 and kappa = 0.516). RF increased the performance of statistical index (SI) and frequency ratio (FR) statistical models. Furthermore, the combination of a generalized linear model (GLM), and functional data analysis (FDA) improved their performances. The results demonstrate that a combination of geographic information systems (GIS) with remote sensing (RS)-based ML models can successfully map gully erosion susceptibility, particularly in low-income and developing regions. This method can aid the analyses and decisions of natural resources managers and local planners to reduce damages by focusing attention and resources on areas prone to the worst and most damaging gully erosion.

scholarly journals Selection of an Appropriate Pre-Injection Pattern in a Marine Diesel Engine Through a Multiple-Criteria Decision Making Approach

2020 ◽  
Vol 10 (7) ◽  
pp. 2482 ◽  
Author(s):  
María Isabel Lamas Galdo ◽  
Laura Castro-Santos ◽  
Carlos G. Rodríguez Vidal
Keyword(s):  
Decision Making ◽  
Diesel Engine ◽  
Multiple Criteria Decision Making ◽  
Injection Rate ◽  
Multiple Criteria ◽  
Injection Duration ◽  
Formal Tool ◽  
Marine Diesel ◽  
Selection Of ◽  
Injection Strategies

In the present work, a numerical model was developed to analyze a commercial diesel engine. The adequacy of this model was validated using experimental results. This model was employed to study several pre-injection strategies. Particularly, the pre-injection rate, duration and starting instant were analyzed in the ranges 5% to 25%, 1° to 5° and −22° to −18°, respectively. The effect on consumption and emissions of NOx, CO, and HC wereas evaluated. Since some of these configurations have opposite effects on consumption and/or emissions, it is necessary to develop a formal tool to characterize the most appropriate injection pattern. To this end, a multiple-criteria decision making approach was employed. It was found that the injection duration must remain as low as possible due to significant reductions in NOx. The most appropriate injection pattern resulted 1° pre-injection duration, 20% pre-injection rate, and −19° pre-injection starting instant. This configuration leads to increments of 6.7% in consumption, 3.47% in CO, and 3.83% in HC but reduces NOx by 34.67% in comparison with the case without pre-injection.

Imprecise DEA Model Based on TOPSIS

2011 ◽  
Vol 63-64 ◽  
pp. 723-727 ◽  
Author(s):  
Ji Heng Xu ◽  
Ling Li ◽  
Jian Yong Liu ◽  
Cheng Qun Fu ◽  
Ji Lin Zheng
Keyword(s):  
Ideal Point ◽  
Multiple Criteria Decision Making ◽  
Decision Makers ◽  
Dea Model ◽  
Model Based ◽  
Relative Closeness ◽  
Subjective Preference ◽  
Order Preference ◽  
Set Up ◽  
Topsis Technique

In view of the defect that TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) can not deal with imprecise data properly in multiple criteria decision making. And the weights are often hard to reflect the fact because of subjective preference of decision makers. We proposed a hybrid model combines TOPSIS and imprecise DEA model to improve the disadvantages. Ideal DMU and anti-ideal DMU were built, and the corresponding positive ideal point and negative ideal point were established. Imprecise DEA was set up and an upper-lower limit method is utilized to formulate the imprecise efficiency scores of the two hypothetical DMUs. Based on the distances between DMU0 and the two hypothetical DMUs respectively, imprecise relative closeness was formulated to rank the superiority of all DMUs. The imprecise DEA model based on TOPSIS can avoid too subjective weights and make the evaluation more rational. A numerical example indicated the efficiency of the hybrid measure.

MULTIPLE CRITERIA CONSTRUCTION MANAGEMENT DECISIONS CONSIDERING RELATIONS BETWEEN CRITERIA / DAUGIATIKSLIAI STATYBOS VALDYMO SPRENDIMAI ATSIŽVELGIANT Į RODIKLIŲ TARPUSAVIO PRIKLAUSOMYBĘ

2010 ◽  
Vol 16 (1) ◽  
pp. 109-125 ◽  
Author(s):  
Jurgita Antuchevičienė ◽  
Edmundas Kazimieras Zavadskas ◽  
Algimantas Zakarevičius
Keyword(s):  
Decision Making ◽  
Mahalanobis Distance ◽  
Construction Management ◽  
Distance Measure ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Ideal Solution ◽  
Order Preference ◽  
Topsis Technique

Decision making in construction management has been always complicated especially if there were more than one criterion under consideration. Multiple criteria decision making (MCDM) has been often applied for complex decisions in construction when a lot of criteria were involved. Traditional MCDM methods, however, operate with independent and conflicting criteria. While in every day problems a decision maker often faces interactive and interrelated criteria. Accordingly, the need of improving and supplementing the methodology of compromise decisions arose. It was proposed to supplement TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) method and integrate the Mahalanobis distance in the usual algorythm of TOPSIS. Mahalanobis distance measure offered an option to take the correlations between the criteria into considerations while making the decision. A case study of building redevelopment in Lithuanian rural areas was presented that demonstrated the application of the proposed methodology. The case study proved that the proposed TOPSIS‐M (TOPSIS applying Mahalanobis distance measure) method could have substantial influence in carrying the proper decision. Santrauka Statybos valdymo spendimų priėmimas visuomet yra komplikuotas, ypač jei turime atsižvelgti į daugelį rodiklių. Kompleksiniams statybos sprendimams, kurie apibūdinami daugeliu rodiklių, taikomi daugiatiksliai sprendimų priėmimo metodai (MCDM ‐ Multiple Criteria Decision Making). Šie metodai skirti sprendimams priimti tuomet, kai vertinami konfliktuojantys bei nepriklausomi rodikliai. Tačiau realiose situacijose, priešingai, nuolat susiduriame su saveikaujančiais ir tarpusavio priklausomybę turinčiais rodikliais. Dėl šios priežasties kyla poreikis patobulinti sprendimų metodologiją. Straipsnyje siūloma papildyti variantų racionalumo nustatymo metoda TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution), taikant Mahalanobio metoda atstumams nustatyti. Mahalanobio atstumų nustatymo metodas suteikia galimybę įvertinti koreliacinės rodiklių priklausomybės priimant daugiatikslį sprendimą. Siūlomos metodologijos taikymas įliustruojamas sprendžiant apleistų pastatų Lietuvos kaimo vietovėse racionalaus sutvarkymo uždavinį. Pateiktas pavyzdys patvirtina, kad TOPSIS‐M metodo (t. y. TOPSIS naudojant Mahalanobio atstuma) taikymas gali turėti esminę įtaka priimant sprendimą.

scholarly journals Multiple-Criteria Decision-Making and Sensitivity Analysis for Selection of Materials for Knee Implant Femoral Component

Materials ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2084
Author(s):  
Raman Kumar ◽  
Rohit Dubey ◽  
Sehijpal Singh ◽  
Sunpreet Singh ◽  
Chander Prakash ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Femoral Component ◽  
Human Life ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Biomedical Science ◽  
Human Beings ◽  
Weighted Sum Method ◽  
Selection Of

Total knee replacement (TKR) is a remarkable achievement in biomedical science that enhances human life. However, human beings still suffer from knee-joint-related problems such as aseptic loosening caused by excessive wear between articular surfaces, stress-shielding of the bone by prosthesis, and soft tissue development in the interface of bone and implant due to inappropriate selection of TKR material. The choice of most suitable materials for the femoral component of TKR is a critical decision; therefore, in this research paper, a hybrid multiple-criteria decision-making (MCDM) tactic is applied using the degree of membership (DoM) technique with a varied system, using the weighted sum method (WSM), the weighted product method (WPM), the weighted aggregated sum product assessment method (WASPAS), an evaluation based on distance from average solution (EDAS), and a technique for order of preference by similarity to ideal solution (TOPSIS). The weights of importance are assigned to different criteria by the equal weights method (EWM). Furthermore, sensitivity analysis is conducted to check the solidity of the projected tactic. The weights of importance are varied using the entropy weights technique (EWT) and the standard deviation method (SDM). The projected hybrid MCDM methodology is simple, reliable and valuable for a conflicting decision-making environment.

scholarly journals KURUMSAL YÖNETİM ENDEKSİ KAPSAMINDAKİ İŞLETMELERİN FİNANSAL PERFORMANS ANALİZİ

2016 ◽  
Vol 4 (2) ◽  
pp. 185-202
Author(s):  
Fatih Temizel ◽  
Hamza Doğan ◽  
Berfu Ece Bayçelebi
Keyword(s):  
Ideal Solution ◽  
Order Preference ◽  
Topsis Technique

Payları Borsa İstanbul’da işlem gören işletmelere Sermaye Piyasası Kurulu (SPK)’nun ilgili tebliğine göre bağımsız derecelendirme kurumlarınca verilen Kurumsal Yönetim İlkelerine Uyum Notu kurumsal yönetim konusunda en başarılı 50 işletmenin belirlenmesini sağlamaktadır. Bu açıdan en başarılı 50 işletme ise fiyat ve getiri analizlerinde yardımcı göstergeler edinmek amacı ile Borsa İstanbul Kurumsal Yönetim Endeksi’ne dâhil edilmektedir. Bu çalışmada Kurumsal Yönetim Endeksi’nde bulunan 50 işletme arasından finans sektörü dışında kalan 34 işletmenin finansal performans sıralaması TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) yöntemi ile elde edilmiştir. TOPSIS yöntemi işletmelere ait 10 adet finansal orana dayalı olarak kullanılmıştır. 2011-2015 yıllarını kapsayan 5 yıl için performans sıralamaları elde edilip yıllar arası sıralama değişimleri incelenmiştir. Son olarak 5 yıllık ortalama performans sıralaması hesaplanarak endeksteki işletmelerin son 5 yıl içerisindeki finansal performans dereceleri ortaya konulmuştur. 

scholarly journals Supplier selection using different metric functions

2015 ◽  
Vol 25 (3) ◽  
pp. 413-423 ◽  
Author(s):  
S.E. Omosigho ◽  
Dickson Omorogbe
Keyword(s):  
Supplier Selection ◽  
Selection Process ◽  
Fuzzy Topsis ◽  
Ideal Solution ◽  
Order Preference ◽  
Metric Functions ◽  
Metric Function ◽  
Topsis Technique ◽  
Intuitionistic Fuzzy Topsis ◽  
Selection Of

Supplier selection is an important component of supply chain management in today?s global competitive environment. Hence, the evaluation and selection of suppliers have received considerable attention in the literature. Many attributes of suppliers, other than cost, are considered in the evaluation and selection process. Therefore, the process of evaluation and selection of suppliers is a multi-criteria decision making process. The methodology adopted to solve the supplier selection problem is intuitionistic fuzzy TOPSIS (Technique for Order Preference by Similarity to the Ideal Solution). Generally, TOPSIS is based on the concept of minimum distance from the positive ideal solution and maximum distance from the negative ideal solution. We examine the deficiencies of using only one metric function in TOPSIS and propose the use of spherical metric function in addition to the commonly used metric functions. For empirical supplier selection problems, more than one metric function should be used.

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