A new multi-criteria approach for sustainable material selection problem

Sustainable material selection is a crucial problem given the new demands of society and novel production strategies that consider the concepts of sustainability. Multi-criteria decision-making methods have been extensively used to help decision-makers select alternatives in different fields of knowledge. Nonetheless, these methods have been criticized due to the rank reversal problem, where the independence of the irrelevant alternative principle is violated after the initial decision problem is changed. Over the course of this study, we observed that the solutions that are proposed for this problem, in the context of sustainable material selection, are insufficient. Thus, we present a new material selection approach that is based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, which is immune to rank reversal. We also demonstrate the causes of rank reversal in the TOPSIS method, how the R-TOPSIS method was designed to solve them, and how it can be applied to sustainable material selection

The rank reversal problem in multi-criteria decision making : a literature review

Despite the importance of multicriteria decision-making (MCDM) techniques for constructing effective decision models, there are many criticisms due to the occurrence of a problem called rank reversal. Nevertheless, there is a lack of a systematic literature review on this important subject which involves different methods. This study reviews the pertinent literature on rank reversal, based on 130 related articles published from 1980 to 2015 in international journals, which were gathered and analyzed according to the following perspectives: multicriteria technique, year and journal in which the papers were published, co-authorship network, rank reversal types, and research goal. Thus our survey provides recommendations for future research, besides useful information and knowledge regarding rank reversal in the MCDM field

TOPSIS-RTCID for range target-based criteria and interval data

[EN] The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is receiving considerable attention as an essential decision analysis technique and becoming a leading method. This paper describes a new version of TOPSIS with interval data and capability to deal with all types of criteria. An improved structure of the TOPSIS is presented to deal with high uncertainty in engineering and engineering decision-making. The proposed Range Target-based Criteria and Interval Data model of TOPSIS (TOPSIS-RTCID) achieves the core contribution in decision making theories through a distinct normalization formula for cost and benefits criteria in scale of point and range target-based values. It is important to notice a very interesting property of the proposed normalization formula being opposite to the usual one. This property can explain why the rank reversal problem is limited. The applicability of the proposed TOPSIS-RTCID method is examined with several empirical litreture’s examples with comparisons, sensitivity analysis, and simulation. The authors have developed a new tool with more efficient, reliable and robust outcomes compared to that from other available tools. The complexity of an engineering design decision problem can be resolved through the development of a well-structured decision making method with multiple attributes. Various decision approches developed for engineering design have neglected elements that should have been taken into account. Through this study, engineering design problems can be resolved with greater reliability and confidence.Jahan, A.; Yazdani, M.; Edwards, K. (2021). TOPSIS-RTCID for range target-based criteria and interval data. International Journal of Production Management and Engineering. 9(1):1-14. https://doi.org/10.4995/ijpme.2021.13323OJS11491Ahn, B.S. (2017). 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Modeling fuzzy criteria preference to evaluate tradespace of system alternatives

2018 Summer.Includes bibliographical references.This dissertation explores techniques for evaluating system concepts using the point of diminishing marginal utility to determine a best value alternative with an optimal combination of risk, performance, reliability, and life cycle cost. The purpose of this research is to address the uncertainty of customer requirements and assess crisp and fuzzy design parameters to determine a best value system. At the time of this research, most commonly used decision analysis (DA) techniques use minimum and maximum values under a specific criterion to evaluate each alternative. These DA methods do not restrict scoring beyond the point of diminished marginal utility resulting in superfluous capabilities and overvalued system alternatives. Using these models, an alternative being evaluated could receive significantly higher scores when reported capabilities are greater than ideal customer requirements. This problem is pronounced whenever weights are applied to criteria where excessive capabilities are recorded. The techniques explored in this dissertation utilize fuzzy membership functions to restrict scoring for alternatives that provide excess capabilities beyond ideal customer requirements. This research investigates and presents DA techniques for evaluating system alternatives that determine an ideal compromise between risk, performance criteria, reliability and life cycle costs

A NEW LOGARITHM METHODOLOGY OF ADDITIVE WEIGHTS (LMAW) FOR MULTI-CRITERIA DECISION-MAKING: APPLICATION IN LOGISTICS

Logistics management has been playing a significant role in ensuring competitive growth of industries and nations. This study proposes a new Multi-Criteria Decision-making (MCDM) framework for evaluating operational efficiency of logistics service provider (LSP). We present a case study of comparative analysis of six leading LSPs in India using our proposed framework. We consider three operational metrics such as annual overhead expense (OE), annual fuel consumption (FC) and cost of delay (CoD, two qualitative indicators such as innovativeness (IN) which basically indicates process innovation and average customer rating (CR)and one outcome variable such as turnover (TO) as the criteria for comparative analysis. The result shows that the final ranking is a combined effect of all criteria. However, it is evident that IN largely influences the ranking. We carry out a comparative analysis of the results obtained from our proposed method with that derived by using existing established frameworks. We find that our method provides consistent results; it is more stable and does not suffer from rank reversal problem

Evaluation of Efficiency of Torrential Protective Structures With New BF-TOPSIS Methods

Decision-Aid Methods (DAMs) such as the CostBenefit Analysis (CBA) and the Analytical Hierarchy Process (AHP) help decision-makers to rank alternatives or to choose the best one among several potential ones

DEVELOPMENT OF A NEW HYBRID MULTI CRITERIA DECISION-MAKING METHOD FOR A CAR SELECTION SCENARIO

Increasing competition in the automobile industry has led to a vast variety of choices when buying a car thus making car selection a tedious task. The objective of this research is to develop a new hybrid multi-criteria decision-making technique, with accuracy greater than that of the already existing methods, in order to help the people in decision-making while buying a car. Hence, considering a broader spectrum, this study aims at easing the process of multi-criteria decision-making problems in different fields. To achieve the objective, seven different alternatives were evaluated with respect to the enlisted evaluation criteria, which were selected after analyzing the secondary data obtained from Pak wheels based on style, fuel economy, price, comfort and performance. These criteria were then analyzed using the proposed Full Consistency Fuzzy TOPSIS method. As the name tells, this method is a unique combination of two techniques. The Full Consistency method is used to calculate the weights of the criteria while the Fuzzy TOPSIS approach is applied to rank the alternatives according to their scores in the selected criteria. The outcomes demonstrate an increase in the consistency ratio of the weight coefficients due to which the ranking of the alternatives by the FCF-TOPSIS is more accurate than the TOPSIS and the Analytical Hierarchy Process. The novelty of the method lies in the fact that this combination has not been used for an alternative selection scenario before. In addition to this, it can be used in various industries where a choice between the available alternatives arises based on a set of evaluation criteria

TOWARDS RELIABLE DECISION-MAKING IN THE GREEN URBAN TRANSPORT DOMAIN

Operational research is a scientific discipline related to the decision theory that allows determining solutions for specific problems related to, for example, widely understood transport. Increasingly popular in this field are issues related to the domain of the green urban transport. In order to support the decision-making process in this area, methods of multi-criteria decision analysis (MCDA) are used more and more often. However, if we solve a specific problem using different MCDA methods, we get different rankings, as each method has a different methodological basis. Therefore, the challenge is how to make a reliable decision. This paper presents a numerical example from the green urban transport domain, which is solved by six different MCDA methods that return a complete ranking. We measure the similarity of these rankings using coefficients rw and WS, and then we propose a simple way of determining a compromise solution. The obtained compromise ranking is guaranteed to be the best match to the selected MCDA methods' rankings, which is proved in the paper. Finally, possible directions for further development work are identified

Dynamic decision making for situational awareness using drones: Requirements, identification and comparison of decision support methods

Decision makers increasingly operate in real-time information-rich environments, where limited time is available for interpreting data to inform decisions. These environments are driven by static or mobile sensing devices that can provide numerous dynamic data points. A prominent approach in this space is to utilise drones, which can be deployed to gather targeted information. However, deciding how best to deploy available drones is nontrivial, and stands to benefit from decision support aids that plan routes. Such a system must operate under time constraints created by the changing attributes of routes as the situation unfolds. This study describes a dynamic decision support system (DSS) for situational awareness with drones. The system applies Multi-Criteria Decision Making (MCDM) methods within a dynamic genetic algorithm to provide a continuously revised ranking of routes. Five desiderata for dynamic decision support are presented. It is shown how a dynamic DSS can be equipped with declarative specification of preferences (Desiderata 1), dynamic revision of recommendations (Desiderata 2), and high diversity of options (Desiderata 3). The study then compares four MCDM methods, namely the Weighted Product Model (WPM), the Analytic Hierarchy Process (AHP), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), with regards to how consistently they trade-off between criteria (Desiderata 4) and the stability of results under small changes to criteria values (Desiderata 5). To evaluate the trade-offs between criteria we analyse the smoothness of change in criteria outcomes as criteria weightings increase for each algorithm. The outcomes are calculated by automating the selection of routes in a case study that applies drones to the task of harbour management. The stability of results for the different MCDM methods are compared. Perturbations were applied to sets of routes ranked by each algorithm then each algorithm was reapplied and the magnitude of the changes in ranking was assessed. Overall, TOPSIS was found to be the algorithm which made the most consistent trade-offs between criteria, only under-performing another algorithm with respect to a single criterion. AHP and WPM were the next most consistent algorithms and PROMETHEE was the least consistent algorithm. TOPSIS was also found to be the most stable method under small changes to criteria values. AHP was the second most stable, followed by PROMETHEE and WPM respectively. The results show that TOPSIS achieves the best result for both Desiderata 4 and 5 and consequently the study finds TOPSIS to be an appropriate MCDM method for dynamic decision support.<br/