MCDM Methods Research Articles

DETECTION OF SUSTAINABLE LOGISTICS SUB-COMPONENTS AND, DETERMINATION OF IMPACT LEVELS OF SUSTAINABLE LOGISTICS COMPONENTS WITH DEMATEL METHOD

Sustainable logistics can be defined as all logistics activities carried out by preserving and maintaining the present and the future for generations in environmental, economic, and social dimensions. In the study, the impact levels and sub-components of the main components in environmental, economic, and social dimensions, which are important for sustainable logistics, are examined. When the environmental dimension is examined, it is seen that its sub-components are use of resource (energy, water, material, etc.), water/soil pollution, greenhouse gas emissions, noise pollution, and waste management. When the sub-components of the economic dimension are examined, it includes the components of product and service quality, cost minimization, recycling, market share/growth, and goods transport intensity. When the sub-components of the social dimension are examined, it is seen that the components of occupational health and safety, education and training, working conditions, and public health are covered. The impact levels of the main components of sustainable logistics are evaluated with the DEMATEL method, which is one of the MCDM methods. According to the DEMATEL method, while economic and environmental dimensions are in the affecting group, social dimension is in the affected group.

Evaluation of the effective factors of unloading and loading goods in ports and the impact on the environment using the MCDM method.

Today, the impact of transportation on sustainable development is undeniable. Maritime transport, as one of the important pillars of this industry and due to its advantages such as low cost and large volume of freight, plays a key role in the development of foreign trade. This study uses TOPSIS and DEMATLE methods to evaluate the factors affecting the unloading and loading of goods on the port's environment. The results of TOPSIS method on the main criteria showed that the atmospheric factors' criterion had the highest weight and the port criterion (includes activities that occur in the port) had the lowest weight. In the sub-criteria section, the suitable visibility of the crane operator had the highest rank and the sub-criterion of storage capacity and its (storage) area obtained the lowest rank. Also, the results of DEMATEL method on the cause-and-effect relationship of sub-criteria of unloading and loading of goods showed that coastal transport equipment, unloading and loading equipment, and safety required for unloading goods are influenced higher than other sub-criteria and also the impact of safety required for unloading goods, proper visibility of the crane operator, and the ability of unloading equipment is greater than other sub-criteria. The results of cause-and-effect relationship for sub-criteria of the port environment showed that the impact of sewage, sanitary effluents from the port and ships, and hazardous waste is higher than other sub-criteria and also the impact of petroleum hydrocarbons, sediments, and toxic substances is higher than other sub-criteria.

Extension of Interval-Valued Fermatean Fuzzy TOPSIS for Evaluating and Benchmarking COVID-19 Vaccines

In order to help curb the spread of the COVID-19 pandemic, this paper develops a multi-attribute decision-making framework for COVID-19 vaccine evaluation based on their major clinical characteristics and efficacy. Firstly, a new multi-criteria Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) modification has been constructed in an interval-valued Fermatean fuzzy (IVFF) environment, improving the shortcomings of traditional TOPSIS. Secondly, a new conceptual framework for static and dynamic evaluation of COVID-19 vaccines has been built. The proposed methodology incorporates a variety of crisp and fuzzy MCDM methods. The analysis of the results of two practical examples shows that the new framework for vaccine comparison is feasible and effective, and finally, some recommendations for enhancement of government anti-COVID-19 strategies are suggested.

Performance Measurement in R&D Projects: Relevance of Indicators Based on US and German Experts

To turn technologies into successful products, it is necessary to understand the development process from ideas to the market and to know how to measure performance. Performance measurement is critical for technology developers and investors in monitoring whether performance meets expectations to make decisions about actions for improving R&D characteristics. This article emphasizes indicators for R&D project performance measurement, especially relevant for measuring project performance in company, start-up and spin-off companies, where the project is perceived as an independent business unit. A clear set of indicators for measuring and controlling the performance of R&D projects for policy representatives would allow them to identify problematic areas in the implementation of R&D projects and to make well-aimed decisions for the promotion and financing of technology development. What indicators should be used to measure the performance of R&D projects? Attempts to find the answer to the question in science were unsuccessful. This article aims to select indicators for measuring the performance of R&D projects and identify and compare their relevance among US and German experts. Research is carried out in different countries, and their results create opportunities for mutual learning and more intensive international cooperation in technological development. In order to achieve a goal, essential decision-making points in R&D projects were identified, and a general set of R&D performance evaluation indicators were prepared based on a literature analysis. Later, two groups of experts from the US and Germany selected from the general list indicators suitable only for evaluating R&D projects and evaluated their relevance. The obtained evaluation results of the US and German experts were processed using the MCDM method and compared.

Pharmaceutical 3PL supplier selection using interval-valued intuitionistic fuzzy TOPSIS

Third party logistics (3PL) supplier selection problem is a multi-criteria selection problem that is frequently discussed in the literature. Medicine is a fundamental element for human health and drug transportation must be carried out on time and under conditions that will ensure that the drug does not lose its physical properties. Therefore, the pharmaceutical industry is one of the foremost and most important sectors in 3PL. In this multi-criteria problem where the evaluation criteria are linguistic rather than numerical, vagueness and impreciseness in evaluations can only be handled with the help of fuzzy sets. With the help of intuitionistic fuzzy sets, one of the new extensions of fuzzy sets, the vagueness and impreciseness here will be discussed and the 3PL supplier selection problem will be tried to be solved with the TOPSIS method, which is one of the most used MCDM methods in the literature. The use of Interval-Valued Intuitionistic Fuzzy sets will add more flexibility and accuracy to the assessment. Thus, the Interval-Valued Intuitionistic Fuzzy TOPSIS method is used to solve the 3PL supplier selection problem and the robustness of the decisions taken is tested with a sensitivity analysis.

Novel Comprehensive MEREC Weighting-Based Score Aggregation Model for Measuring Innovation Performance: The Case of G7 Countries

Innovation can be the greatest hope of overcoming economic challenges. This paper aims to evaluate countries concerning their innovation performances. We introduce an innovation performance evaluation methodology by considering objective factors and applying seven reliable MCDM methods: MEREC, CODAS, MABAC, MARCOS, CoCoSo, WASPAS, and MAIRCA. MEREC calculates the relative weights of indicators considered, while the other techniques decide the ranking order of G7 countries. The Borda rule is then employed to gain an aggregated ranking order. “Business sophistication” is the most critical indicator, whereas the US has the best position regarding the overall ranking. Sensitivity control is as well conducted.

Evaluation of Marine currents energy plant using DETMAL MCDM Method

Evaluation of Marine currents energy plant using DETMAL MCDM Method

Green Supplier Selection in an Uncertain Environment in Agriculture Using a Hybrid MCDM Model: Z-Numbers–Fuzzy LMAW–Fuzzy CRADIS Model

The goal of this research was to find a selection of green suppliers (GSS) that will, in the best way, help agricultural producers to apply green agricultural production using uncertainty in decision making. In order to avoid the possibility of uncertainty in the expert decision making, Z-numbers were used together with the fuzzy LMAW (Logarithm Methodology of Additive Weights) method and fuzzy CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) method. By applying Z-numbers and the fuzzy LMAW method, the weighting coefficients of the criteria were determined, where the experts, in addition to the criteria ratings, also defined the degrees of certainty in the criteria ratings they gave. The obtained results indicated that the criteria related to price and qualities are the most important during the selection process. To select the best alternative, the CRADIS method modified with Z-numbers and fuzzy numbers was applied. The results obtained by applying this method showed that suppliers A2 and A3 have the best characteristics and are the first choice for the procurement of raw and production materials. As part of the paper, the validation of the results and the sensitivity analysis of the model were carried out by conducting the procedure of comparing the obtained results with the results obtained by other MCDM methods and changing the weighting coefficients of the criteria. These analyses indicated that the model presented provides stable results. The conducted research showed how Z-numbers can be used to reduce uncertainty in decision making and how Z-numbers can be used with other fuzzy methods to perform GSS.

Genetic Algorithm-enhanced Rank aggregation model to measure the performance of Pulp and Paper Industries

Performance measurement is a complex but important task required in all sectors. The problem however arises when usage of different methods for performance assessment provides different results. Under such circumstances when there is a difference of opinions, rank aggregation methods can be used to provide the best solution to decision-makers (DMs). Such approaches, also known as data fusion approaches, combine ranked lists from various methods to generate a consensus. In this study, a novel rank aggregation method is proposed for addressing the problem of conflicting MCDM ranking results. The suggested method uses genetic algorithm (GA) to minimize the Euclidean distance between the ideal ranking and the ranking computed by multiple MCDM methods. This model is embedded into a hybrid multi-criteria decision-making (HMCDM) approach, which is divided into three distinct phases. The first phase identifies the most efficient alternatives; the second analyses the rankings obtained through various MCDM methods; and finally, a compromise ranking result is generated. The proposed approach is employed to measure the performance of Indian Pulp and Papermaking Industries (IPPI).

Similarity of TOPSIS results based on criterion variability: Case study on public economic.

In the real world, acceptance of a decision is conditional on the availability of a great volume of data. Selection of a suitable solution on the basis of this data represents a problem that multi-criterial methods (MCDM) are applied to. The issue of which of these should be favoured during their use involves a specification of the importance of the assessed criteria. The goal of the presented research is to quantify the differences (symmetry) in assessment using selected MCDM methods (Technique for Order of Preference by Similarity to Ideal Solution-TOPSIS), while applying an absolute and relative variability of the assessed criteria to a determination of their importance. The obtained results indicate that the order of the assessed subject (alternative) is not directly influenced by the method of determining the variability of the assessed criteria. We can also state that the degree of concurrence in the order of application of the TOPSIS technique, in combination with both approaches expressed by the Jaccard index, is relatively low.

Application of MCDM methods for flood susceptibility assessment and evaluation the impacts of past experiences on flood preparedness

This research is analyzed the flood hazard using two ensemble models in ​​Mazandaran Province, Iran and assess how the experience contributed to community flood preparedness by household surveys. For this purpose, two approaches of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Attributive Border Approximation Area Comparison (MABAC) combined with Analytical Hierarchy Process (AHP) and weight of evidence (WOE) models were applied. Applying eight effective factors, including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, the flood risk zoning map was prepared with the help of Geographic Information System. The area under the receiver operating characteristic curve (AUROC) for test sample set was 87.14% and 83.24% for TOPSIS-based and MABAC-based models, respectively. Regression analysis found a negative correlation between past flood experience and flood preparedness of residents, consistent with cluster analysis. This indicates that local people had not learned much from previous experiences of flood catastrophes.

A hybrid approach of fuzzy risk assessment-based incenter of centroid and MCDM methods for multimodal transportation route selection

A hybrid approach of fuzzy risk assessment-based incenter of centroid and MCDM methods for multimodal transportation route selection

Comparative analysis of hybrid MCDM methods in material selection for dental applications

Ascertaining the appropriate material through detailed functionalities out of numerous existing materials is a complex and time-consuming exercise. Hence, it is essential to follow a methodical and resourceful approach to decide on the best material for any given application. This paper aims to demonstrate the capability of five distinct Hybrid Multi Criteria Decision Making (HMCDM) methods in identifying the best polymer based biomaterial used in dentistry. A total of seven materials were evaluated based on ten criteria. At first, Analytical Hierarchy Process (AHP) was employed to determine the weights of the recognized criterion. Later, AHP-VIKOR, AHP-TOPSIS, AHP-MOORA, AHP-ELECTRE, and AHP-PROMTHEE procedures were used to rank the materials. The methodologies have displayed their effectiveness by providing the equivalent ranking to the materials. Finally, spearman’s rank correlation was determined to compare HMCDM methods. The comparative analysis revealed that AHP-VIKOR, AHP-TOPSIS, and AHP-PROMETHEE have a greater correlation compared to AHP-MOORA and AHP-ELECTRE.

A Systematic Literature Review on Performance Evaluation of Power System From the Perspective of Sustainability

Sustainability is a comprehensive concept that integrates at least three dimensions of environment, economy and society. The power system is the primary source of greenhouse gas emissions, adversely impacting environmental sustainability. It also generates necessary energy supplies, which promote economic and social sustainable development. Based on the sustainability nature of power system, this study puts forward an improved methodology, namely “Planning-Searching-Screening-Reporting-Reflecting” (PSSRR Cycle) to review the literature systematically on power system performance evaluation from a sustainability perspective over the past 20 years, with the aim of describing the current state of the whole performance evaluation system including the evaluation framework, evaluation indicators and evaluation methods, and providing research suggestions for future research. This study finds in the current literature that the Triple Bottom Line theory is the most commonly used theoretical evaluation framework; environmental and economic sustainability indicators are more emphasized; the DEA and MCDM methods are the more common evaluation methods. This study presents some future research notes, including improving the Sustainable Balanced Scorecard as a sustainable performance evaluation framework, emphasizing more social sustainability indicators, and using a combination of existing evaluation methods to make performance evaluation more efficient and accurate.

Integrated modelling approach for an eco-industrial park site selection

Inconsistencies of the single multi-criteria decision making (SMCD) methods in criteria weight assessment make them unreliable and have led to the wrong siting of industrial parks, which are often abandoned as brownfields that emit GHG. Eco-industrial parks (EIPs) are replacing brownfields but require robust decision-making tools to weigh and rank suitable locations for industry clusters' synergies. Integrated multi-criteria decision making (IMCDM) to address the weaknesses and strengthen the advantages of SMCDM methods, and a model to overlay criteria weights and spatial data easily and accurately were developed. The spatial criteria data for 2009 and 2019 from Tanjung Langsat Industrial Area were collected and prepared by GIS to test the SMCDM and IMCMD consistency weighting and the model resilience. The SMCDM (AHP, ANP and F-AHP) and the IMCDM weights with the 2009 criteria data identified the entire water bodies around the brownfield as suitable sites, making them inconsistent. The 2019 data with the SMCDM weights identified tiny sites as best, also making them inconsistent. The integrated hierarchy network fuzzy analytic process (HN-FAP) and the hierarchy network analytic process (HNAP) with the 2019 criteria data identified part of the water bodies as suitable making it inconsistent. The hierarchy fuzzy analytic process (H-FAP) and the network fuzzy hierarchy analytic process (NFh-AP) identified larger suitable sites without overlaps making them consistent algorithms. The H-FAP and NFh-AP procedures eliminate the weaknesses and consolidate the strengths, giving optimally consistent criteria weights. The two algorithms' consistency and the model efficiency can use different criteria weights and spatial data inputs from elsewhere for 4IR-driven EIP modelling to help brownfield-EIP stakeholders. Future research would address the reverse ranking of MCDM methods when alternatives are added or removed.

Multi-Criteria Decision-Making Methods in Green Logistics

Due to the increased demands of the world community in accordance with the goals of the concept of sustainable development, supply chain management requires complex decisionmaking models that consider many environmental, economic, and social constraints when implementing various environmentally friendly, green methods and technologies. An effective tool in such conditions is the use of MCDM, multi-criteria decision-making methods. The objective of the research, the results of which are provided in the article, is to analyse the application of MCDM in green logistics and management of green supply chains. The work used a set of methods including system and structural-functional analysis, methods of the theory of fuzzy sets, mathematical statistics, and expert assessments. A general scheme of MCDM implementation is offered and a combined MCDM model is developed for assessing decisions on the choice of green technologies, including a system of indicators for logistics flows, a model for managing logistics flows and a system of tools for green logistics. In the MCDM model, a fuzzy analytical hierarchical process (fuzzy AHP) is used to establish the weight of indicators of logistics flows, eleven MCDM methods are used to rank green logistics tools: SAW, TOPSIS, PROMETHEE, COPRAS, ARAS, WASPAS, MAIRCA, EDAS, MABAC, CODAS, MARCOS. Comparison of the use of various MCDM methods showed a high convergence of the ranking results (Spearman’s rank correlation coefficient is of 0.949). The most consistent are SAW, MARCOS and WASPAS methods, the least consistent are CODAS methods. The results of the design example showed that the most preferable solution is the «use of intermodal technologies and multimodal transportation» (ranked first within all eleven methods), the least preferable solution is the «use of environmentally friendly fuels and lubricants (fuels)» (ranked 12th within 10 methods of 11).

Selection of Suppliers in Industrial Manufacturing: A Fuzzy Rough PROMETHEE Approach

In supply chain management (SCM), the selection of suppliers plays a vital role in an efficient production process. Over the last few years, to form a trade-off between the quantitative and qualitative criteria, the selection of suppliers in SCM is considered very conclusive. The decisions generally demand different criteria to balance between every possible inconsistent parameters involving subjectivity and uncertainty in the process. The evaluation information mostly depends on the experience and knowledge of experts that are unsure and indistinct. This study introduces a novel decision-making method by integrating rough approximations with fuzzy numbers and preference ranking organization method for enrichment evaluation (PROMETHEE) to deal with subjective and objective vagueness in the assessment of decision makers. To minimize the dependency on experts’ judgements, entropy weights are computed from the original data set. The preference index is then computed using entropy weights and deviations among alternatives. The alternatives are ranked using the intersection of both positive flow and negative flow. To show the significance and importance of fuzzy rough PROMETHEE method, a case study of supplier selection in industrial manufacturing is discussed in detail. The developed method is effectively used to rank the supplier alternatives under given criteria. The results are then compared with different rough numbers and fuzzy numbers based on MCDM methods. The fuzzy rough PROMETHEE method can be efficiently used for the selection of the best suppliers to reduce losses and maximize the production process.

MCDM methods: Practical difficulties and future directions for improvement

This paper critically reviews practical difficulties inherent in some of the existing multi-criteria decision-making methods. The paper also emphasizes why a benchmark decision situation is essential in assessing the capabilities of any multi-criteria decision-making method. The capability is in terms of accuracy in modeling the human decision-making process. Most multi-criteria decision-making methods consist of two important steps. The first step involves elicitation of preferences from the decision-maker on various criteria and alternatives of the problem. In the second step, the preferences defined by the decision-maker are aggregated. The overall score generated after aggregation is used in rank order calculation and final selection. However, if the prescriptions of multi-criteria decision-making method do not resemble actual or real decision of the very same decision-maker, then multi-criteria decision-making method failed in either capturing the true preferences of the decision-maker or in aggregating these preferences as per the expectations of the decision-maker. This paper discusses some of the latest theories of decision-making and provides three important directions to improve the descriptive aspects of multi-criteria decision analysis.

A Two-Step Fuzzy MCDM Method for Implementation of Sustainable Precision Manufacturing: Evidence from China

Despite that products of precision manufacturing are widely used in many fields involving the national economy, precision manufacturing processes are more unfriendly to the environment, resources and social development than general manufacturing. Hence, the implementation of sustainable precision manufacturing (SPM) is of great strategic significance. There is no literature identifying and ranking the drivers of implementation of SPM and the impact on sustainability owing to the application of advanced manufacturing technologies in SPM has not been explored. To resolve these problems, drivers of SPM are identified based on combined support of prior studies and six groups of experts consisting of 71 individuals from six precision manufacturing enterprises. The drivers are calculated and ranked by a two-step fuzzy MCDM method which integrated the fuzzy AHP (fuzzy analytic hierarchy process) and fuzzy TOPSIS (fuzzy technique for order of preference by similarity to ideal solution) algorithms. The evaluation of drivers is based on the basic principles of sustainable development (environmental criterion, social criterion and economic criterion). The paper concludes that technological innovation, government support and current legislation are the most critical drivers during SPM implementation. Additionally, the result of sensitivity verification of the proposed method conducted proves the robustness and correctness of the algorithm and results.

PRODUCTIVE CAPACITY PERFORMANCE ANALYSIS OF G20 GROUP COUNTRIES: AN APPLICATION WITH ENTROPY BASED TOPSIS METHOD

Özellikle büyük ekonomilere sahip ülkeler üretim kapasitelerini iyileştirerek küresel ekonomiye katkılarını artırabilmektedirler. Bunun için ülkelerin üretim kapasitesi performansları konusunda farkındalık kazanması ve mevcut üretim kapasitesine göre stratejiler oluşturması için ülkelerin üretim kapasitesi performanslarının ölçümü büyük önem arz etmektedir. Bu kapsamda araştırmanın amacı, dünyanın en büyük ekonomilerine sahip olan G20 grubunda yer alan 19 ülkenin en son ve güncel olan 2000-2018 yıl aralığındaki Birleşmiş Milletler Ticaret ve Kalkınma Konferansı Üretim Kapasitesi Endeksi (PCI) bileşenlerine ait değerler üzerinden üretim kapasitesi performanslarını ENTROPİ tabanlı TOPSIS yöntemi ile ölçmektir. Bulgulara göre, ülkeler açısından ENTROPİ yöntemi kapsamında en önemli üretim kapasitesi bileşeninin ‘‘ulaşım’’ olduğu tespit edilmiştir. Devamında ülkelerin ENTROPİ tabanlı TOPSIS yöntemine göre üretim kapasitesi performansları en fazla olan ilk üç ülkenin Almanya, ABD ve Güney Kore olduğu gözlenmiştir. Ayrıca ülkelerin ortalama üretim performans değeri hesaplanarak söz konusu değerden düşük olan ülkelerin küresel ekonomiye katkılarının daha fazla olması için üretim kapasite performanslarını artırması gerektiği sonucuna ulaşılmıştır. Bunların dışında, yöntem kapsamında ülkelerin üretim kapasite performans değerleri ENTROPİ tabanlı bazı Çok Kriterli Karar Verme yöntemleri (ÇKKV: ARAS, COPRAS, EDAS, WASPAS, ROV, Gri İlişkisel Analiz) ile ölçülerek söz konusu değerler arasındaki ilişkiler Pearson korelasyon katsayısı ile ölçülmüştür. Bu ölçüme göre, PCI’nın başta ENTROPİ tabanlı TOPSIS yöntemi olmak üzere diğer ENTROPİ tabanlı ÇKKV yöntemleri ile açıklanabileceği değerlendirilmiştir.