Compromise Solution Method Research Articles

A heterogeneous picture fuzzy SWARA-MARCOS evaluation framework based on a novel cross-entropy measure

Multi-criteria decision-making (MCDM) entails a heterogeneous decision-making problem, which poses challenges for decision-makers (DMs) in generating an optimal solution. To address this, we have proposed a heterogeneous evaluation framework. First, a novel picture fuzzy cross-entropy measure was defined with the simultaneous consideration of uncertainty and hesitancy of picture fuzzy information, overcoming the shortcomings of the existing cross-entropy measure in relation to its validity and properties. Next, an optimisation-model for determining the objective weights of criteria was constructed based on the proposed closeness measurement and the step-wise weight assessment ratio analysis (SWARA) method. This model was constituted from both objective and subjective perspectives under the circumstance of completely unknown criterion information. Additionally, the conventional Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method was extended to the picture fuzzy environment, and a normalisation method was developed to transform heterogeneous judgments, including linguistic terms, interval numbers, and picture fuzzy numbers, into a unified representation form. A heterogeneous picture fuzzy SWARA-MARCOS evaluation framework was then established and used to solve a military equipment supplier selection problem. The results demonstrated the validity and feasibility of the proposed evaluation framework, while sensitivity, comparative, and complexity analyses demonstrated the robustness and superiority of it.

An analysis of the inhibitors of resilience in the global value chains of multinational enterprises

PurposeThe COVID-19 pandemic has showcased the lack of resilience found in the global value chains (GVCs) of multinational enterprises (MNEs). Existing evidence shows that MNEs have only recently and slowly started recovering and attempting to rebuild the resilience of their GVCs. This paper analyzes the challenges/inhibitors faced by MNEs in building their resilience through their GVCs.Design/methodology/approachA four-stage hybrid model was used to identify the interrelationship among the identified inhibitors and to distinguish the most critical ones by ranking them. In the first stage, we employed a modified total interpretive structural modeling (m-TISM) approach to determine the inter-relationship among the inhibitors. Additionally, we identified the inhibitors' driving power and dependency by performing a matrix multiplication applied to classification (MICMAC) analysis. In the second stage, we employed the Pythagorean fuzzy analytic hierarchy process (PF-AHP) method to determine the weight of the criteria. The next stage followed, in which we used the Pythagorean fuzzy combined compromise solution (PF-CoCoSo) method to rank the inhibitors. Finally, we performed a sensitivity analysis to determine the robustness of the framework we had built based on the criteria and inhibitors.FindingsWe find business sustainability to have the highest importance and managerial governance as the most critical inhibitor hindering the path to resilience. Based on these insights, we derive four research propositions aimed at strengthening the resilience of such GVCs, followed by their implications for theory and practice.Originality/valueOur findings contribute to the extant literature by uncovering key inhibitors that act as barriers to MNEs. We link out our findings with a number of propositions that we derive, which may be considered for implementation by MNEs and could help them endow their GVCs with resilience.

An integrated decision-making method for corporate culture influence evaluation with intuitionistic fuzzy multiple attribute group decision-making

Corporate culture is an objective existence that arises with the rise and development of enterprises. It originates from enterprise practice and influences the behavior of employees. Whether it is intentional identification or unintentional avoidance, corporate culture is not a question of absence, but a question of quality; It’s not about non-existent issues, but about the magnitude of their influence. Therefore, building a corporate culture that conforms to the characteristics of the enterprise and is recognized by the majority of employees, continuously enhancing the influence of corporate culture, is a very important topic in the construction of corporate culture. The corporate culture influence evaluation is looked as the multiple attribute group decision-making (MAGDM) problem. The intuitionistic fuzzy sets (IFSs) are easy to depict the uncertain information during the corporate culture influence evaluation. Then, intuitionistic fuzzy Combined Compromise Solution (IF-CoCoSo) method is designed under IFSs. Furthermore, IF-CoCoSo is used to cope with the MAGDM. At last, an example is supplied for corporate culture influence evaluation to prove the practicability of the IF-CoCoSo method and some comparative analysis are conducted to verify the effectiveness of IF-CoCoSo method. Thus, the main objectives of this paper are outlined as follows: (1) the CRITIC method is used to obtain the weight information; (2) intuitionistic fuzzy Combined Compromise Solution (IF-CoCoSo) method is designed under IFSs; (3) IF-CoCoSo is used to cope with the MAGDM based on CRITIC weight information and Euclidean distance; (4) At last, an example is supplied for corporate culture influence evaluation to prove the practicability of the IF-CoCoSo method and some comparative analysis are conducted to show the effectiveness of IF-CoCoSo method.

Optimizing Priority Sequencing Rules in Parallel Machine Scheduling: An Evaluation and Selection Approach using Hybrid MCDM Techniques

Priority sequencing criteria are of utmost importance in the determination of the sequence in which jobs are processed at workstations in parallel machine scheduling. The utilization of diverse priority rules can result in varied sequencing arrangements, hence requiring more experimentation to ascertain the optimal rule. Hence, it is imperative to formulate a thorough approach for the selection of the most suitable priority sequencing rule from the standpoint of management decision-making. The objective of this research is to analyze and compare six different priority sequencing rules in the context of parallel machine scheduling. Additionally, a methodology is proposed for the assessment and selection of the most suitable rule. This methodology combines the full consistency method (FUCOM) with the measurement of alternatives and ranking according to compromise solution (MARCOS) method, which are both multi-criteria decision-making techniques. When reviewing and selecting the optimal priority sequencing rule, seven parameters are taken into consideration. The weights of these criteria are computed using the FUCOM method, while the relative proximity values of all priority sequencing rules are derived by the MARCOS method. The data indicate that the priority sequencing rules are prioritized according to their level of importance. The approach outlined in this study is essential for workstation management to make well-informed decisions regarding the choice of the most advantageous priority sequencing rule for parallel machine scheduling.

A q-rung orthopair fuzzy decision-making framework considering experts trust relationships and psychological behavior: An application to green supplier selection

The selection of an optimal supplier is a critical and open challenge in supply chain management. While experts' assessments significantly influence the supplier selection process, their subjective interactions can introduce unpredictable uncertainty. Existing methods have limitations in effectively representing and handling this uncertainty. The paper aims to address these challenges by proposing a novel approach that leverages q-rung orthopair fuzzy sets (q-ROFSs). The novelty of the proposed approach lies in its ability to accurately capture experts' preferences through the use of q-ROFSs, which offer membership and non-membership degrees, providing a broader expression space compared to conventional fuzzy sets. Additionally, it incorporates social network analysis (SNA) to effectively consider the trust relationships among experts. The proposed approach is divided into three stages. The first stage, presents a novel method to determine experts' weights by combining SNA, the Bayesian formula, and the maximum entropy principle. This approach allows for a more precise representation of varying levels of expertise and influence among experts, addressing the uncertainty arising from subjective interactions. The second stage introduces a hybrid weight determination method to determine criteria weights within the context of q-ROFSs. Entropy plays a crucial role in capturing fuzziness and uncertainty in q-ROFSs, while the projection measure simultaneously provides information about the distance and angle between alternatives. By employing both objective weights estimated using entropy and normalized projection measure and subjective weights derived using an aggregation operator and a score function, the presented approach achieves a comprehensive assessment of criteria importance. To incorporate both subjective and objective weights effectively, game theory is applied which allows us to align decision-making with both quantitative and qualitative aspects, making the method more versatile and applicable. The third stage redefines the traditional Combined Compromise Solution (CoCoSo) method using Bonferroni mean operators which captures interrelationships among arguments to be aggregated. Furthermore, in recognition of the importance of an expert risk preferences and psychological behaviors, we apply regret theory, ensuring that the chosen solutions align more effectively with their underlying preferences and aspirations. The applicability and effectiveness of the proposed approach are demonstrated through a numerical example of green supplier selection. The comparative analysis illustrates the practicality and real-world relevance while the sensitivity analysis, confirms the stability and robustness of the proposed approach.

A new similarity measure for rankings obtained in MCDM problems using different normalization techniques

The paper presents an analysis of the impact of normalization techniques on the ranking of alternatives obtained using the combined compromise solution (CoCoSo) method. Similarity measures known from the literature and a new measure called the TOPSIS similarity measure (TOPSIS-SM) are used to assess the resulting rankings. This new measure is based on the TOPSIS algorithm, where the arithmetic mean of the considered rankings is taken as the ideal solution. In contrast, the antiideal solution is divided into a minimum and a maximum solution, which exhibit maximum separation from the ideal solution. The results obtained by this new method are different from those obtained using other similarity measures known from the literature.

Mitigating Climate Change Effects of Urban Transportation Using a Type-2 Neutrosophic MEREC-MARCOS Model

Climate change is the foremost environmental problem that negatively affects the lives of people. Sustainable policies offer an opportunity to mitigate climate change effects of urban transportation. Four sustainable policies are presented to alter the propagation speed of climate change. However, there is a major gap regarding the prioritization of the sustainable policies in advantage order, which is an important problem for all municipalities. Therefore, this study introduces a two-stage model that hybridizes the method based on the removal effects of criteria (MEREC) and measurement of alternatives and ranking according to compromise solution (MARCOS) method into a unique framework under the type-2 neutrosophic number (T2NN) environment. In the first stage, the T2NN-MEREC determines the objective importance of the criteria. In the second stage, the T2NN-MARCOS method ranks the sustainable policies. Our practical findings indicate that land-use planning is the best sustainable policy for mitigating climate change effects of urban transportation. By applying this solution, municipalities may generate an extra budget from low emission zones and limited traffic zones in the short-term run. The introduced T2NN-MEREC-MARCOS model could be used as decision support for authorities while mitigating climate change effects of not only urban transportation but also other human activities.

Exploring the Role of Blockchain Technology in Improving Sustainable Supply Chain Performance: A System-Analysis-Based Approach

Due to the complexity of SSC practices, BT can be adopted as an innovative tool for addressing socioenvironmental issues. However, BT adoption has not been receiving growing attention because of organizational challenges (e.g., financial constraints). This study develops a system-analysis-based approach to investigate the impact of BT adoption on the improvement of SSC performance. This approach is proposed based on the FCM to model CRs between SSC-performance-related targets and enablers of BT adoption. These enablers include inherent features of BT identified in the context of sustainability (e.g., social responsibility and environmental sustainability). After developing an FCM model for the BT adoption problem, the impact of enablers on target concepts is investigated by implementing a hybrid FCM learning algorithm according to the extracted CRs. Based on the outputs of the FCM learning algorithm, this study also identifies the most effective enablers for improving SSC performance using the combined compromise solution method. The proposed system-analysis-based approach demonstrates that BT can significantly affect various dimensions of SSC networks’ performance. The results imply that the appropriate BT adoption supports SSC performance by improving environmental sustainability, creating smart contracts, and increasing traceability.

Multi-attribute decision-making based on similarity measure between picture fuzzy sets and the MARCOS method

In this paper, we propose a new similarity measure (SM) between picture fuzzy sets (PFSs). The proposed SM between PFSs can overcome the drawbacks of the existing SMs between PFSs. We also propose a weight-determination approach based on the proposed SM between PFSs to determine attributes’ weights in picture fuzzy environments. We also propose a new multi-attribute decision-making (MADM) approach based on the proposed SM between PFSs and the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method for MADM with picture fuzzy information. The proposed MADM approach can overcome the shortcomings of the existing MADM approaches based on PFSs. It provides us a very useful approach for MADM in picture fuzzy settings.

Development of the MCDM fuzzy LMAW-grey MARCOS model for selection of a dump truck

This study presents the MCDM model created for the selection of a dump truck for the needs of the army engineering units, based primarily on the truck’s construction features and purchasing and maintenance costs. In this study was used the Methodology of Additive Weights (LMAW) in Fuzzy surrounding for determination of weight coefficients of criteria, while for the selection of the optimal alternative (for a dump truck) it was used the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method, modified by interval grey numbers. Input data for this methodology were obtained by engaging experts. Finally, the analysis was made of the sensitivity of output results of the proposed MCDM methodology to the change of weight coefficients of criteria, as well as the comparison of the obtained results with the results of other methodologies. In the conclusion, the proposed model showed stability but it was sensitive to weight coefficients change which should be taken into account by defining the same by experts.

Renewable Energy Transition Task Solution for the Oil Countries Using Scenario-Driven Fuzzy Multiple-Criteria Decision-Making Models: The Case of Azerbaijan

The renewable energy transition of oil- and gas-producing countries has specific peculiarities due to the ambivalent position of these countries in the global energy market, both as producers and consumers of energy resources. This task becomes even more challenging when the share of oil and gas in the country’s GDP is very high. These circumstances pose serious challenges for long-term energy policy development and require compromising decisions to better align the existing and newly created energy policies of the country. The scale, scope, and pace of changes in the transition process must be well balanced, considering the increasing pressure of economic and environmental factors. The objective of this paper is to develop models that allow the selection of the most appropriate scenario for renewable energy transition in an oil- and gas-producing country. The distinguishing feature of the proposed model is that alternatives in the decision matrix are presented as scenarios, composed of a set of energy resources and the level of their use. Linguistic descriptions of the alternative scenarios are formalized in the form of fuzzy statements. For the problem solution, four different Multiple-Criteria Decision-Making (MCDM) methods were used: the fuzzy simple additive weighting (F-SAW) method, the distance-based fuzzy TOPSIS method (Technique of Order Preference Similarity to the Ideal Solution), the ratio-analysis-based fuzzy MOORA method (Multi-Objective Optimization Model Based on the Ratio Analysis), and the fuzzy multi-criteria optimization and compromise solution method VIKOR (Serbian: VIekriterijumsko Kompromisno Rangiranje). This approach is illustrated using the example of the energy sector of Azerbaijan. The recommended solution for the country involves increasing natural gas (NG) moderately, maintaining hydro, and increasing solar notably and wind moderately.

Technology adoption theories towards environmentally sustainable pharma 4.0: a rational selection approach

PurposeThis study is centered on the identification of the most appropriate Technology Adoption (TA) model for investigating the adoption of Industry 4.0 technologies within pharmaceutical and related enterprises. The aim is to facilitate a smooth transition to advanced technologies while concurrently achieving environmental sustainability.Design/methodology/approachSelection of a suitable TA theory is carried out using a hybrid multi-criteria decision-making (MCDM) approach incorporating PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and Fuzzy Measurement of alternatives and ranking according to Compromise solution (F-MARCOS) methods. A group of three experts is formulated for the ranking of criteria and alternatives based on those criteria.FindingsThe results indicate that out of all six TA models considered unified theory of acceptance and use of technology (UTAUT) model gets the highest utility function value, followed by the technical adoption model (TAM). Further, sensitivity analysis is conducted to confirm the validity of the MCDM model employed.Research limitations/implicationsChallenging times like COVID-19 pointed out the importance of technology in the pharmaceutical and healthcare sectors. TA studies in this area can help in the identification of critical factors that can assist pharmaceutical firms in their efforts to embrace emerging technologies, enhance their outputs and increase their efficiency.Originality/valueThe novelty of this research lies in the fact that the utilization of a TA theory prior to its implementation has not been witnessed in existing scholarly literature. The utilization of a TA theory, specifically within the pharmaceutical industry, can assist enterprises in directing their attention toward pertinent factors when contemplating the implementation of emerging technologies and achieving sustainable development.

An integrated three-way decision methodology for sustainability of wastewater circularity in thermal power plants

The presence of multiple criteria for evaluating wastewater reuse applications indicates the potential usage of Multi-Criteria Decision-Making (MCDM) methods for this purpose. However, there is currently a scarcity of studies in the domain literature that utilize MCDM approaches in this application topic. This paper therefore advances the domain literature in two distinctive ways. Firstly, it analyzes and advances the reuse agenda of wastewater from thermal power plants, recognized as large-scale users of water, thus promoting greater water circularity. Secondly, it provides a methodological advance by integrating the notion of Three-Way Decision (3WD) into the computational structure of MCDM methods by introducing a middle reference point. Such an initiative results in a novel 3WD extension of the Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) method. Additionally, this work provides a proof that the MARCOS method utilizes a compromise solution in identifying priority alternatives, along with the integration of a Weighted Aggregated Sum Product ASsessment (WASPAS) metric. An initial hypothetical example illustrates how the proposed approach augments the canonical MARCOS method, particularly in promoting the “thinking in threes” as a more natural information processing approach and the high degree of distinguishability of priorities between decision alternatives. An actual case study in a thermal power plant then demonstrates the contributions of this work. With the best-worst method used to determine the priorities of the decision attributes, the findings reveal that wastewater reuse applications achieving reduced costs for needed infrastructures, operational simplicity, technological compatibility, consumer safety and household savings are preferred by stakeholders. The 3WD-MARCOS approach identifies industrial and commercial use, municipal use, environmental restoration, and household use as the high-priority alternatives, with cooking and drinking as least preferred. These insights guide stakeholders in their design of initiatives that allocate resources for greater wastewater reuse. A comparative analysis yields high consistency of these findings with similar MCDM methods. In addition, the efficacy of the novel 3WD-MARCOS method highlights its potential in handling MCDM problems, including those promoting water circularity.

EXPERIMENTAL ANALYSIS OF WIRE-EDM ON SUB-COOLED LOW-CARBON TOOL STEEL USING HYBRID MARCOS METHOD AND HONEY BADGER ALGORITHM

AISI P20 tool steel is commonly used for engineering of die holders, rails, plastic molds, wear strips, bolsters, shafts, and backers. When normally heated and sub-cooled, it experiences metallurgical modifications. Because the essential aspect of the “wire-electro discharge machine (WEDM)” is product finishing, this research examines the parametric responses when machining parent metal and sub-cooled metal (AISI P20). Thus, it is always necessary to choose a proper parametric arrangement for reaching maximum material removal rate (MRR), cutting speed with minimum surface roughness (SR), and kerf width. The effects of pulse on time, wire speed, discharge current, wire tension, and flushing pressure on individual machining reactions are also discussed in this study. An orthogonal array was created using Taguchi’s mixed experimental approach. The measurement alternatives and ranking according to compromise solution (MARCOS) method, the Honey Badger Algorithm, and the Taguchi-MARCOS method are engaged to obtain the important machining constraints and the optimal parametric settings for each response. When compared to the parent material, sub-cooled material has a greater cutting speed, SR, kerf breadth, and MRR. Here, cutting speed, MRR, and fitness were maximized along with minimization of SR and kerf width using the Honey Badger Algorithm as compared to both the MARCOS and Taguchi-MARCOS methods. According to ANOVA, for both parent and sub-cooled material, discharge current was the most significant and influential factor. The sub-cooled material’s machined surface contains smaller deposits and more spherical globules than the parent material, which has bigger melted deposits.

Student Ranking Based on Learning Assessment Using the Simplified PIPRECIA Method and CoCoSo Method

The problems that occur in determining the best students based on the learning process of the assessment process are still based on the academic scores of students and have not considered the learning process carried out by students. This study aims to apply the Combined Compromise Solution (CoCoSo) method in ranking students based on learning assessment using criteria of academic progress, problem-solving ability, mastery of skills, independence, motivation and positive attitude, adaptability, and for weighting the criteria used to apply the Simplified PIPRECIA (Pivot Pairwise Relative Criteria Importance Assessment) weighting method. The Simplified PIPRECIA method is particularly useful in situations where there are diverse criteria to be considered and complex decisions must be made taking into account the preferences and interests of various stakeholders. The Combined Compromise Solution Method is useful when there are conflicts in various criteria that need to be considered in the decision-making process. With this approach, each criterion is weighted and carefully calculated, so that the resulting decisions reflect comprehensive considerations that can meet various requirements and constraints. Based on the results of student rankings based on assessments in learning in the table above, rank 1 was obtained by students with Student ID 1211313 with a final grade of 6.487, rank 2 was obtained by students with Student ID 1211316 with a final grade of 6.402, and rank 3 was obtained by students with Student ID 1211314 with a final grade of 5.814.

Facile and optimal evaluation model of intelligent waste collection systems based on the Internet of Things: a new approach toward sustainability

Waste management is a difficult and complicated issue. Since this waste may constitute a threat to persons and the environment, it is vital to guarantee that it is adequately collected. Therefore, new waste collection technologies that adopt modern technology and the Internet of Things (IoT) are the appropriate alternative. Determining the optimal intelligent technology for waste management and tuning its priorities is a complicated task that requires taking into account the dimensions of environmental, economic, and social sustainability. Thus, this paper introduces a hybrid methodology for multi-criteria decision-making (MCDM) that assesses intelligent waste management technologies utilizing IoT, taking into account multiple criteria. First, eleven decision-making criteria are determined to give a realistic approach. Second, the researchers demonstrated the innovative decision approach established on the combination of the Measurement of Alternatives and Ranking according to the COmpromise Solution (MARCOS) method and the Indifference Threshold-based Attribute Ratio Analysis (ITARA) method, called T2NN-ITARA, under a type-2 neutrosophic numbers (T2NNs) environment. This approach has been used to define the criteria’s relative significance. Also, T2NN-MARCOS approach has been established to evaluate and classify intelligent waste management technologies based on IoT and to reveal the most sustainable solution. An illustrative case study evaluating four intelligent waste management technologies based on IoT is presented to prove the validity of the applied methodology. The findings show that the criteria of sustainability and standardization are the two most influential criteria in the evaluation and classification of intelligent waste management technologies based on IoT. It has also been determined that the RFID and GPRS blend for waste management is the most suitable intelligent technology for garbage management. Sensitivity and comparison analyzes were also accomplished to illustrate the stability, strength, and robustness of the suggested approach. The research provides significant information for government and waste practitioners.

Enhanced CoCoSo method for picture 2-tuple linguistic MAGDM and applications to classroom teaching quality evaluation of college physical education

In order to better improve the physical health status of Chinese college students, enrich the knowledge content of university physical education courses, enhance their physical exercise ability, cultivate lifelong physical education awareness and conscious exercise habits, and promote the healthy development of quality education in China, combined with the current reality of university physical education teaching, a quality assurance system for university physical education teaching in China is constructed to promote the continuous improvement of the quality of university physical education teaching. The guarantee system for college physical education teaching consists of three subsystems: the design and quality management system for college physical education teaching objectives, the evaluation system for college physical education teaching quality, and the feedback system for college physical education teaching quality information. The quality evaluation of college physical education classroom teaching is a multiple attribute group decision making (MAGDM) issue. In this paper, depending on the CoCoSo (Combined Compromise Solution) method and picture 2-tuple linguistic sets (P2TLSs), a novel MAGDM issue based on the picture 2-tuple linguistic CoCoSo (P2TL-CoCoSo) method is designed to tackle it. In addition, the weights of criteria are decided objectively by utilizing entropy method. Eventually, a related application for quality evaluation of college physical education classroom teaching and comparative analysis is given to demonstrate the superiority of the designed method. The results illustrate that the designed method is useful for quality evaluation of college physical education classroom teaching.

Evaluating the risks of the internet of things in renewable energy systems using a hybrid fuzzy decision approach

Using the Internet of Things (IoT) and its integration into Renewable Energy Systems (RES) leads to energy planning, management of energy consumption and demand, and, as a result, sustainably matches energy supply and demand. There are several risks in implementing and integrating IoT in RES, which is an obstacle to successfully implementing these systems. Therefore, this research aims to assess the risks of implementing IoT in RES based on the hybrid Multi-Criteria Decision-Making (MCDM) approach under uncertain conditions in Iran. In this study, to specify the importance of criteria, the Fuzzy Stepwise Weight Assessment Ratio Analysis (F-SWARA) method is utilized, and to assess the risks of IoT integration in RES, the usage of the Fuzzy Combined Compromise Solution (F-CoCoSo) method is suggested. The following examines the sensitivity analysis and validation with fuzzy WASPAS, fuzzy ARAS, and fuzzy TOPSIS techniques. The findings showed that the proposed approach had adequate robustness and flexibility for risk assessment, and risks of investment, infrastructure development, and privacy and security were introduced as the most critical risks.

Evaluation of the Corporate Sustainability Performance of Manufacturing Companies in the BIST Sustainability Index with Multi-Criteria Decision Making Methods

This study aims to evaluate the corporate sustainability performance of manufacturing companies listed in the Borsa Istanbul (BIST) Sustainability Index using multi-criteria decision-making (MCDM) methods. For this purpose, by examining the literature, 11 criteria, including economic, social, and environmental, were determined and the criteria were weighted with method based on the removal effects of criteria (MEREC), one of the objective MCDM methods. According to the weights obtained through the MEREC method, the most important criteria were "return on equity", "operating profitability", and "asset profitability", while the least important criteria were "employee turnover rate", "training hours per employee" and "proportion of female employees". Subsequently, using the weights derived from the MEREC method, the companies were ranked using the CoCoSo (Combined Compromise Solution) method. According to the rankings, the companies demonstrating the highest corporate sustainability performance are Türk Traktor, Ford, Tofaş, and Otokar, while the companies with the lowest corporate sustainability performance are Kerevitaş, Coca-Cola, Petkim, and Tüpraş, respectively. Sensitivity analysis is carried out to test the consistency of the results obtained. Although these results contribute to the literature, in subsequent studies, MEREC and CoCoSo methods can be used to measure different performance criteria of companies. The fact remains that corporate sustainability performances of companies can be measured by using combinations of different MCDM methods.

Sustainable Evaluation of Major Third-Party Logistics Providers: A Framework of an MCDM-Based Entropy Objective Weighting Method

This study aims to efficiently assist decision makers in evaluating global third-party logistics (3PL) providers from the perspectives of economic, social, and environmental sustainability and explore the determinants of the 3PL providers’ performance. In doing so, an integrated framework for an MCDM-based entropy objective weighting method is proposed for the first time in a logistics industry assessment. In the first stage, the entropy method defines the weight of the decision criteria based on real data collected from the top 15 global 3PL providers. This study lists the prominent quantitative evaluation criteria, taking into consideration the sustainability perspective. The advantage of the entropy method is that it reduces the subjective impact of decision makers and increases objectivity. In the second stage, the measurement of alternatives and ranking according to compromise solution (MARCOS) method is used to rank the 3PL providers according to their performance on the basis of these criteria. Sensitivity analysis and comparative analysis are implemented to validate the results. The current research work is devoted to the emerging research topic of sustainable development in the logistics industry and supply chain management. The proposed model identifies key performance indicators in the logistics industry and determines the most efficient 3PL providers. Consequently, the results show that the carbon dioxide emissions (20.50%) factor is the most important criterion for the competitiveness of global logistics companies. The results of this study can help inefficient 3PL providers make strategic decisions to improve their performance. However, this study only focuses on 15 companies due to a lack of data. The integration of these two techniques provides a novel way to evaluate global 3PL providers which has not been addressed in the logistics industry to date and as such remains a gap that needs to be investigated.