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This study investigates an extended version of the combined compromise solution method with grey numbers, named CoCoSo-G for short, to measure the performance of suppliers in a construction company in Madrid. Seven criteria from a relevant previous study are the basis for assessing the performance of suppliers, while ten suppliers are composing our decision matrix. To initiate the decision-making process, we invite experts to aid us in the qualitative evaluation of the suppliers using grey interval values. Two weighting methods, including the DEMATEL (Decision Making Trial and Evaluation Laboratory) and BWM (best worst method) are used to achieve the importance of supplier criteria in a combined manner. The DEMATEL method is used to realise the best and worst criteria, and the BWM is used to sort the criteria according to a linear programming formulation.  The CoCoSo-G method used to release the score of each supplier and rank them. We compare the results obtained by the CoCoSo-G with those obtained by the Complex Proportional Assessment method. It is evident that offering grey values for supplier qualification, using the combined weighting tool and proposing the new CoCoSo-G approach facilitate the evaluation process while indicating trustable outcomes.

Purpose The purpose of this paper is to discuss the advantage of a combinatory methodology presented in this study. The paper suggests that the comparison with results of previously developed methods is in high agreement. Design/methodology/approach This paper introduces a combined compromise decision-making algorithm with the aid of some aggregation strategies. The authors have considered a distance measure, which originates from grey relational coefficient and targets to enhance the flexibility of the results. Hence, the weight of the alternatives is placed in the decision-making process with three equations. In the final stage, an aggregated multiplication rule is employed to release the ranking of the alternatives and end the decision process. Findings The authors described a real case of choosing logistics and transportation companies in France from a supply chain project. Some comparisons such as sensitivity analysis approach and comparing to other studies and methods provided to validate the performance of the proposed algorithm. Originality/value The algorithm has a unique structure among MCDM methods which is presented for the first time in this paper.

Adding green elements in logistics functions have biggest impact in shaping the supply chains towards sustainability. Business strategies must promote environmentally conscious thinking through continuous integration of green and evaluation of resultant business and environment sustainability performance. The approach is illustrated and validated through the development and analysis of sustainability initiatives implemented in warehouses of frozen food supply chains in Saudi Arabia. Modelled on a case study basis, this three-phase study builds on theoretical concepts of contingency theory and triple bottom line approach. It incorporates identification and ranking of essential sustainable practices of warehousing using literature analysis, participation of practitioners in fuzzy Delphi and Best Worst Method. Further, study establishes its uniqueness by applying combined compromise solution to rank the resultant sustainability performance improvement in warehouses. The results draw attention to green operations for energy and resource conservations, promotes the role of sustainable work culture, sustainable strategies, and policies for their role in encouraging sustainability performance outcomes.

The financial risk evaluation is critically vital for enterprises to identify the potential financial risks, provide decision basis for financial risk management, and prevent and reduce risk losses. In the case of considering financial risk assessment, the basic problems that arise are related to strong fuzziness, ambiguity and inaccuracy. q-rung orthopair fuzzy set (q-ROFS), portrayed by the degrees of membership and non-membership, is a more resultful tool to seize fuzziness. In this article, the novel q-rung orthopair fuzzy score function is given for dealing the comparison problem. Later, the and operations are explored and their interesting properties are discussed. Then, the objective weights are calculated by CRITIC (Criteria Importance Through Inter-criteria Correlation). Moreover, we present combined weights that reflects both subjective preference and objective preference. In addition, the q-rung orthopair fuzzy MCDM (multi-criteria decision making) algorithm based on CoCoSo (Combined Compromise Solution) is presented. Finally, the feasibility of algorithm is stated by a financial risk evaluation example with corresponding sensitivity analysis. The salient features of the proposed algorithm are that they have no counter-intuitive case and have a stronger capacity in differentiating the best alternative. First published online 03 March 2020

The 5G industry is of great concern to countries to formulate a major national strategy for 5G planning, promote industrial upgrading, and accelerate their economic and technological modernization. When considering the 5G industry evaluation, the basic issues involve strong uncertainty. Pythagorean fuzzy sets, depicted by membership degree and non-membership degree, are a more resultful means for capturing uncertainty. In this paper, the comparison issue in Pythagorean fuzzy environment is disposed by proposing novel score function. Next, the ⊖ and ⊘ operations are defined and their properties are proved. Later, the objective weight is calculated by Criteria Importance Through Inter-criteria Correlation method. Meanwhile, the combined weight is determined by reflecting both subjective weight and the objective weight. Then, the Pythagorean fuzzy decision making algorithm based Combined Compromise Solution is developed. Lastly, the validity of algorithm is expounded by the 5G evaluation issue, along with their sensitivity analysis. The main advantages of proposed algorithm are: (1) have no counterintuitive phenomena; (2) without division or antilogarithm by zero problem; (3) own stronger ability to distinguish alternatives.

Under the influence of circular economy theory, waste clothing recycling has been widely studied in the resource sector, and the waste clothing recycling channel (WCRC) is the vital link that affects the recycling efficiency of waste clothing. How to select the optimal WCRC is considered a typical multiple attribute group decision-making (MAGDM) problem. In this article, we develop sine trigonometric interaction operational laws (IOLs) (STIOLs) using Pythagorean fuzzy information. The sine trigonometric interaction Pythagorean fuzzy weighted averaging (STI-PyFWA) and sine trigonometric interaction Pythagorean fuzzy weighted geometric (STI-PyFWG) operators are advanced, and their several desirable properties are discussed. Further, we build a MAGDM framework based on the modified Pythagorean fuzzy CoCoSo (Combined Compromise Solution) method to solve the WCRC selection problem. The combined weight of attributes is determined, and the proposed aggregation operators (AOs) are applied to the CoCoSo method. A Pythagorean fuzzy distance measure is used to achieve the defuzzification of aggregation strategies. Finally, we deal with the WCRC selection problem for a sustainable environment by implementing the proposed method and performing sensitivity analysis and comparative study to validate its effectiveness and superiority.

The ranking of solutions to determine the best one among many solutions is always the setting goal for all activities of all fields in general and in the turning process in particular. When a solution is evaluated by multiple criteria, this is known as “Multi-Criteria Decision Making - MCDM”. Many MCDM methods were proposed by scientists, however, the ranked results of the solutions are not the same. In addition, the ranked results of the solutions also depend on the weighting methods of the criteria. In this study, the ranking of the solutions in the hole turning process was performed by different MCDM methods and with different weighting methods. Five MCDM methods were mentioned in this study including COCOSO, MABAC, MAIRCA, EAMR, and TOPSIS. In this study, five weighting methods were also used including MEREC weight, EQUAl weight, ROC weight, RS weight, and FUCOM weight. The combination of MCDM and weighting methods creates twenty-five ranking results of the solutions. It is interesting to note that all twenty-five ranking results determine the same best solution. The stability in ranking the solutions by MCDM methods was also discussed in this study. From the obtained results, several recommendations were drawn. Some issues that have not been solved in this study and need to be done in near future are also mentioned in the last section of this study.

Nowadays, customers are not only interested in the quality of products, but they also want to have these products in a timely manner. The managers of an organization are faced with two problems when the distribution of products is in question, namely: (1) customers are usually geographically dispersed and (2) transportation should be performed in a cost-effective way. Although managers may have a significant experience and formal knowledge, decisions connected with the selection of an appropriate transportation company may very often be biased. For the purpose of avoiding making the inadequate decisions that might harm the operation of the organization, the application of a hybrid MCDM model is proposed in this paper. The proposed model consists of three fuzzy MCDM methods, including: the PIPRECIA, the PSI, and the CoCoSo methods. The fuzzy-PIPRECIA method is used to achieve the subjective weights of criteria, whereas the fuzzy-PSI method is used to obtain the objective weights of criteria. Fuzzy-CoCoSo is utilized to rank alternative transportation companies according to their performances. The possibilities of the proposed hybrid model are tested on a real case study pointed at the selection of an appropriate company for the transportation of ready-garments to retailers in Turkey. First published online 07 July 2021

Attaining green environment for various machining processes has now caught the attention of many manufacturing industries. The input parameters involved in those machining processes are mainly responsible for achieving the desired performance as they are directly related to the process outputs. Hence, proper selection of the input process parameters becomes vital for having sustainable machining environment. In this paper, an integrated application of step-wise weight assessment ratio analysis (SWARA) and combined compromise solution (CoCoSo) methods is presented to identify the optimal parametric combinations of two green dry milling processes. In the first example, cutting speed, depth of cut, feed rate and nose radius are treated as the input parameters, while power factor, electric consumption and surface roughness are the responses. On the other hand, in the second example, cutting speed, feed rate, depth of cut and width of cut, and surface roughness, active cutting energy and material removal rate are respectively considered as the input parameters and responses. Instead of considering equal weights, SWARA method assigns relative subjective importance to the responses based on the preference set by the decision-makers, while CoCoSo ranks the experimental trials from the best to the worst. The derived optimal parametric settings are finally analyzed using the developed regression equations. It is observed that SWARA-CoCoSo method outperforms the other popular optimization techniques in identifying the best parametric intermixes for the green dry milling processes for having improved machining performance with minimal environmental effect.

PurposeModern technologies are seen as an essential component of the fourth industrial revolution (industry 4.0) and their adoption is vital to transform the existing manufacturing system into industry 4.0-based manufacturing system. Therefore, the primary objective of this research explores the barriers of modern technology adoption and their mitigating solutions in order to align with Industry 4.0 objectives.Design/methodology/approachBarriers to adopting modern technologies and respective mitigating solutions are identified from the available literature. Further, these barriers are ranked with the help of expert opinions by using the BWM method appropriately. The identified solutions are ranked using the combined compromise solution (CoCoSo) method.FindingsSeveral modern technologies and their capabilities are recognised to support the industry 4.0-based manufacturing systems. This study identifies 22 barriers to the effective adoption of modern technologies in manufacturing and 14 solutions to overcome these barriers. Change management, the high initial cost of technology and appropriate support infrastructure are the most significant barriers. The most prominent solutions to overcome the most considerable barriers are ‘supportive research, development and commercialisation environment’, ‘updated policy and effective implementation’ and ‘capacity building through training’ that are the top three solutions that need to be addressed.Research limitations/implicationsThe barriers and solutions of modern technology adoption are obtained through a comprehensive literature review, so there is a chance to ignore some significant barriers and their solutions. Furthermore, ranking barriers and solutions is done with expert opinion, which is not free from biases.Practical implicationsThis identification and prioritisation of barriers will help managers to understand the barriers so they can better prepare themselves. Furthermore, the suggested solutions to overcome these barriers are helpful for the managers and could be strategically adopted through optimal resource utilisation.Originality/valueThis study proposes a framework to identify and analyse the significant barriers and solutions to adopting modern technologies in the manufacturing system. It might be helpful for manufacturing organisations that are willing to transform their manufacturing system into industry 4.0.