Supplier selection management has been considered as an important subject for industrial organizations. In order to remain on the market, to gain profitability and to retain competitive advantage, business units need to establish an integrated and structured supplier selection system. In addition, environmental protection problems have been big solicitudes for organizations to consider green approach in supplier selection problem. However, finding proper suppliers involves several variables and it is critically a complex process. In this paper, the main attention is focused on finding the right supplier based on fuzzy multi criteria decision making (MCDM) process. The weights of criteria are calculated by analytical hierarchical process (AHP) and the final ranking is achieved by fuzzy technique for order preference by similarity to an ideal solution (TOPSIS). TOPSIS advantage among the other similar methods is to obtain the best solution close to ideal solution. The paper attempts to express better understanding by an example of an automobile manufacturing supply chain.

Wind farms are designed to supply power to the consumers at a minimal price. The cost of wind power production directly or indirectly depends on proper selection of vendors. The present paper highlights a model for selection and ranking of vendors for a wind farm based on fuzzy set theory to determine criteria weights and an additive ratio assessment (ARAS) method to analysis criteria values. The objective of the paper is to establish the ARAS method as an effective method for Vendor selection. A case study is shown to ascertain the proposed method especially when the criteria are interdependent and conflicting in nature. The result is validated with another popular MCDM technique, COPRAS, which shows that the models are effective and applicable, and provide decision makers with better solutions for decision making.

The pharmaceutical industry is vital for global health, supplying necessary medicines, yet its conventional supply chain has notable environmental and social impacts. Amid a growing sustainability focus across sectors, the pharmaceutical industry must also adopt sustainable practices throughout its supply chain. This includes lessening its ecological impact, curbing waste, and endorsing social responsibility. Assessing a supplier's environmental performance, or "green performance," is of great interest. This involves gauging their eco-friendly actions like energy efficiency, waste management, and carbon footprint reduction. Metrics cover certifications, resource conservation, and responsible sourcing. In a study within a renowned Bangladeshi pharmaceutical firm, a key drugs manufacturer, seven criteria were used to evaluate suppliers' green performance. For this multi-criteria decision-making (MCDM) task, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied, considering a fuzzy environment. It ranked alternative suppliers via a widely used approach using linguistic terms expressed as Triangular Fuzzy Numbers (TFN). Important weights were determined via the Center of Area (COA) method. The study revealed supplier 4 as the top performer in green performance among five alternatives. This study introduces an innovative strategy for manufacturing decision-makers to choose the most suitable green supplier. It's anticipated to aid decision-makers in emerging economy pharmaceutical industries, facilitating the efficient evaluation of economically viable and environmentally sustainable suppliers for the long term.

During the Covid-19 pandemic, when strict restrictions were imposed to protect public health, e-commerce played a significant role in providing products on time. E-commerce technology and big data analytics enable companies to gain competitive advantages and respond to customers more efficiently. To make e-commerce more sustainable, the three dimensions of sustainability must be met, otherwise it can have negative consequences that lead to ecosystem destruction. Thus, e-commerce must learn how to effectively manage certain aspects of sustainability and adapt its operations to achieve balance. E-commerce's impact on sustainability can be measured in three pillars: economic, social and environmental and achieving a balance among these is the ultimate goal of sustainable development. Although the sustainability issue and big data analytics have gained increasing popularity in recent years, there is still a gap in evaluating applications of big data based on sustainable development indicators. In this study, we used a hybrid multi-criteria decision-making technique combining fuzzy TOPSIS and BWM to assess big data applications in e-commerce considering sustainable development indicators. The results showed environmental sustainability and energy consumption efficiency received the highest weight for the main pillars and sub criteria of sustainability indicators. Coordinating and monitoring supply chain processes, innovating product, process and business models, and creating new products and services are the top three applications of big data in e-commerce considering sustainable development indicators. E-commerce managers and experts can make better decisions about sustainable approaches by prioritizing big data applications based on sustainable development indicators. In addition, the proposed approach can also be used to evaluate big data analytics in other industries that consider sustainable development indicators.

Building Maintenance plays an important role throughout the building lifecycle from devis-ing conceptual plans to the end. Due to the high cost of building maintenance and the direct impact of maintenance effectiveness on the quality of life of building occupants, special attention must be devoted. One of the most important issues in this field is building maintenance contractor selection. This issue becomes even more critical in public buildings, such as hospitals, offices, and military centers. The purpose of this study is to present a method that can be used to select the contractor in such a way that the response robustness is high and the employed method is the most accurate one among other similar methods. To do this, the contractors are ranked by 7 multi-criteria decision-making methods. Then, the Spearman correlation coefficients are obtained for each pair of methods. When there is a significant difference between the outcomes of the methods, the output of each method is compared with the output of the Artificial Neural Network (ANN) model. The method with the least difference with the neural network output is taken as the superior method. After selecting the best method, a robustness analysis is performed on it to verify the stability of the answer. The proposed model is implemented on a real case study. Statistical analysis shows that the implementation of this method has increased the satisfaction of the residents.

The main objective of this research article is to select the best mobile model among various alternatives available on the market. For this analysis 10 alternative models from different brands are selected from different online shopping website having different specifications and ranging from low budget to medium budget in terms of price. For this selection purposes two multiple criteria decision making tools (MCDM) has been adopted i.e. Complex Propor-tional Assessment (COPRAS) and Additive Ratio Assessment (ARAS). The selection process is done based on four important criteria i.e. price, internal storage, RAM and brand. The weightages of the criteria are calculated by using Analytic Hierarchy Process (AHP) and these weightages are further used in COPRAS and ARAS methods. Individual COPRAS and ARAS method is applied for the selection of the best mobile and the preference ranking order of the models are also proposed by each process. The proposed ranking order by both the methods are compared and it is found that the outcome results are more or less the same using both techniques but there is a slight change in ranking of the middle-order alternatives. Both processes give model 1 and model 4 as the best and the worst models respectively among 10 alternatives.

In real world multi criteria decision making (MCDM) problem, it is tough to solve a decision matrix with vague and imprecise data. The degree of impreciseness depends on the kind of data avail-able. For interval valued data this impreciseness is less and interval-valued MCDM methods can be effectively used to solve the problem. A flexible manufacturing system (FMS) selection prob-lem was taken into consideration to find the best FMS among available alternatives. An interval extension of CODAS method is proposed in this paper which was used to solve the problem along with two other interval-valued decision-making methods i.e. interval-valued TOPSIS, interval-valued EDAS. All the three methods are distance-based approaches and it was found that the interval-valued CODAS method gave the exact same ranking with that of interval-valued TOPSIS and interval-valued EDAS.

Generation of solid waste is the integral consequence of civilization and its management is stand-ing as a big challenge in front of state government at present time. Municipal solid waste in Durg-Bhilai is littered with improper places due to lack of understanding about the scientific im-portance of garbage and its consequences. Our foremost concern is the selection of appropriate dumping area nearby Durg-Bhilai (C.G.) and for the selection of appropriate dumping area, a multi criteria decision making technique is used. This paper attempts to evaluate 4 dumping areas based on 5 criteria. Analytical hierarchy process (AHP) is used to determine the weighs for each criterion and quality function deployment (QFD) is also implemented. The results indicate that Potiyakala was the best dumping site nearby Durg-Bhilai area.

The ready-to-wear sector is expanding quickly and already accounts for a significant contribution to Ethiopia's export revenue. Due to its lower labor and investment costs, availability of resources, government assistance, etc. than other sectors, this industry attracts young entrepreneurs from around the nation as investment destinations. However, while investing in the leather industry, there are frequently many more aspects to consider. Again, there are numerous leather investment opportunities in the leathers sector, including handbags, jackets, belts, shoes, gloves, etc. Different sorts of leather items necessitate various levels of capital investment, operator skill sets, and resource requirements, among many other things. Again, based on their capacity for managing businesses, available capital, business location, and other factors, not all investors are in the same position. To assist investors in making decisions on the opening of a new leather industry, this paper presents a FAHP methodology for choosing the best leather item among various possibilities in a fuzzy environment. The methods enable the decision-maker to incorporate language expressions of expert judgment into the evaluation process. Using the suggested process, the ideal leather item is chosen for the launching of a leather industry in Ethiopia under the current circumstances.

A new flexible and adjustable model which is based on an Intuitionistic Fuzzy TOPSIS model with an exponential-related function and intuitionistic fuzzy entropy method has been presented and apply in this study. For supporting designers in determining design stakeholders’ preferences when finding the best compromise between new concepts design during early product develop-ment phase. The main advantage of the new model is that it is flexible and adjustable and affords the design stakeholders the option to a change of action in the event they receive new information or reason not to continue with a particular design or product. Finally, it uses both subjective and objective weights methods for the evaluation of the design concepts criteria, which makes the proposed model more realistic and more practical. The model has been applied for the design concept evaluation of seven forklift trucks with the view to improving the reliability of the machine.