a mixed edas/entropy mcdm method for selecting the best locations of establishing cement factories in iran considering corporate social responsibility measures

Traditional company managers tend to improve the cost efficiency of their supply chain networks. as a result, they strive to minimize the final price of their products to improve their customers’ satisfaction. however, in the current era, the issue of buying cheap products is not the only concern of consumers. indeed, the social concerns of manufacturers is an important factor for customers. the organizations that seek to improve social welfare are more favorable in the viewpoint of consumers. notably, the cement production industry is one of the industries that could be successful in iran owing to the availability of its needed raw materials. also, by the establishment of cement manufacturing factories, social welfare in different regions of the country could be positively affected. so, selecting the best locations of opening cement manufacturing facilities for improving the social responsibility level is an important issue. the notable matter is that there are different social responsibility factors that opening each production plant could affect each of these aspects differently. in this regard, this paper aims to propose a hybrid multi-criteria decision-making method for selecting and ranking of the best locations of establishing cement production factories in iran to maximize social welfare in different regions of the country. in this regard, two efficient methods are employed. in the first step, different social criteria are defined based on the opinion of field experts. then, the entropy method is applied to determine the weight of the criteria. finally, the edas method is used to define the final rank and weight of locations of opening the cement factories. notably, the output results prove that the hybridized method works efficiently and helps to effectively improve the social responsibility of supply chains. also, results show that the proposed method works efficiently and is applicable in real-world industrial case studies.