**2. Literature Review on Location Selection**

The sustainable development of supply chains has been a hot topic in recent years. In the past, a lot of research has focused on the discussion of supply chain management framework and evaluation methods [24–27]. Kusi-Sarpong et al. [25] raised the viewpoints of sustainable innovation to discuss supply chain management and believed that the sustainability of supply chain management will depend on innovation. Other scholars held different opinions, discussing the location and sustainability of production plants and finally determined that environmental, social and economic perspectives are important factors influencing location decisions [28–30]. The choice of location is an important company level decision-making problem. It can be divided into two parts—"how" (ownership and governance strategies) and "where" (location strategy) [31]. This study will focus on the analysis of the "where" part, specifically by design in a framework for the "selection of the best geographical location" for manufacturing plants. Past methods used for analyzing location selection can roughly be divided into two categories—qualitative and quantitative models. Most of the quantitative models are based on statistical or economic models. For example, Reference [32] used an economic model to analyze the factors affecting location selection. They found that per capita gross domestic product (GDP), GDP growth rate, agglomeration and government spending have a significant influence on location selection. Some studies argue that institutional quality and natural resources are also important factors influencing location selection [33]. Shuyan and Fabuš [34] used the spatial economic model to analyze the problem of location selection and found that market size and investment freedom were the most important factors for Chinese companies investing in the EU. In addition, the market size, technical level and investment freedom of the host country all have significant influences on location selection for China's foreign direct investment in the EU. He and Romanos [35] used regional taxation as the basis of analysis via regression models of the companies' location preferences and explored influence relationships in vertical and horizontal industrial linkages. The results indicate that both types of links have significant positive impacts. They also found that high taxation will hinder companies from choosing locations in these areas. Wyrwa [36] constructed a theoretical model based on structural equation modeling to explore influence of market size, labor costs, workforce quality and workforce availability on site selection. In contrast, the number of studies using qualitative methods have been relatively few. Wang et al. [37] used triangulation data collection combined with a qualitative research method to explore the determinants of location selection for enterprises in the biotechnology industry from the perspective of market expansion. Rahman and Kabir [38] used the Geographic Information System (GIS) to analyze the location pattern, then applied qualitative analysis to discuss the causes of forming clusters or localization for the manufacturing industry.

The above quantitative models which rely upon data collection, economic or statistical model analysis and hypothesis testing need long term and massive data collection, which might not be practical or reflective of rapid changes in the markets. In the past few decades, the MCDM method has been applied in the location selection problems. The advantages of MCDM are that it is—(1) simple to operate and suitable for complex practical problems; (2) can provide decision makers with clear information for reference; (3) more comprehensive in relation to the level of consideration and criteria, which is helpful to recognize the problem status; (4) supports the evaluation of multiple alternatives with qualitative or quantitative data [39–42].

The most popular MCDM method is Satty's AHP. Marinkovi´c et al. [11] used a two stage Delphi and AHP methodology to analyze and formulate the determinants for location selection for the ICT industry and confirmed the relative significance of these factors. They found human resource availability to be the primary factor, followed by the political and economic environment. Some have used the Delphi, AHP, Preference ranking organization method for enrichment evaluation (PROMETHEE) methods to select the location of manufacturing factories, with consideration of factors such as skilled workers, expansion possibilities, availability of required materials, investment costs and on-site risk assessment [7]. Wang et al. [43] given the consideration of human semantic ambiguity, used fuzzy Analytic Network Process (ANP) to explore the issue of location selection and found

that small and medium-sized enterprises give priority to costs over regulations and communities, while large enterprises give priority to regulations over costs and community.

AHP and ANP are quite mature in the application of MCDM field. Although these methods can evaluate a complicate system based on the pairwise comparisons between the criteria, it is a time-consuming process and not easy to obtain the consistent results. Therefore, in recent years, there have been many advantages by using Best-worst method (BWM) [25,44]. This method selects the best and worst criteria first and then compares them with other criteria in pairs, effectively reducing the number of pairwise comparisons and obtaining better consistent results. In addition, some scholars use different methods. For example, Rocha et al. [45] used evolutionary game theory and input–output analysis to evaluate a company's strategic location selection. They considered many exogenous factors—potential markets, local productive interdependence, tax incentives and macroeconomic stability. Studies have found that a location in a tax-free market is not necessarily the best choice and that there is a direct relationship between government incentives and regional attractiveness. Liu [6] applied the DEMATEL technology to analyze the causality and discuss the key factors for location selection. Their results showed production costs to be the most influential and industry characteristics to be the least influential factor.

Although the methods discussed above perform well, most of them ignore the fact that the criteria are actually interactive in the real world [42,46]. The DANP-mV model is suitable for improving this weakness and has been applied in many fields [47–52]. However, the DANP-mV is based on the subjective opinions of decision-makers. To remedy this shortcoming, the entropy method can be combined with the DANP method, thereby effectively reducing the limitation of subjective weighting in the DANP method. In practice, one also can adjust the ratio between subjective and objective weights based on decision needs. Therefore, the new model has the merit of being closer to the real environment.
