Article Preview
TopIntroduction
The iron and steel industry is an important indicator of economic development and the level of industrialization within a country. In 2013, along with depth adjustment in the world economy, economic recovery of developed countries was slow. Developing country economies continued to experience structural adjustment, and the global iron and steel industry face a slowdown in demand. In this context, overcapacity, trade friction, environmental protection and other constraints severely hinders the global iron and steel industry. Therefore, the industry needs a higher level of innovation to meet these challenges, especially environmental protection. In both developed and developing countries, resource and energy conservation and an environment-friendly premise have become the consensus of the global iron and steel industry.
China has become the largest steel producing country in recent years and the government has focused on the development of the iron and steel industry. The industry faces a variety of hurdles; Overcapacity, environmental pollution and economic inefficiency of low technical or irrational allocation restricts sustainable development and companies need to identify shortcomings.
In this paper, the authors attempt to employ an effective method in multiple criteria decision making (MCDM) problem, namely data envelopment analysis(DEA). To evaluate the relative efficiencies of a set of peer decision making units (DMU), it’s first proposed the data envelopment analysis (DEA) method, a nonparametric, multifactor, productivity analysis tool which considers multiple inputs and multiple outputs (Charnes, Cooper & Rhodes, 1978). Not only does it determine DMU efficiency, but also provides useful management information. Since the advent of DEA in 1978, there has been an impressive growth both in theoretical developments and applications. The application scope of DEA includes banking, health care, transportation, environment, energy, etc. (Zhou et al., 2008; Chang et al., 2013; Bi et al., 2014; Zhu et al., 2014).
Since the advent of DEA in 1978, there has been an impressive growth both in theoretical developments and applications of the ideas to practical situations. DEA method has been widely used in the evaluation of production efficiency. The idea of estimating production efficiency scores is firstly proposed in a nonparametric framework (Farrell, 1957). Efficiency scores of production units are measured by their distance to an estimated production frontier. DEA is nonparametric, in the sense that no particular functional assumptions are made on the production frontier before the evaluation. Relies on convexity assumptions of the attainable set of productions, when allowing for variable returns-to-scale (VRS), the frontier is taken as the boundary of the convex hull of the set of observations in the input/output space (Shephard, 1970). In terms of linear programming techniques, the DEA estimators have been extensively used in the economic and business literature.