Evaluation and Selection of Cement Suppliers under the Background of New and Old Driving Energy Conversion in China

Abstract

Accompanied by the concept of supply-side structural reform and conversion of new and old driving energy to create a new round of economic development in China, cement supplier evaluation and selection are increasingly crucial for concrete production plants, ensuring not only raw material supply with high quality and at a reasonable price but also sustainable and long-term cooperation with suppliers. Given the limitations of the existing approaches, this study proposes a cement supplier evaluation and selection framework based on the combination of the improved FAHP-CRITIC method and VIKOR method. We first develop a cement supplier evaluation and selection index system under the background of new and old driving energy conversion, including eight first-level indicators and twenty-one second-level indicators. The proposed model then uses triangular fuzzy numbers AHP (TFN-AHP) and the improved CRITIC method to calculate subjective and objective weights by replacing the coefficient of variation with standard deviation, uses the ideal solution-based method to determine their combination weights, and combines the VIKOR method to calculate the comprehensive evaluation values of candidate cement suppliers. After that, the proposed approach is applied to evaluate and select ten cement suppliers for concrete production plants, and the results are compared and analyzed with those using the traditional method. The results of the comparison show that the proposed strategy can be scientific and reliable, helping managers to make the right decision under the background of new and old driving energy conversion in China.

1. Introduction

In the context of global economic structural transformation and the development of strategic emerging industries in recent years, China has entered the “New Normal” in terms of economic growth, and put forward the concept of supply-side reform and the replacement of the old driving energy force with new ones to create a new round of economic development [1,2]. In essence, the main goal is to promote structural adjustment, reduce inefficient and low-end supply, and improve total factor productivity. New and old driving energy conversion is regarded as the primary strategy to realize innovation-driven and economic development at high speed and with high quality. From the worldwide perspective, it is not only an objective law of the evolution of the world economy, but also an inevitable requirement for the sustained development of the new technological revolution. From the domestic perspective, the new and old driving energy conversion is also a fundamental way for China to rise to the middle and high end of the global value chain and enter a new economic era [3]. At present, we are in the critical period of the force transformation of economic development from factor-driven to innovation-driven. The new and old driving energy conversion has affected every walk of life, directly or indirectly, in recent years.

As a base for the national economy, the concrete industry plays an important role in the construction industry and has an important impact on China’s economic development. Although concrete production plants do not need excessive consideration regarding energy saving and transition, their upstream enterprises, such as cement suppliers, are often affected by laws and regulations on reducing “high energy consumption and high pollution” processes. In detail, cement is the essential and indispensable raw material for concrete production plants; it significantly determines the concrete quality, and therefore determines the economic benefit. As a high energy-consumption and high-pollution industry, cement manufacturing has become one area of focus in terms of high-quality development. Under the background of new and old driving energy conversion in China, laws and regulations on “replacing the old driving force with new ones” have been formulated and issued in recent years that have put a heavy burden on cement production enterprises [4,5]. On the one hand, some enterprises will be ordered to shut down because they do not meet the requirements of industrial upgrading and sustainable development; on the other hand, concrete production plants are likely to be disturbed or even halted by a severe cement supply shortage. In this way, concrete production plants must take into account not only suppliers’ cement price and quality, but also their speed of energy-saving emission reduction and the replacement of the old driving energy force. Currently, many concrete production plants have some problems in evaluating and selecting cement suppliers, involving several indicators related to new and old driving force conversion, such as less scientific and technological innovation indicators and low-carbon environmental protection indicators. They have been forced to reduce production with increasing frequency because cement suppliers have failed to supply them on time in recent years [6]. Therefore, it has become an urgent problem in the field of management decisions for concrete production plants to improve their capability for cement supplier evaluation on metrics such as effectiveness, feasibility, and sustainability.

Many researchers have paid attention to supplier evaluation from different directions, and several efficient approaches have been proposed to improve accuracy and rationality for evaluation, including Delphi, AHP, DEA, TOPSIS, the BP neural network, and so on [7,8,9,10]. However, few studies have been undertaken to evaluate and select the suppliers under the background of new and old driving force conversion, and no one has considered cement suppliers for concrete production plants to our knowledge. In particular, most enterprises put more emphasis on upgrading and transforming themselves, rarely considering their upstream suppliers [11]. Consequently, a new and scientific evaluation system and selection framework should be proposed to evaluate cement suppliers under the background of new and old driving energy conversion in China [12,13]. Based on the above analysis, this study aims to establish a cement supplier evaluation index system under the background of new and old driving energy conversion, and calculate their weights using triangular fuzzy numbers and the improved CRITIC by replacing the coefficient of variation with standard deviation and using the absolute value of correlation coefficient in calculation. Comprehensive evaluation values are calculated by combining this with the VIKOR method. After this, the proposed approach is applied to ten cement suppliers for concrete production plants, and the results are compared and analyzed against those using the traditional evaluation method, to provide a theoretical basis and suggestions for decision making.

The remainder of the paper is arranged as follows. We first review the relevant literature on research issues, and then develop a cement supplier evaluation index system for concrete production plants under the background of new and old driving energy conversion in China, including eight first-level indicators and twenty-one second-level indicators. Next, we present the cement supplier evaluation and selection framework based on improved FAHP-CRITIC and the VIKOR method in Section 4 and Section 5. After this, the results are compared and analyzed with those using the traditional evaluation approach in Section 6. Finally, Section 7 presents the conclusions, limitations, and future research prospects.

2. Literature Review

In this section, we will review the relevant literature on research issues, including the concept of new and old driving energy conversion in China, and current supplier evaluation indicators and methodologies.

2.1. New and Old Driving Energy Conversion

New and old driving energy conversion, also called replacing old growth drivers with new ones, was first put forward in China in the recent past, with a similar study abroad named “industrial transformation and upgrading” [14,15,16,17]. Its main goal is to constitute the new forces that can support the growth of the new normal economy in China, including the new economy formed by new products, new forms of business, new models, and the upgrading of traditional industries that are “new from exiting” together. In detail, it includes four levels: factors, enterprises, industries, and society. Additionally, the new and old driving energy conversion can be realized from government-led to government-guided projects, from economic fields to social fields, from critical demonstration to all-round promotion, from factor-driven to innovation-driven, and from progressive innovation to subversive innovation [18]. Compared with the domestic research in China, foreign industrial transformation and upgrading started earlier, and shifted more quickly from old driving energy to new energy, from traditional industries to emerging industries [15,16,17,18]. Yang H. X. and Jiao Y. analyzed the connotation and mode of new and old driving energy conversion from two perspectives: technology efficiency and technological progress [19]. Reference [20] designed a package of evaluation criteria based on the policy objectives and implementation characteristics of the conversion, which provided a basis for the measuring of the fiscal expenditure performance. In an evaluation of technical methods, Xu Z. et al. [21] provided a performance evaluation index system from the perspectives of economy, industry, resources and environment, technical equipment, and society under the background of new and old driving energy conversion. Quantitative analysis of new and old driving energy conversion performance was carried out using DEA-Malmquist, where technological innovations and steadily optimized resource allocation are regard as critical elements in performance evaluation in reference [22]. Additionally, some researchers mainly focused on the concept definition and development path of industrial transformation and upgrading. Gereffi C. pointed out that industrial transformation and upgrading would lead to efficient production, development and use of high-quality products, and skills and expertise, which played an essential role in the developing of the national economy [23]. Humphrey J. et al. mainly put forward strategies for the upgrading of industrial structure, such as the upgrading of process, function, and the product and value chain, providing a clear development direction for the industrial structure transformation and upgrading in the future [24]. Overall, the performance evaluation studies on the conversion of new and old driving energy are mainly focused on four major directions, including scientific and technological innovation, environmental protection and energy conservation, financial economy, and social impact.

2.2. Supplier Evaluation and Selection Indicators and Methodologies

There have been several studies related to the supplier evaluation index system in recent years, mainly involving fundamental evaluation indicators and evaluation indexes with social and industrial characteristics [25,26]. Goffin K. et al. investigated and proposed supplier evaluation indicators, such as total cost, on-time delivery capacity, and financial status of the company, to determine the average number of suppliers [27]. Reference [28] designed a comprehensive evaluation of suppliers from four aspects: business capacity, production capacity, quality system, and enterprise environment. The result in reference [29] pointed out that the selection of manufacturing suppliers should be considered from three levels: enterprise capability, cooperation, and service level. Among them, the enterprise capability and cooperation included four and three second-level indicators, respectively. Reference [30] evaluated and selected suppliers in the automotive industry from four dimensions: enterprise performance, business structure and production capacity, quality system, and enterprise environment. With the rapid development of society and economy, many new models such as green, innovation, and other ideas emerge; therefore, new indicators for supplier evaluation are added to satisfy modern enterprises’ management requirements. Kajal C. et al. concentrated on green supply chain management study and proposed an evaluation index system from green design, procurement, production, storage, and transportation, to adapt to the trend of society and improve the objectivity of supplier evaluation [31]. Meanwhile, Hosseini S. et al. gave a series of standards related to innovation and R & D capability to establish the supplier evaluation index system [32]. Alireza F. et al. studied the supplier management of colleges and universities, comprehensively considered the management status of school equipment, and gave a supplier selection index with service, quality, price, supply capacity, and R & D technology [33].

In general, the supplier evaluation methods include qualitative analyses, quantitative analyses, or a combination of these as only using a qualitative or quantitative approach is ineffective at solving complex problems; therefore, the combination of qualitative and quantitative analysis methods, such as AHP (analytic hierarchy process) [34], DEA (data envelopment analysis) [35], fuzzy comprehensive evaluation [36], TOPSIS [37], and so on, are widely used these days. Reference [33] proposed a hybrid model to identify the most sustainable supplier through the combination of fuzzy preference programming and fuzzy technology and presented an Iranian textile manufacturing company as a case study. To tackle the weaknesses of the AHP, Ristono A. et al. improved the AHP and applied it to a real-life case of new supplier selection in the Indonesian steel pipe industry, and the result indicates a high degree of validity [38]. To solve problems such as large index numbers, complex calculation, and coordination difficulties, an improved TOPSIS based on SVM and the trapezoidal fuzzy number-rough set method is proposed and applied to the ball vendors’ sequencing decision in bearing manufacturing, verifying the practical effect of the proposed method [39]. Given the current evaluation model of naval ship construction suppliers, reference [40] presented an eight-dimension index, and used an integrated evaluation method with DEMATEL, ANP, and TOPSIS, whose validity and feasibility were verified through an example analysis. However, TOPSIS does not consider the distance importance between candidate solutions and ideal solutions, and the gray correlation of AHP may omit the impact of evaluation indicators on evaluation results.

Additionally, some scholars applied intelligent algorithms to supplier evaluation and improved them with other evaluation methods. Using a heuristic approach based on a kernel search, Thomas K. et al. formulated the corresponding planning problem of selecting suppliers and storage facilities and determined order quantities and transport flows under the discount schemes offered by the suppliers [41]. In reference [42], an effort is made to develop an efficient system by integrating the traditional multi-criteria performance evaluation tool DEA with the DE algorithm and further with MODE to overcome the inherent drawbacks of DEA. James J. H. et al. developed a data-driven MADM (multi-attribute decision making) model to help decision makers select suitable green suppliers and provide systemic improvement strategies [43]. To solve the defect of MADM and classical neural networks, a decision-making model for outsourcing supplier evaluation based on hybrid PSO–Adam neural networks is proposed, whose results show that the model can objectively evaluate suppliers in the current complex outsourcing environment [44,45,46]. For all that, it usually takes a significant amount of time, and this time can be substantial.

2.3. Review of Existing Literature

Based on the analysis, such conclusions can be drawn. (1) Although research on evaluation index systems has well-developed in the past decades, considering not only basic indictors but also elements with industry characteristics, there has been no comprehensive research on the cement supplier evaluation index until now, due to the lack of indicators, such as environmental protection, energy conservation, and technological innovation. (2) Qualitative analyses, quantitative analyses, and combined analyses, as well as intelligent algorithms, are applied to supplier evaluation and selection. Most researchers utilized AHP and the entropy weight method to overcome the shortcoming of judging matrix inconsistency. Nevertheless, the influence of the indicator variation size on its weight has not been considered in existing research. As an improved entropy weight calculation method, CRITIC has the advantage of processing conflicting factors between indicators, which is regarded as a novel approach for supplier evaluation. However, objective weights in the standard deviation method, entropy method, and CRITIC all ignore the subjective intention of evaluators and the importance between them. (3) As a multi-attribute decision-making method based on ideal points, TOPSIS is a widely used method for concrete supplier evaluation and selection by many researchers. It first calculates the positive and negative ideal solutions of each alternative supplier, and then sorts them according to their similarity to the ideal solution. As an improved version of TOPSIS, VIKOR considers the subjective inclination of decision makers, which is especially suitable for concrete supplier evaluation by concrete production plants.

In summary, existing studies only focused on supplier fundamental evaluation indicators; therefore, they are not directly applicable for concrete production plants. Moreover, there is a lack of research and discussion on the evaluation and selection of cement suppliers under the background of new and old driving energy conversion. Therefore, this paper aims to establish a cement supplier evaluation index system using literature analysis, document analysis, and an expert scoring method of index screening under the background of old and new driving energy conversion. Then, the subjective weights of the indicators are calculated based on the fuzzy analytic hierarchy process of the triangular fuzzy number, the objective weights are calculated according to the improved CRITIC method, and the combined weights are calculated based on the ideal solution method. After that, ten cement suppliers are evaluated and selected using the proposed strategy. At the end, the VIKOR method is used to evaluate and rank the candidate cement suppliers for concrete production plants, and the results of the two evaluation approaches are compared and analyzed.

3. Construction of Cement Supplier Evaluation Index System for Concrete Production Plant under the Background of New and Old Driving Energy Conversion in China

In this study, the primary cement supplier evaluation indicators were determined through literature analysis and on-site investigation. Then, the selected index was optimized and obtained through a questionnaire survey. We first investigated and reviewed relevant literature in CNKI (https://www.cnki.net, accessed on 16 July 2022) and the Web of Science (http://www.webofknowledge.com, accessed on 16 July 2022) over the past five years. Additionally, the top 100 papers with the highest number of citations were queried and retrieved using keywords, such as supplier evaluation, cement supplier, concrete enterprise supplier, new and old driving energy conversion, and so on. Additionally, some frequently appearing evaluation indicators were selected and sorted to evaluate cement suppliers for concrete production plants. However, most of the existing relevant literature on cement supplier evaluation indicators does not involve new and old driving energy conversion. Then, we collected the latest relevant literature on the conversion of new and old driving energy published in recent years, and merged those indicators with the same meaning. For example, as technological innovation capability, innovation-driven, and technological innovation are all used to measure the innovation ability of enterprises, they were unified as one indicator, termed “scientific and technological innovation”. Additionally,

Table 1. Candidate indicators’ frequencies related to new and old driving energy conversion.

Indicators Type	Frequencies
Technological innovation	52
Environmental protection and energy conservation	38
Financial and economic	30
Social impact	24

presents the candidate indicators’ frequencies as they relate to new and old driving energy conversion.

Additionally, we invited twelve experts to explore the elements of the cement supplier evaluation. In addition, we gathered information from questionnaires provided to concrete production plants, including the manager of the technology department, the manager of the production department, the manager of the logistics department, and so on. Their opinions have specific values in the evaluation of cement suppliers. The questionnaire consisted of fifty multiple-choice questions and one open-ended question, which are the evaluation of alternative indicators. After distributing, collecting, and summarizing these via the Internet through “Questionnaire Star” (https://www.wjx.cn/, accessed on 16 July 2022), questionnaires were finally obtained. Indicator scores by experts related to new and old driving energy conversion can be seen in Appendix A. The final evaluation index for cement suppliers under the background of new and old driving energy conversion is described in

Table 2. Evaluation Index on Cement Supplier under the Background of New and Old Driving Energy Conversion.

First-Level Index	Second-Level Index	Type
B1: delivery	C11: delivery distance	Quantitative
	C12: on-time delivery level	Quantitative
	C13: ahead of time for delivery	Quantitative
	C14: protection ability for cement in bulk	Qualitative
B2: service	C21: order response speed	Qualitative
	C22: cooperation satisfaction rate	Quantitative
	C23: emergency handling capacity	Qualitative
B3: quality	C31: cement qualification rate	Quantitative
	C32: number of quality certifications	Quantitative
B4: price	C41: cement price	Quantitative
	C42: shipping charges	Quantitative
B5: comprehensive strength	C51: corporate reputation	Qualitative
	C52: financial status	Qualitative
B6: technological innovation	C61: number of innovative technology	Quantitative
	C62: proportion of innovation	Quantitative
	C63: number of special cement	Quantitative
B7: environmental protection and energy saving	C71: pollutant emission	Qualitative
	C72: proportion of environmental investment and total expenditure	Quantitative
	C73: waste recycling and utilization	Quantitative
B8: product replacement	C81: cement clinker production line	Qualitative
	C82: cement grinder	Qualitative

, where indicators related to new and old driving force conversion are categorized as B6, B7, and B8.

Next, we will explain the meaning of some indicators. “C11: delivery distance” refers to the route length between the supplier’s bulk cement delivery place and the concrete production plant. “C12: on-time delivery level” is used to describe the proportion of on-time deliveries times and the total number of deliveries within a certain period. “C14: protection ability for cement in bulk” is evaluated by experts according to the level of protection of bulk cement during transportation. “C41: cement price” can be calculated using an average price of the supplier’s price divided by the average price of bulk cement. Indicator “C52: financial status” is used to describe the assets and liabilities, profitability, and operational capacity by scoring. “C72: proportion of environmental investment and total expenditure” represents the proportion of investment in environmental protection to total expenditure. “C81: cement clinker production line” refers to cement clinker production of less than 2500 t/d and production capacity replacement. “C82: cement Grinder” shows the evaluation of a cement grinder less than 3.2 m in diameter.

4. Weight of Indicators with an Improved FAHP-CRITIC Method in Cement Supplier Evaluation

In this section, we will propose an evaluation model for cement suppliers based on the improved FAHP-CRITIC method for calculations of subject weights, objective weights, and a combination of them.

4.1. Subjective Weight Calculation

The subject weights of the evaluation index system are calculated using TFN-AHP based on triangular fuzzy numbers (TFNs) in this paper, which takes advantage of the analytic hierarchy process (AHP) and triangular fuzzy numbers to calculate the subjective weights of each evaluation indicator. As has been shown, TFN-AHP not only takes into account the advantages of the traditional AHP method and the triangular fuzzy number, but also successfully solves the problem of repeatedly testing the judgment matrix consistency in the traditional AHP method. Due to space reasons, the detailed calculation can be referred to in [47].

4.2. Objective Weight Calculation

Indicators’ objective weights in the cement supplier evaluation under the background of new and old driving energy conversion are obtained based on the actual production situation using an improved CRITIC in this study. The CRITIC (Criteria Importance Though Intercrieria Correlation) method was first proposed by Diakoulaki in 1995 [48]. Compared with other ways, such as the standard deviation method and the entropy method, CRITIC is more objective and comprehensive, with weighting results. Additionally, it calculates the objective weights based on the quantification of two fundamental notions: the contrast intensity and the conflicting character of the evaluation criteria. In this study, we replace standard deviation with the correlation coefficient, and use the absolute value of the indicators’ correlation coefficient in the calculation, because the traditional method ignores the evaluators’ subjective intention and indicators’ importance relation.

4.2.1. Raw Data Standardization

Suppose there are m candidate solutions P = {P₁, P₂, …, P_m} and n evaluation indicators L= {L₁, L₂, …, L_n}; then, x_ij describes the j^th indicator value of solution i. Additionally, the raw evaluation matrix is shown as follows.

$$X=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \cdots & x_{1n}\\ x_{21}& x_{22}& \cdots & x_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ x_{m1}& x_{m2}& \cdots & x_{mn}\end{array}\right]$$

The values of evaluation indicators for cement suppliers are described using different units and orders, and de-dimension and standardization processing for raw data are necessary; for example, “kilometer” is used as the unit of “C11: Delivery distance”, while “C21: Order response speed” takes “day” as its unit.

In order to ensure comparability between indicators, dimensionless processing is built to map these values to a set range of [0, 1]. Additionally, the equations are shown in the following with positive and negative indicators, respectively.

For positive indicator calculation, Equation (1) should be used, as follows.

$$y_{ij}=\frac{x_{ij}-x_j^{min}}{x_j^{max}-x_j^{min}}$$

(1)

For negative indicator calculation, Equation (2) should be used, as follows.

$$y_{ij}=\frac{x_j^{max}-x_{ij}}{x_j^{max}-x_j^{min}}$$

(2)

where i = 1, 2, …, m, j = 1, 2, …, n; x_ij is the j^th indicator value in solution i; y_ij is the standardization value for x_ij. $x_j^{min} \mathrm{and} x_j^{max}$ are the maximum and the minimum values for the j^th indicator in all candidate solutions, respectively. In this way, the standardization matrix can be described as follows.

$$Y=\left[\begin{array}{cccc}{y}_{11}& y_{12}& \cdots & y_{1n}\\ y_{21}& y_{22}& \cdots & y_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ y_{m1}& y_{m2}& \cdots & y_{mn}\end{array}\right]$$

4.2.2. The Contrast Intensity

In this study, we use ψ_j to represent the contrast intensity of the j^th indicator, and its value can be calculated using Equation (3).

$${\mathsf{\psi }}_j=\frac{{\sigma }_j}{{\overline{y}}_j},$$

(3)

where σ_j is the standard deviation of the j^th indicator, and σ_j = $\sqrt{\frac{1}{m}{\displaystyle \sum }_{i=1}^m{\left(y_{ij}-{\overline{y}}_j\right)}^2}$; and ${\overline{y}}_j=\frac{1}{m}{\displaystyle \sum }_{i=1}^m{y}_{ij}$.

4.2.3. The Conflicting Character Calculation

The correlation coefficient is used to describe the relationship between pairs of indicators, and can be calculated using Equation (4).

$$r_{kl}=\frac{{{\displaystyle \sum }}_{i=1}^m\left(y_{ik}-{\overline{y}}_k\right)\times \left(y_{il}-{\overline{y}}_l\right)}{\sqrt{{{\displaystyle \sum }}_{i=1}^m{\left(y_{ik}-{\overline{y}}_k\right)}^2{{\displaystyle \sum }}_{i=1}^m{\left(y_{il}-{\overline{y}}_l\right)}^2}}$$

(4)

where k = 1, 2, …, n; l = 1, 2, …, k; r_kl is the correlation coefficient between the k^th indicator and the l_th indicator; y_ik and y_il are the values of the k^th and the l^th indicators in candidate solution i, respectively; and are the average values of the k^th and the l^th indicators in standardization matrix Y. In particularly, r_kl = r_lk for k = 1, 2, …, n; l = k + 1, …, n. In this way, the correlation coefficient matrix R = (r_kl) _n×n can be obtained as follows.

$$R=\left[\begin{array}{cccc}{r}_{11}& r_{12}& \cdots & r_{1n}\\ r_{21}& r_{22}& \cdots & r_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ r_{n1}& r_{n2}& \cdots & r_{nn}\end{array}\right]$$

The conflicting between the j^th indicator and other indicators can be calculated using Equation (5).

$${\xi }_j={\displaystyle \sum }_{k=1}^n\left(1-\left|r_{kj}\right|\right)$$

(5)

where j = 1, 2, …, n; r_kj is the correlation coefficient between the k^th indicator and the j^th indicator in matrix R.

The information amount of the j^th indicator can be calculated using Equation (6).

$${\phi }_j={\psi }_j\times {\xi }_j={\displaystyle \sum }_{k=1}^n\left(1-\left|r_{kj}\right|\right)$$

(6)

where j = 1, 2, …, n.

The objective weight of the j^th indicator can be calculated using Equation (7).

$${\beta }_j=\frac{{\phi }_j}{{{\displaystyle \sum }}_{j=1}^n{\phi }_j}, j=1, 2, \dots ., n.$$

(7)

4.3. Combination Weight Calculation

It has shown that weight setting based on an ideal solution is more reliable than other combined weighting approaches, such as the linear weighting method and multiplication normalization [49]. In this paper, the combination weight ω_j is set based on the ideal solution method by combining the subjective weight α_j determined by the triangular fuzzy number analytic hierarchy process, and the objective weight β_j is determined by the improved CRITIC method. Additionally, the formula can be described in Equation (8).

$${\omega }_j=\frac{w_j^{\prime }}{{{\displaystyle \sum }}_{j=1}^n{w}_j^{\prime }}$$

(8)

where $w_j^{\prime }=\sqrt{\frac{{\alpha }_j^2+{\beta }_j^2}{2}}$, j = 1, 2, …, n.

5. Cement Supplier Evaluation with VIKOR Method under New and Old Driving Energy Conversion

The VIKOR method was first proposed by Opricovic and Tzeng [50], and was used as a multi-attribute decision-making model. In this study, we evaluate the candidate cement suppliers by calculating their group utility, individual regret values, and comprehensive evaluation values. VIKOR was developed from the aggregate function L_p-metric, and its definition is given in Equation (9).

$$L_p={\left\{{\displaystyle \sum }_{j=1}^n{\left[\frac{w_j\left(y_0^{+}-y_{ij}\right)}{y_0^{+}-y_0^{-}}\right]}^p\right\}}^{\frac{1}{p}}$$

(9)

where 0 ≤ p ≤ ∞, L_p is the distance between the candidate solution and ideal solution, and the smaller the better; y_ij is the standardized data in matrix Y; $y_0^{+}\mathrm{and} y_0^{-}$ are positive and negative ideal solutions, respectively.

5.1. Positive and Negative Ideal Solutions

First, we need to normalize the feature vector using Equation (10).

$$v_{ij}=\frac{x_{ij}}{\sqrt{{{\displaystyle \sum }}_{i=1}^m{x}_{ij}^2}}$$

(10)

Based on Equation (10), the standardization evaluation matrix V = (v_ij)_m×n can be calculated, and positive and negative ideal solutions can be obtained by Equation (11).

$$v_0^{+}=\left\{\left(\underset{1\le i\le m}{\max }{v}_{ij}|j\in J_1\right),\left(\underset{1\le i\le m}{\min }{v}_{ij}|j\in J_2\right)\right\}$$

$$v_0^{-}=\left\{\left(\underset{1\le i\le m}{\min }{v}_{ij}|j\in J_1\right),\left(\underset{1\le i\le m}{\max }{v}_{ij}|j\in J_2\right)\right\}$$

(11)

where J₁ is the set of positive indicators, and J₂ is the set of negative indicators.

Group utility and individual regret values are calculated based on the combination weights using the Equations (12) and (13), respectively.

$$S_i={\displaystyle \sum }_{j=1}^n\left[{\omega }_j\frac{\left(v_0^{+}-v_{ij}\right)}{\left(v_0^{+}-v_0^{-}\right)}\right]$$

(12)

$$R_i=\underset{j}{\mathit{max}}\left[{\omega }_j\frac{\left(v_0^{+}-v_{ij}\right)}{\left(v_0^{+}-v_0^{-}\right)}\right]$$

(13)

where i = 1, 2, …, m, ω_j is the combination weight of the j^th indicator.

Next, we will calculate values of the compromise decision indicator. According to the calculated group utility value and the individual regret value, the value of the decision-making index is calculated using the Equation (14). Additionally, the smaller the compromise value, the better the solution.

$$Q_i=\lambda \frac{S_i-S^{+}}{S^{-}-S^{+}}+\left(1-\lambda \right)\frac{R_i-R^{+}}{R^{-}-R^{+}}; (1\le \mathrm{i}\le \mathrm{m}),$$

(14)

where S ⁺ and S⁻ represent the maximum and minimum values of S, respectively; R⁺ and R⁻ are the maximum and minimum values of R_i, respectively; λ is a decision mechanism coefficient, whose value is between 0 and 1. When λ = 0.5, it means that the evaluator thinks S_i and R_i are equally considered, and when λ > 0.5, it shows that the evaluator favors the group utility value, while when λ < 0.5, it shows that evaluator favors individual regret values. In this study, we set the value of λ as 0.5.

5.2. Candidate Solution Selection

Suppose Q₁ < Q₂ < …… < Q_i < …… < Q_m, where Q_i is the identifier of supplier P_i’s compromise value. Additionally, the best solution can be obtained using the following rules.

Rule 1: if the following inequalities and equalities in Equation (15) hold for P₁ simultaneously, then P₁ is the final solution.

$$\left\{\begin{array}{c}{Q}_1<Q_2<\cdots Q_i<\cdots Q_m; \\ Q_2-Q_1\ge \frac{1}{m-1}; \\ S_1=\underset{}{\min }\left(S_i,i=1,2,\dots ,m\right) or\\ R_1=\underset{}{\min }\left(R_i,i=1,2,\dots ,m\right). \end{array}\right.$$

(15)

Rule 2: if rule 1 does not hold for P₁, then the compromise solution set can be determined by the following cases.

Case 1: if P₁ satisfies the following conditions in Equation (16), then {P₁, P₂} is the final compromise solution.

$$\left\{\begin{array}{c}{Q}_1<Q_2<\cdots Q_i<\cdots Q_m; \\ Q_2-Q_1\ge \frac{1}{m-1}; \\ S_1\ne \underset{}{\min }\left(S_i,i=1,2,\dots ,m\right) or\\ R_1\ne \underset{}{\min }\left(R_i,i=1,2,\dots ,m\right). \end{array}\right.$$

(16)

Case 2: if P₁ satisfies the following conditions in Equation (17), then {P₁, P₂, …, P_m} could be the final compromise solution, where m = $\underset{ }{\mathit{max}}\left\{i:Q_i-Q_1<\frac{1}{m-1},i=1,2,\dots ,m\right\}$.

$$\left\{\begin{array}{c}{Q}_1<Q_2<\cdots Q_i<\cdots Q_m;\\ Q_2-Q_1<\frac{1}{m-1}.\end{array}\right.$$

(17)

6. Case Study: Candidate Cement-Supplier Evaluation and Selection under the Background of New and Old Driving Energy Conversion

In this section, we will present the evaluation and selection implementation with the approach proposed in the previous sections using ten candidate cement suppliers as an example.

6.1. Weight Calculation for Candidate Cement Suppliers

First, we designed a questionnaire including eight first-level indicators and twenty-one second-level indicators scoring sheets. Then, the questionnaire was distributed to managers or senior staff working in departments, such as purchasing departments, logistics department, marketing departments, and so on. All survey takers were required to score the indicators according to their importance. The summary scoring results can be seen in Appendix B. In addition, we interviewed managers of the statistical department to obtain more information about the evaluation.

Taking the A-B fuzzy judgment matrix as an example, we present the detailed process of calculating the subjective weights of indicators under the background of new and old driving energy conversion.

The A-B fuzzy judgment matrix can be written as follows.

$$\begin{gathered} B= \\ \left[\begin{array}{cccccccc}(1,1,1)& (2,3,4)& (1/4,1/3,1/2)& (1/4,1/3,1/2)& (2,3,4)& (1/4,1/3,1/2)& (1/4,1/3,1/2)& (1/4,1/3,1/2)\\ (1/4,1/3,1/2)& (1,1,1)& (1/4,1/3,1/2)& (1/4,1/3,1/2)& (2,3,4)& (1/4,1/3,1/2)& (1/6,1/5,1/4)& (1/6,1/5,1/4)\\ (4,5,6)& (2,3,4)& (1,1,1)& (2,3,4)& (1/4,1/3,1/2)& (2,3,4)& (1/4,1/3,1/2)& (1/4,1/3,1/2)\\ (2,3,4)& (2,3,4)& (1/4,1/3,1/2)& (1,1,1)& (2,3,4)& (1,1,1)& (1/4,1/3,1/2)& (1,1,1)\\ (1/4,1/3,1/2)& (1/4,1/3,1/2)& (2,3,4)& (1/4,1/3,1/2)& (1,1,1)& (1/4,1/3,1/2)& (1/4,1/3,1/2)& (1/5,1/5,1/4)\\ (2,3,4)& (2,3,4)& (1/4,1/3,1/2)& (1,1,1)& (2,3,4)& (1,1,1)& (2,3,4)& (1,1,1)\\ (2,3,4)& (4,5,6)& (2,3,4)& (2,3,4)& (2,3,4)& (1/4,1/3,1/2)& (1,1,1)& (1/4,1/3,1/2)\\ (2,3,4)& (2,3,4)& (2,3,4)& (2,3,4)& (1,1,1)& (2,3,4)& (1,1,1)& (1,1,1)\end{array}\right] \end{gathered}$$

Additionally, the fuzzy comprehensive degrees of indicators are shown as follows.

Y_B1 = (6.25, 8.67, 11.50) × (1/133.75, 1/104.93, 1/78.00) = (0.0467, 0.0826, 0.1474);

Y_B2 = (4.33, 5.73, 7.50) × (1/133.75, 1/104.93, 1/78.00) = (0.0324, 0.0546, 0.0962);

Y_B3 = (11.75, 16.00, 20.50) × (1/133.75, 1/104.93, 1/78.00) = (0.0879, 0.1525, 0.2628);

Y_B4 = (9.5, 12.67, 16.00) × (1/133.75, 1/104.93, 1/78.00) = (0.0710,0.1207,0.2051);

Y_B5 = (4.42, 5.87, 7.75) × (1/133.75, 1/104.93, 1/78.00) = (0.0330,0.0559, 0.0994);

Y_B6 = (11.25, 15.33, 19.50) × (1/133.75, 1/104.93, 1/78.00) = (0.0841, 0.1461, 0.2500);

Y_B7 = (13.5, 18.67, 24.00) × (1/133.75, 1/104.93, 1/78.00) = (0.1009, 0.1779, 0.3077);

Y_B8 = (17.00, 22.00, 27.00) × (1/133.75, 1/104.93, 1/78.00) = (0.1271, 0.2097, 0.3462).

Normalization of w^k is necessary, and the local weight vector can be obtained using the above formulas.

w^A = (0.0792, 0.0135, 0.1721, 0.1355, 0.0183, 0.1657, 0.1946, 0.2211).

Similarly,

Table 3. Indicator weights for cement supplier evaluation.

First-Level Indicator	Second-Level Indicator	Subjective Weight	Objective Weight	Combination Weight
B1 (0.0792)	C11	0.0119	0.0645	0.0415
	C12	0.0345	0.0546	0.0409
	C13	0.0073	0.0568	0.0362
	C14	0.0255	0.0612	0.0419
B2 (0.0135)	C21	0.0064	0.0413	0.0264
	C22	0.0034	0.0509	0.0323
	C23	0.0037	0.0365	0.0232
B3 (0.1721)	C31	0.1171	0.0367	0.0777
	C32	0.0550	0.0493	0.0467
B4 (0.1355)	C41	0.0861	0.0462	0.0618
	C42	0.0494	0.0519	0.0453
B5 (0.0183)	C51	0.0067	0.0604	0.0384
	C52	0.0116	0.0607	0.0391
B6 (0.1657)	C61	0.0611	0.0468	0.0487
	C62	0.0448	0.0653	0.0501
	C63	0.0598	0.0504	0.0495
B7 (0.1946)	C71	0.0731	0.0421	0.0534
	C72	0.0458	0.0492	0.0425
	C73	0.0757	0.041	0.0545
B8 (0.2211)	C81	0.0806	0.0371	0.0561
	C82	0.1405	0.0416	0.0927

gives the subjective weights, objective weights, and combination weights of each indicator according to Section 4.1.

6.2. Candidate Cement Supplier Evaluation and Selection

There are ten candidate cement suppliers in this study, named X₁, X₂, X₃, X₄, X₅, X₆, X₇, X₈, X₉, and X₁₀, respectively. Additionally, the number of second-level indicators is twenty-one, and the original evaluation matrix is a 7 × 21 two-dimension matrix. Results after standardization can be seen in

Table 4. Values of indicators after normalization under the background of new and old driving energy conversion.

Indicator	Candidate Cement Suppliers
	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10
C11	0.3785	0.2800	0.2722	0.3701	0.3142	0.3203	0.2488	0.3569	0.2722	0.3203
C12	0.3169	0.3193	0.3243	0.3120	0.3029	0.3102	0.3192	0.3230	0.3150	0.3189
C13	0.2530	0.3509	0.1714	0.3182	0.3672	0.3264	0.3101	0.2611	0.3509	0.3917
C14	0.3138	0.3061	0.3176	0.3253	0.3329	0.3367	0.3214	0.3253	0.2679	0.3100
C21	0.3226	0.3763	0.2688	0.3226	0.3226	0.2688	0.2150	0.3763	0.2688	0.3763
C22	0.3201	0.3170	0.3187	0.3154	0.3227	0.3142	0.3172	0.3119	0.3044	0.3204
C23	0.2993	0.2954	0.3032	0.3265	0.3304	0.3304	0.3498	0.2876	0.2993	0.3343
C31	0.3227	0.3167	0.3099	0.3081	0.3217	0.3138	0.3260	0.3120	0.3167	0.3143
C32	0.3318	0.2765	0.2765	0.3318	0.3318	0.3318	0.3318	0.3318	0.2765	0.3318
C41	0.3470	0.3123	0.3053	0.3470	0.3227	0.3123	0.2949	0.3123	0.3053	0.2984
C42	0.3137	0.2775	0.3217	0.3418	0.3539	0.2775	0.3016	0.3097	0.3378	0.3177
C51	0.3135	0.3172	0.3135	0.3319	0.2951	0.3246	0.3135	0.3098	0.3246	0.3172
C52	0.3301	0.3378	0.3186	0.2956	0.3263	0.3186	0.3148	0.3186	0.2956	0.3033
C61	0.3490	0.4363	0.2036	0.2327	0.2618	0.3200	0.2909	0.3200	0.4072	0.2618
C62	0.2613	0.3286	0.1490	0.3152	0.3243	0.1822	0.4072	0.2815	0.4301	0.3658
C63	0.3162	0.2108	0.2108	0.3162	0.3162	0.5270	0.3162	0.1054	0.2108	0.4216
C71	0.3490	0.4363	0.2036	0.2327	0.2618	0.3200	0.2909	0.3200	0.4072	0.2618
C72	0.2983	0.3258	0.2829	0.2724	0.3196	0.3127	0.4076	0.3002	0.3410	0.2796
C73	0.3079	0.3153	0.3339	0.2968	0.3227	0.3190	0.3264	0.3042	0.3190	0.3153
C81	0.3138	0.3064	0.2880	0.3249	0.3249	0.3138	0.3323	0.3175	0.3138	0.3249
C82	0.3331	0.3257	0.3146	0.2850	0.3146	0.3220	0.3627	0.3257	0.2887	0.2813

.

According to the VIKOR method, the positive ideal solution is the set of maximum positive indicators and minimum negative indicators after standardization, while the negative ideal solution is the set of maximum negative indicators and minimum positive indicators. In this way, the whole indicators are categorized as positive set and negative set. Additionally, the positive indicators set include C12, C14, C22, C31, C32, C51, C52, C61, C62, C63, C71, C72, C73, C81, and C82, while the other six indicators belong to the negative indicators set. Additionally, the positive and negative ideal solutions are shown in

Table 5. Positive and negative ideal solutions of cement supplier evaluation indicators.

Second-Level Indicator	Positive Ideal Solutions v0+	Negative Ideal Solutions v0−
C11	0.249	0.379
C12	0.324	0.303
C13	0.171	0.392
C14	0.337	0.268
C21	0.215	0.376
C22	0.323	0.304
C23	0.350	0.288
C31	0.326	0.308
C32	0.332	0.277
C41	0.295	0.347
C42	0.278	0.354
C51	0.332	0.295
C52	0.338	0.296
C61	0.436	0.204
C62	0.430	0.149
C63	0.527	0.105
C71	0.436	0.204
C72	0.408	0.272
C73	0.334	0.297
C81	0.332	0.288
C82	0.363	0.281

.

In addition, group utility and individual regret values were calculated based on the combination weights using Equations (12) and (13), as can be seen in

Table 6. Candidate cement supplier’s group utility S_i and regret values R_i..

Candidate Cement Suppliers	Si	Ri
X1	0.4711	0.0619
X2	0.4311	0.0326
X3	0.5846	0.0699
X4	0.6793	0.0886
X5	0.4962	0.0548
X6	0.4226	0.0531
X7	0.2101	0.0334
X8	0.5345	0.0608
X9	0.5395	0.0844
X10	0.5184	0.0928

.

Next, we will calculate the value of the compromise decision indicator. According to the results of the group utility value and the individual regret value, the value of the decision-making index is calculated using Equation (14). Additionally, the results are shown in

Table 7. Values of compromise and ranking for candidate cement suppliers.

Candidate Cement Suppliers	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10
Qi	0.5182	0.3784	0.7064	0.9645	0.4855	0.3528	0.1845	0.4765	0.7801	0.8285
Ranking	6	3	7	10	5	2	1	4	8	9

.

According to Equations (15)–(17), rule 1 does not hold for X7 because R₇ is not the minimum value of R_i. Thus, the compromise solution is {X7, X6}, with the satisfaction of case 1 according to rule 2.

6.3. Cement Supplier Evaluation with Traditional Approach

In order to confirm the evaluation effectiveness and validity, we compare the results with those of a non-new and old driving energy conversion background. In the traditional evaluation approach, the first-level indicator system does not include those related to new and old driving energy conversion. In other words, there are only thirteen secondary indicators in total.

Additionally, the fuzzy judgment matrix with the traditional evaluation approach is described as follows.

$$\mathrm{B}=\left[\begin{array}{ccccc}(1,1,1)& (2,3,4)& (1/6,1/5,1/4)& (1/4,1/3,1/2)& (4,5,6)\\ (1/4,1/3,1/2)& (1,1,1)& (1/4,1/3,1/2)& (2,3,4)& (2,3,4)\\ (4,5,6)& (2,3,4)& (1,1,1)& (2,3,4)& (1/4,1/3,1/2)\\ (2,3,4)& (1/4,1/3,1/2)& (1/4,1/3,1/2)& (1,1,1)& (2,3,4)\\ (1/6,1/5,1/4)& (1/4,1/3,1/2)& (2,3,4)& (1/4,1/3,1/2)& (1,1,1)\end{array}\right]$$

Additionally, the subjective weight, objective weight, and combination weights are shown in

Table 8. Weights of indicators with traditional evaluation approach.

First-Level Indicator	Second-Level Indicator	Subjective Weight	Objective Weight	Combination Weight
B1 (0.2161)	C11	0.0325	0.0852	0.0631
	C12	0.0940	0.0641	0.0787
	C13	0.0200	0.0757	0.0542
	C14	0.0696	0.0661	0.0664
B2 (0.1633)	C21	0.0774	0.0479	0.0630
	C22	0.0411	0.0585	0.0495
	C23	0.0447	0.0421	0.0425
B3 (0.2872)	C31	0.1954	0.1408	0.1666
	C32	0.0919	0.0847	0.0865
B4 (0.1633)	C41	0.0820	0.1121	0.0961
	C42	0.0813	0.0822	0.0800
B5 (0.1701)	C51	0.1036	0.0697	0.0864
	C52	0.0665	0.0709	0.0672

.

Similarly, we use the VIKOR method to evaluate and rank the alternative cement suppliers of concrete production plants under the traditional background. Additionally, the results can be seen in

Table 9. Values of indicators after normalization with the traditional approach.

Indicator	Candidate Cement Suppliers
	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10
C11	0.3785	0.2800	0.2722	0.3701	0.3142	0.3203	0.2488	0.3569	0.2722	0.3203
C12	0.3169	0.3193	0.3243	0.3120	0.3029	0.3102	0.3192	0.3230	0.3150	0.3189
C13	0.2530	0.3509	0.1714	0.3182	0.3672	0.3264	0.3101	0.2611	0.3509	0.3917
C14	0.3138	0.3061	0.3176	0.3253	0.3329	0.3367	0.3214	0.3253	0.2679	0.3100
C21	0.3226	0.3763	0.2688	0.3226	0.3226	0.2688	0.2150	0.3763	0.2688	0.3763
C22	0.3201	0.3170	0.3187	0.3154	0.3227	0.3142	0.3172	0.3119	0.3044	0.3204
C23	0.2993	0.2954	0.3032	0.3265	0.3304	0.3304	0.3498	0.2876	0.2993	0.3343
C31	0.3227	0.3167	0.3099	0.3081	0.3217	0.3138	0.3260	0.3120	0.3167	0.3143
C32	0.3318	0.2765	0.2765	0.3318	0.3318	0.3318	0.3318	0.3318	0.2765	0.3318
C41	0.3470	0.3123	0.3053	0.3470	0.3227	0.3123	0.2949	0.3123	0.3053	0.2984
C42	0.3137	0.2775	0.3217	0.3418	0.3539	0.2775	0.3016	0.3097	0.3378	0.3177
C51	0.3135	0.3172	0.3135	0.3319	0.2951	0.3246	0.3135	0.3098	0.3246	0.3172
C52	0.3301	0.3378	0.3186	0.2956	0.3263	0.3186	0.3148	0.3186	0.2956	0.3033

.

According to Equation (11) in Section 5.1, positive and negative ideal solutions of cement supplier evaluation indicators are shown as follows.

v₀⁺ = {0.2493, 0.3241, 0.1712, 0.3375, 0.2156, 0.323, 0.3500, 0.3269, 0.3327, 0.2952, 0.2785, 0.3323, 0.3384};

v₀⁻ = {0.3791, 0.3033, 0.3925, 0.2689, 0.3764,0.3047,0.2885, 0.3086, 0.2777, 0.3473, 0.3549, 0.2953, 0.2961}.

Using Equations (12) and (13) and the combination weights, the group utility and individual regret values with the traditional evaluation approach are calculated, and the results are shown as follows in

Table 10. Group utility and individual regret values with the traditional evaluation approach.

Candidate Cement Suppliers	Si	Ri
X1	0.4359	0.0961
X2	0.4623	0.0867
X3	0.4691	0.1499
X4	0.6262	0.1666
X5	0.4938	0.0864
X6	0.3758	0.1140
X7	0.1875	0.0432
X8	0.5038	0.1304
X9	0.6011	0.0867
X10	0.4609	0.1087

.

According to Equation (14), the compromise values and ranking of candidate cement suppliers with the traditional evaluation approach can be obtained as follows in

Table 11. Values of compromise and ranking for candidate cement suppliers with the traditional evaluation approach.

Candidate Cement Suppliers	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10
Qi	0.4974	0.5895	0.7531	0.9546	0.4241	0.5013	0.1206	0.7136	0.7476	0.4907
Ranking	4	6	9	10	2	5	1	7	8	3

.

Using the selection rules in the previous subsection, {X7} is found to be the final solution for the concrete production plant as (Q₅ − Q₇) > 1/(10 − 1).

6.4. Results Comparison and Discussion

Section 6.2 and Section 6.3 present the cement supplier evaluation and selection with new and old driving energy conversion background and the traditional approach, respectively. Additionally, a comparison of their compromise value can be seen in Figure 1.

From Figure 1, we can see that X7 is always the most stable one according to evaluation results, and it could be the final solution, though the ranking of most candidate cement suppliers significantly changes with two evaluation approaches. Furthermore, according to Table 7 and Table 11, X6’s ranking changed most significantly, from fifth place in the traditional evaluation approach to second place with the new and old driving energy conversion background. Other cement suppliers’ ranking slightly changed, such as X2 and X3. Furthermore, X5’s performance was better than that of X6 in the traditional evaluation mode, indicating that X5 supplies high-quality products at reasonable prices. However, X6 obtained a better ranking than X5 under the background of new and old driving energy conversion. It can be concluded that there was still a big difference in the selection of suppliers between these two evaluation approaches. In addition, according to the investigation, the differences between X5 and X6 are relatively significant in terms of innovative technology ownership, pollutant emissions, waste recycling, and so on.

(1)

Innovative technology comparison and analysis

In terms of innovative technology, the numbers of these two suppliers are much different with 35 in X6 and 21 in X5, respectively. Furthermore, most of the innovative technologies of X5 appear in X6, while the extra 14 technologies in X6 are mainly related to the production process of cement, greatly improving the efficiency and reducing the cost with low pollution emissions. For example, X6 has upgraded the denitration system in the cement rotary kiln, and changed the position in which the tailings are feed into the furnace and the fourth-stage feeding pipe in recent years. At the same time, X5 has not improved any facilities in the past five years. Additionally, the new transformation technologies reduce the amount of ammonia water by 40–50% and lead to nearly CNY 1 million in savings in the annual ammonia water treatment cost for X6. In addition, grate cooler modification, Malvern particle size analyzer, raw material homogenization modification and technical transformation in the cement roller press have offered substantial savings in both plenty of human resources and material resources, enhancing the competitiveness of X6.

(2)

Pollutant emissions reduction comparison and analysis

Compared with cement supplier X5, X6 has made great efforts in dealing with pollutant emissions reduction and invested more than CNY 30 million for ultra-low emissions in the past few years. Additionally, many improvements have been made to protect the environment in X6, such as material shed rebuilding, environmental protection facility upgrading, sewerage treatment facilities transformation, ground hardening, etc. In addition, X6 has undertaken a series of environmental protection measures, satisfying the requirement of ultra-low emissions with dangerous nitrogen oxide (NOx) emissions. Additionally, pollutant emissions of X6 are far lower than the required standard of the government, being ahead of schedule.

Furthermore, X6 not only reduces the pollutant emissions, but also reuses them after treatment. In the cement industry, a cement rotary kiln produces a considerable amount of heat. This heat disposal is a complex problem, resulting in a quantity of heat wasted without a reasonable solution. X5 takes simple cooling measures, while X6 introduced a waste heat energy using a recycling system for a cement clinker production line, playing an essential role in energy saving, environment protection, and economic benefits. The system in X6 can produce low-pressure steam of about 1.8 t/h and 1.5 MW power each year, taking full advantage of waste heat with reasonable recovery and utilization.

(3)

Waste recycling comparison and analysis

Currently, a batch of cement clinker production lines less than 2500 t/d (tons per day) and cement mill equipment with a diameter of less than 3.2 m have been ordered to close by the government under the background of new and old driving energy conversion. X6 has already removed non-conforming equipment, and the output of its existing production lines is more than 2500 t/d. Remarkably, the newly built clinker production lines through capacity replacement are higher than 4000 t/d, and the diameter of the cement mill is higher than 3.8 m. Therefore, X6 does not have any risk of being affected by excessive pollution. However, X5 has three cement clinker production lines with a daily output of less than or equal to 2500 t/d and two cement mills with diameters less than or equal to 3.2 m, without any capacity replacement measures taken until recently. Once these three production lines and mills are shut down without any prearranged plan, the normal production and operation of the company may be affected, which in turn will have a particular impact on the cooperation with concrete production plants, as can be seen in

Table 12. Unqualified cement clinker production lines and cement mills in X5.

Name	Cement Clinker Production Line 1#	Cement Clinker Production Line 2#	Cement Clinker Production Line 3#	Cement Mill 1#	Cement Mill 2#
Specifications	2500 t/d; rotary kiln Φ4.0 m × 60 m	2000 t/d; rotary kiln Φ4.0 m × 56 m	1600 t/d; rotary kiln Φ4.0 m × 56 m	Diameter Φ3.2 m × 13 m	Diameter Φ3.2 m × 14 m

. In this way, X6 has a more significant advantage than X5 in terms of long-standing continuous cooperation from the concrete production plants’ view.

To summarize, for concrete production plants, the cement supplier X6 is more stable than X5 in terms of cooperation sustainability and bears fewer risks, which can further enhance its competitiveness. As a result, the evaluation of cement suppliers based on the conversion of old and new driving energy conversion is more accurate and scientific, thus meeting the needs of concrete production plants [51,52,53,54].

7. Conclusions and Future Works

Under the background of new and old driving energy conversion, it is very imperative that cement suppliers are scientifically and reasonably evaluated and selected to improve the competitiveness and sustainability of concrete production plants in China. Based on the investigation and analysis of the existing literature, this paper proposes a cement supplier evaluation and selection framework based on an improved FAHP-CRITIC method and VIKOR method. The model adopts the optimal combination weighting method to set the weighting of evaluation indicators, which overcomes the shortcoming of the previous single-weighting method, making the weighting results more scientific, reasonable, and closer to the actual results, enriching the comprehensive evaluation method for selecting suppliers under the background of new and old driving energy conversion. Additionally, we developed a cement supplier evaluation and selection index system under the background of new and old driving energy conversion, including eight first-level indicators and twenty-one second-level indicators. Additionally, the cement supplier evaluation and selection results were compared with different approaches, and the results confirm their effectiveness and applicability. Furthermore, the proposed cement supplier evaluation and selection framework, on the one hand, enriches the literature on the conversion of old and new driving forces of concrete production plants; on the other hand, it is helpful for solving similar problems in other fields, such as the evaluation and selection of drug suppliers, vegetables suppliers, and so on. Accordingly, the proposed approach provides a theoretical basis and suggestions for decision making.

Nonetheless, there are still some questions unsolved in this paper, involving too-simple financial indicators, small sample sizes, and so on. In addition, the qualitative index data processing in the VIKOR method used for supplier sorting needs to be further studied and improved using fuzzy language values and interval numbers, enhancing its efficiency and effectiveness. Additionally, this approach should include risk for supplier selection of cement plants as an additional factor of uncertainties for comprehensive evaluation in future works.