Supplier selection in nuclear power industry with extended VIKOR method under linguistic information
Graphical abstract
Introduction
The nuclear power industry belongs to a quality-sensitive one, as nuclear power plants have an extremely high request for the security and reliability compared with the conventional ones. Once nuclear incidents appear, the consequences will be significantly catastrophic. And China put its nuclear power projects on hold to assess safety concerns due to Japan's Fukushima disaster. Chinese government approved the resumption of nuclear energy until the first quarter of 2015, facing triple pressure of environmental, energy and economic. Given the booming construction of nuclear plants and stricter security requirements in recent five years, supplier selection in nuclear power industry in China has become increasingly urgent and necessary. However, very few scholars, such as Yang, Huang and Lei [1], kept supplier selection in nuclear power industry in their study. Currently, the research on supplier selection mainly focus on manufacturing industry [2], automotive industry [3], household appliance industry [4], electronic industry [5], airline retail industry [6] and so forth.
Selecting the best supplier from a large number of alternatives involves many complex and time-consuming tasks [7]. Actually, many tasks may be meaningless and unnecessary due to invalid alternatives. To optimize decision process and improve the efficiency of management, we propose a two-stage methodology for the supplier selection in nuclear power industry. These two subsequent stages are defined as phase I (Qualification) and phase II (Ranking), respectively. Phase I aims to select the qualified suppliers, which firstly ensures the final selected supplier can meet requirements, and secondly reduces the complexity of decision-making by avoiding useless tasks. For phase II, we propose an extended VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method under linguistic information.
When selecting the optimal supplier, quite a few criteria, including qualitative and quantitative ones, should be taken into consideration, such as quality, cost and technological capacity and the like. Thus, supplier selection is a multi-criteria decision-making (MCDM) problem. And the VIKOR method, based on an aggregating function representing “closeness to the ideal” using linear normalization, has been widely used for supplier selection [8], [9], [10], [11]. It considers group utility maximization and individual regret minimization and fully reflects the decision makers’ subjective preferences, which makes it superior to some traditional MCDM methods [12], [13]. In decision situations, some problems present qualitative aspects that are complex to assess by means of precise [14], [15], and thus, the use of a linguistic approach is necessary [16], [17]. For supplier selection in nuclear power industry, some performance values cannot be assessed precisely in a quantitative form but may be in a qualitative one, such as service, credit, environmental consciousness. By the use of a linguistic approach, the experts can easier express their perception preferences about the alternatives [18]. As the natural language is the standard representation of those concepts that humans use for communication [19], the application of the qualitative concept makes communication among experts comprehensive and cognitive, and also can largely facilitate experts’ work [20]. However, natural languages usually involve ambiguity and uncertainty, so it is difficult to form an exact definition of linguistic information. Among the uncertainties involved in natural language, randomness and fuzziness are the two most important aspects [21]. Fortunately, the proposed extended VIKOR can express human fuzziness and randomness with cloud variables, which can effectively improve decision quality.
This paper aims to design an applicable method for supplier selection in nuclear power industry. The main contributions of this article are: Firstly, there is no investigation of supplier selection in nuclear power industry using extended VIKOR with cloud variables. Therefore, this is the first work attempting to use this technique to select the best supplier, which can enrich research idea and provide reference for practice. Secondly, the proposed method can promote the accuracy and quality of decision-making. The VIKOR method is applied to aggregate the whole criteria, which can fully consider the relative importance of the criteria, and a balance between total and individual satisfaction. And the cloud model is introduced to describe the information uncertainty, which can give consideration to both fuzziness and randomness to reduce information loss. Both efforts contribute to a better decision-making. Thirdly, the decision process and management efficiency can be improved. By designing the two-phase method, the unqualified suppliers will be excluded in an early stage, which avoids the unqualified ones to participate in the selection from beginning to end. The tasks related to the unqualified suppliers can be substantially decreased. And the final selected supplier must conform to the fundamental requirements.
The remainder of the paper is structured as follows. Section 2 reviews supplier selection methods and analyzes the detailed sub-criteria of criteria considered for the selection of suppliers in the nuclear industry. A two-phase extended VIKOR method under linguistic information is proposed in Section 3. In Section 4, a case study from China is evaluated. And results and discussion are shown in Section 5. Subsequently, final conclusion is provided in Section 6.
Section snippets
Supplier selection methods
Currently, the research on supplier selection methods in nuclear industry is very few. Yang, Huang and Lei [1] proposed an integrated framework based on analytic hierarchy process (AHP) and technique for order preference by similarity to an ideal solution (TOPSIS) for selecting the suitable supplier. Besides, some MCDM methods used for supplier selection can be listed as follows: analytic network process (ANP) [22], [23], [24], preference ranking organization method for enrichment evaluations
Proposed supplier selection methodology
For the purpose of supplier selection, a two-phase methodology is proposed: Phase I: qualification and Phase II: rank the alternatives. Phase I screens the alternatives in accordance with quality, while Phase II proposes the application of an innovative methodology based on the cloud model and VIKOR to rank the alternatives. The individual phase is explained in the greater detail below, shown in Fig. 1.
A case study
A nuclear power plant ‘H’ locating in China was expected to further improve the production ability of complete sets of equipment, and encourage the formation of a complete self-independence nuclear power industry system. H’s plan aimed for six nuclear power units with mega kilowatt during two phases. The first phase of the project including four nuclear power units was completed in 2015. And the second phase approved in 2015 decided to use ACPR1000. ACPR1000 refers to the self-developed third
Results and discussion
The utility threshold value is . According to the aforementioned judgment standards and rules, it is found that A1 is weakly superior to A2, A2 significantly superior to A4, and A4 weakly superior to A3. That is to say, the final order relation is:, with A1 the best supplier.
Conclusions
In this study, the paper proposes the extended VIKOR based on cloud model for supplier selection in nuclear power industry, which is the first work attempted to use this technique for supplier selection. The extended VIKOR can rank and select the best one from a set of alternatives. The cloud variable is used to define criteria, sub criteria and their uncertain weights, which can handle well imprecise information. The extended method is based on the aggregating cloud merit representing the
Acknowledgements
Project supported by the Fundamental Research Funds for the Central Universities (No.2015XS27), the National Nature Science Foundation of China (No.71271085).
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