An integrated Delphi-AHP-DEA-LPP multi criteria decision making approach for supplier selection and order quantity allocation system

2014 ◽  
Vol 18 (3) ◽  
pp. 366
Author(s):  
Rakesh D. Raut
Processes ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. 252 ◽  
Author(s):  
Chia-Nan Wang ◽  
Ying-Fang Huang ◽  
I-Fang Cheng ◽  
Van Nguyen

Suppliers are extremely important in business operations. The supplier ensures the supply of materials, raw materials, commodities, etc. in sufficient quantity, quality, stability, and accuracy to meet the requirements of production and business with low costs and on-time deliveries. Therefore, selecting and managing good suppliers is a prerequisite for organizing the production of quality products as desired, according to the schedule, and with reasonable prices and competitiveness in the market. It is also important to gain the support of suppliers in order to continue to improve and achieve more as a business. The evaluation and selection of a supplier is a Multi-Criteria Decision-Making (MCDM) issue, in which the decision-maker is faced with both qualitative and quantitative factors. In this research, the authors propose an MCDM model using a hybrid of Supply Chain Operations Reference metrics (SCOR metrics), the Analytic Hierarchy Process (AHP) model, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach for supplier evaluation and selection in the gas and oil industry. Using literature reviews on SCOR metrics, all criteria that impact supplier selection are defined in the first stage, the AHP model is applied to determine the weight of each factor in the second stage, and the optimal supplier is presented in final stage using the TOPSIS model. As a result, Decision-Making Unit 5 (DMU-05) is found to be the best supplier for the gas and oil industry in this research. The contribution of this work is to propose a new hybrid MCDM model for supplier selection in the gas and oil industry. This research also introduces a useful tool for supplier selection in other industries.


Author(s):  
YELENY ZULUETA ◽  
JUAN MARTÍNEZ-MORENO ◽  
RAFAEL BELLO PÉREZ ◽  
LUIS MARTÍNEZ

While Multi-Criteria Decision Making (MCDM) models are focused on selecting the best alternative from a finite number of feasible solutions according to a set of criteria, in Dynamic Multi-Criteria Decision Making (DMCDM) the selection process also takes into account the temporal performance of such alternatives during different time periods. In this paper a new discrete time variable index is proposed, to handling differences in temporal behavior of alternatives, which are not discriminated in preceding dynamic approaches, also considering rating-based perspectives for discrimination of the decision maker by modeling different attitudes to deal with the rating changes along different time periods. Moreover a DMCDM for supplier selection example is provided to illustrate the feasibility and effectiveness of the proposed index.


2020 ◽  
Vol 39 (3) ◽  
pp. 4285-4298
Author(s):  
Ran Tao ◽  
Fuyuan Xiao

Group multi-criteria decision-making (GMCDM) is an important part of decision theory, which is aimed to assess alternatives according to multiple criteria by collecting the wisdom of experts. However, in the process of evaluating, because of the limitation of human knowledge and the complexity of problems, an efficient GMCDM approach under uncertain environment still need to be further explored. Thus, in this paper, a novel GMCDM approach with linguistic Z-numbers based on TOPSIS and Choquet integral is proposed. Firstly, since linguistic Z-numbers performs better in coping with uncertain information, it is used to express the evaluation information. Secondly, TOPSIS, one of the most useful and systematic multi-criteria decision-making (MCDM) method, is adopted as the framework of the proposed approach. Thirdly, frequently it exists interaction between criteria, so Choquet integral is introduced to capture this kind of influence. What’s more, viewing that decision makers (DMs) show different preferences for uncertainty, the risk preference is regarded as a vital parameter when calculating the score of linguistic Z-numbers. An application in supplier selection is illustrated to demonstrate the effectiveness of the proposed approach. Finally, a further comparison and discussion of the proposed GMCDM method is given.


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