Analysing and mitigating errors in production processes is a primary objective of companies in the automotive sector. Unfortunately, due to inaccurate or partially missing information, comparing errors is often very d...
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Analysing and mitigating errors in production processes is a primary objective of companies in the automotive sector. Unfortunately, due to inaccurate or partially missing information, comparing errors is often very difficult, resulting from the experts' provision of incomplete pairwise comparison matrices. In the literature, several techniques have been developed to complete such matrices. These techniques merely estimate what the decision makers or experts would have entered according to known entries. In this article, we propose a new methodology based on the stochastic multi-objective acceptabilityanalysis;we apply it to vary the missing entries of the pairwise comparison matrix, thus providing the probability that an alternative/criterion will attain a given rank. This approach gives a complete view of the possible outcomes because it represents all possible decision maker mindsets. We present a case study carried out in a multinational automotive industry where we apply our methodology for evaluating errors in the production process.
Ordinal regression methods of Multiple Criteria Decision Aiding (MCDA) take into account one, several, or all value functions compatible with the indirect preference information provided by the Decision Maker (DM). Wh...
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Ordinal regression methods of Multiple Criteria Decision Aiding (MCDA) take into account one, several, or all value functions compatible with the indirect preference information provided by the Decision Maker (DM). When dealing with multiple criteria ranking problems, typically, this information is a series of holistic and certain judgments having the form of pairwise comparisons of some reference alternatives, indicating that alternative a is certainly either preferred to or indifferent with alternative b. In some decision situations, it might be useful, however, to additionally account for uncertain pairwise comparisons interpreted in the following way: although the preference of a over b is not certain, it is more credible than preference of b over a. To handle certain and uncertain preference information, we propose a new approach that builds a probability distribution over the space of all value functions compatible with the DM's certain holistic judgments. A didactic example shows the applicability of the proposed approach. (C) 2016 Elsevier B.V. All rights reserved.
Supplier selection is critical to the healthcare industry, especially in emergencies. Accurate and timely supply of healthcare resources can reduce economic cost and ensure the safety of people's lives. However, t...
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Supplier selection is critical to the healthcare industry, especially in emergencies. Accurate and timely supply of healthcare resources can reduce economic cost and ensure the safety of people's lives. However, the healthcare supplier market is enormous and complex;making accurate evaluations of different suppliers and helping managers choose the most suitable one when considering many factors is becoming an important task. Many multicriteria decision-making (MCDM) methods have been applied to supplier selection in existing literature. However, the research on healthcare supplier selection still needs to be completed. In this paper, to deal with healthcare supplier selection problems when there is no sufficient time and information to decide with uncertainty, the stochastic multicriteria acceptabilityanalysis (SMAA) method and multi-attributive border approximation area comparison (MABAC) method are first combined by taking both the advantages of SMAA and MABAC methods when the assessments are expressed by belief distributions (BDs). In the SMAA-MABAC method, the global ignorance in BDs and the uncertain criteria weights are considered by judging the feasible space to derive the final rank of healthcare suppliers with the most possible. The more straightforward calculation process of SMAA-MABAC makes it faster to make an accurate choice among various suppliers. Moreover, it is flexible to generate different recommended suggestions when diverse preference information is added in SMAA-MABAC. Finally, a case study of healthcare supplier selection is examined for managers based on distributed reviews, and targeted selection advice is given concerning different demands. Besides, the sensitive analysis is provided to better understand the influence of the criteria weights on the proposed model. The results of the comparative analysis also show the effectiveness and rationality of the proposed method.
As a risk modeling about fuzzy numbers, R-numbers have successfully extended to multi-criteria decision making (MCDM) methods for the real-life decision making problems involving the risk and uncertainties associated ...
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As a risk modeling about fuzzy numbers, R-numbers have successfully extended to multi-criteria decision making (MCDM) methods for the real-life decision making problems involving the risk and uncertainties associated with fuzzy numbers. To obtain more reliable and robust multi-criteria ranking alternatives in these uncertain situations, a hybrid decision making aided framework involving stochastic multiobjective acceptability analysis (SMAA), robust ordinal regression (ROR), and multi-attributive border approximation area comparison (MABAC) is proposed for MCDM problems with risk factors and preference models. Firstly, some novel operations of the R-numbers associated with triangular fuzzy numbers are proposed to explore a broader application scope. Secondly, a novel MABAC method combined with the R-numbers is proposed for MCDM problems which focus on uncertainty and error of triangular fuzzy numbers. Thirdly, a hybrid decision making aided framework which applies SMAA and ROR into the novel MABAC method is proposed for obtaining robust multi-criteria ranking alternatives through two binary relations, and two measures complement each other. Moreover, a Monte Carlo simulation of the framework is performed. Lastly, an application of assessment of wind energy potential and comparative analysis is provided to illustrate the efficiency and superiority of the proposed framework.
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