A new robust optimization model under uncertainty for new and remanufactured products

Eslamipoor, Reza; Fakhrzad, Mohammad Bagher; Mehrjerdi, Yahia Zare

doi:10.1080/17509653.2014.930721

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…Over the years, there have been numerous researchers presented many papers in this area considering the different environments such as fuzzy, type-2 fuzzy, etc. ( [3], [4]). Baidya et al [5] use the gradient reduced method and genetic algorithm to solve the traditional TP.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks

et al. 2021

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Transportation is one of the critical functions in any business, and its cost depends on many constraints, including driver behavior, weather, distance, and demand in the market. This study proposes a novel approach for multi-criteria decision-making problems using the analytical hierarchy process (AHP) with the trapezoidal neutrosophic fuzzy numbers to produce the best criteria for evaluating total transportation cost. The proposed trapezoidal neutrosophic fuzzy analytical hierarchy process (TNF-AHP) determines the most significant criteria to be considered for further investigation in ANN training. In this study on the transportation problem (TP), the demands at different destination points and the distances between source and demand cities were determined. An artificial neural network (ANN) model has been proposed for the collected data of the TP to investigate the prediction of total transportation cost. The proposed ANN model predicts the total transportation cost with two input which were chosen by the TNF-AHP. Collected data are trained from 2 to 25 neurons with a logsig activation function, and the ideal model for ANN has been observed by Levenberg-Marquardt's feed-forward back-propagation (trainlm) learning algorithm with a single hidden layer (6-9-1) topology. It is found that the ANN model can predict the total transportation cost with high efficiency as the R values indicate a high degree of correlation. The recommended ANN model, mean absolute percentage error, Pearson product-moment correlation coefficient (R), and mean square error have been obtained adequately. The ANN model validation has been conducted, and its results are compared with the collected data.

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Section: Introductionmentioning

confidence: 99%

Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks

et al. 2021

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“…Robust optimization optimizes at worst so that a robust approach to optimization problems has been used since the early 1970s. It has recently been studied widely [3]. Alem and Morabito [4] cited two reasons for using robust optimization.…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model for a Blood Supply Chain Network with the Robust Fuzzy Possibilistic Programming Approach: A Case Study at Namazi Hospital

Tadarok

Fakhrzad

Jokardarabi

et al. 2021

IJE

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“…Several factors such as unquantifiable information, incomplete information, unobtainable information and partial ignorance cause the imprecision in decision making. Since conventional MADM methods cannot effectively handle problems with such imprecise information, therefore fuzzy multiple attribute decision-making methods have been developed owing to the imprecision in assessing the relative importance of attributes and the performance ratings of alternatives with respect to attributes [2]. Decision-Making Trial and Evaluation Laboratory (DEMATEL) is a widely used method to analyze and visualize the structure of complex systems through matrices and digraphs.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy DEMATEL approach for evaluating the risk factors

2021

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Many risk factors are dependent on each other and taking this into account can help manage appropriate decisions. The poor evaluation of these factors will impose high costs in many real applications, especially for a supply chain. There are different methods for risk evaluating and their effects for ranking them. For instant, in the fuzzy DEMATEL method, the experts’ linguistic opinions and preferences on the agent effects are used as the input. The important point is how these opinions are aggregated to produce the less computational error. In this regard, this paper proposes a new method based on statistical inferences for a fuzzy DEMATEL approach for evaluating the factors of a supply chain. This method was applied to a case study and the results showed that the proposed method was better than other methods for integrating the experts’ opinions in a supply chain. However, this method was proposed for the evaluation of supply chain factors, it can be applied to other systems as well.

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A new robust optimization model under uncertainty for new and remanufactured products

Cited by 11 publications

References 25 publications

Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks

Prediction of Transportation Costs Using Trapezoidal Neutrosophic Fuzzy Analytic Hierarchy Process and Artificial Neural Networks

A Mathematical Model for a Blood Supply Chain Network with the Robust Fuzzy Possibilistic Programming Approach: A Case Study at Namazi Hospital

A fuzzy DEMATEL approach for evaluating the risk factors

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