Research on Prediction of Reverse Returned Logistics Based on Grey-Markov Model

Luo, Yuming

doi:10.14257/ijunesst.2016.9.8.23

IJUNESST

2016

DOI: 10.14257/ijunesst.2016.9.8.23

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Research on Prediction of Reverse Returned Logistics Based on Grey-Markov Model

Yuming Luo¹

Abstract: Abstract

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Cited by 2 publications

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“…Different from other studies they used Markov chain correction to estimate the sign of the residual errors rather than forecasting residual errors. Luo (2016) compared the forecasting accuracy of GM (1,1) grey model and the GM (1,1) integrated Markov model using these models to predict the monthly reverse logistics of a company's computers and concluded that average relative error and MSE of the Grey-Markov predictions are smaller.…”

mentioning

confidence: 99%

Predicting product demand from small-sized data: grey models

Özdemir

Özdağoğlu

2017

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Purpose Prediction problems raised in uncertain environments require different solution approaches such as grey prediction models, which consider uncertainty in information and also enable the use of small data sets. The purpose of this paper is to investigate the comparative performances of grey prediction models (GM) and Markov chain integrated grey models in a demand prediction problem. Design/methodology/approach The modeling process of grey models is initially described, and then an integrated model called the Grey-Markov model is presented for the convenience of applications. The analyses are conducted on a monthly demand prediction problem to demonstrate the modeling accuracies of the GM (1,1), GM (2,1), GM (1,1)-Markov, and GM (2,1)-Markov models. Findings Numerical results reveal that the Grey-Markov model based on GM (2,1) achieves better prediction performance than the other models. Practical implications It is thought that the methodology and the findings of the study will be a significant reference for both academics and executives who struggle with similar demand prediction problems in their fields of interest. Originality/value The novelty of this study comes from the fact that the GM (2,1)-Markov model has been first used for demand prediction. Furthermore, the GM (2,1)-Markov model represents a relatively new approach, and this is the second paper that addresses the GM (2,1)-Markov model in any area.

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confidence: 99%

Predicting product demand from small-sized data: grey models

Özdemir

Özdağoğlu

2017

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Multi-time Scale Attention Network for WEEE reverse logistics return prediction

Zhang

Gao

Zhao

et al. 2023

Expert Systems with Applications

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Research on Prediction of Reverse Returned Logistics Based on Grey-Markov Model

Abstract: Abstract

Cited by 2 publications

References 13 publications

Predicting product demand from small-sized data: grey models

Predicting product demand from small-sized data: grey models

Multi-time Scale Attention Network for WEEE reverse logistics return prediction

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