Forecasting of Disassembly Waste Generation under Uncertainties Using Digital Twinning-Based Hidden Markov Model

Yinsheng Yang, Gang Yuan, Jiaxiang Cai and Silin Wei
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Yinsheng Yang: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Gang Yuan: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China
Jiaxiang Cai: Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore 119077, Singapore
Silin Wei: College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China

Sustainability, 2021, vol. 13, issue 10, 1-15

Abstract: Disassembly waste generation forecasting is the foundation for determining disassembly waste treatment and process formulation and is also an important prerequisite for optimizing waste management. The prediction of disassembly waste generation is a complex process which is affected by potential time, environment, and economy characteristic variables. Uncertainty features, such as disassembly amount, disassembly component status, and workshop scheduling, play an important role in predicting the fluctuation of disassembly waste generation. We therefore focus on revealing the trend of waste generation in disassembly remanufacturing that faces significant influences of technology and economic changes to achieve circular industry sustainable development. To dynamically predict the generation of disassembly waste under uncertainty, this work proposes a statistical method driven by a probabilistic model, which integrates the digital twinning, Gaussian mixture, and the hidden Markov model (DG-HMM). First, digital twinning technology is used for real-time data interaction between simulation prediction and decision evaluation. Then, the Gaussian mixture and HMM are used to dynamically predict the generation of disassembly waste. In order to effectively predict the amount of disassembly waste generation, real data collected from a disassembly enterprise are used to train and verify the model. Finally, the proposed model is compared with other general prediction models to illustrate the correctness and feasibility of the proposed model. The comparison results show that DG-HMM has better prediction accuracy for the actual disassembly waste generation.

Keywords: disassembly; DG-HMM; waste forecasting; digital twinning; optimization; real-time interaction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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