A new belief Markov chain model and its application in inventory prediction
This work addresses prediction challenges in complex systems with uncertainty, such as inventory management, but it appears incremental as it combines existing theories (Markov chains and Dempster-Shafer) for a specific application.
The authors tackled the limitations of classical Discrete-time Markov chains (DTMC) in handling uncertain information and non-discrete state spaces by proposing a new belief Markov chain model that integrates Dempster-Shafer evidence theory, allowing uncertain data to be processed as interval numbers and generating basic probability assignments based on distance. The model demonstrated effectiveness in inventory prediction, showing improved performance compared to classical DTMC, though specific numerical results were not provided.
Markov chain model is widely applied in many fields, especially the field of prediction. The classical Discrete-time Markov chain(DTMC) is a widely used method for prediction. However, the classical DTMC model has some limitation when the system is complex with uncertain information or state space is not discrete. To address it, a new belief Markov chain model is proposed by combining Dempster-Shafer evidence theory with Markov chain. In our model, the uncertain data is allowed to be handle in the form of interval number and the basic probability assignment(BPA) is generated based on the distance between interval numbers. The new belief Markov chain model overcomes the shortcomings of classical Markov chain and has an efficient ability in dealing with uncertain information. Moreover, an example of inventory prediction and the comparison between our model and classical DTMC model can show the effectiveness and rationality of our proposed model.