Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization

IF 2.5 4区 综合性期刊 Q2 CHEMISTRY, MULTIDISCIPLINARY
Shebiao Hu, Kun Li
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引用次数: 0

Abstract

With the increasing variety of products, the increasing substitutability of products, and the trend of customized products, the volatility of market demand is increasing, which poses a challenge to make accurate demand forecasting. The Bayesian method is particularly promising and appealing when the data fluctuate greatly. This paper proposes a product-demand forecasting model based on multilayer Bayesian network, which introduces hidden layer variables and volatility factors to meet the time series connection and volatility of the demand data. However, most studies use sampling methods to estimate the parameters. We use Bayesian maximum a posteriori estimation to estimate the model parameters and introduce an improved particle swarm optimization algorithm (MPSO) to optimize the objective function. In order to increase the diversity of the particle population and accelerate the convergence, an adaptive particle velocity, position updating strategy, and nonlinear changing inertia weight are introduced in the algorithm. Finally, RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error) are used as the evaluation criterion to conduct experiments on six different datasets, and the experimental results are compared with the results of the ARIMA (autoregressive integrated moving average model) method and PSO algorithm. The experimental results show that the method has a good prediction effect. It provides a new idea for demand forecasting in the supply chain.
基于改进粒子群优化的贝叶斯网络需求预测模型
随着产品种类的增加、产品可替代性的提高以及定制化产品的趋势,市场需求的波动性越来越大,这对准确的需求预测提出了挑战。当数据波动很大时,贝叶斯方法特别有前景和吸引力。本文提出了一种基于多层贝叶斯网络的产品需求预测模型,该模型引入了隐层变量和波动因子,以满足需求数据的时间序列连接和波动性。然而,大多数研究使用抽样方法来估计参数。我们使用贝叶斯最大后验估计来估计模型参数,并引入一种改进的粒子群优化算法(MPSO)来优化目标函数。为了增加粒子群的多样性并加速收敛,在算法中引入了自适应粒子速度、位置更新策略和非线性变化惯性权重。最后,以均方根误差(RMSE)和平均绝对百分比误差(MAPE)为评价标准,在六个不同的数据集上进行了实验,并将实验结果与ARIMA(自回归综合移动平均模型)方法和PSO算法的结果进行了比较。实验结果表明,该方法具有良好的预测效果。它为供应链中的需求预测提供了一种新的思路。
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来源期刊
Applied Sciences-Basel
Applied Sciences-Basel CHEMISTRY, MULTIDISCIPLINARYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
5.30
自引率
11.10%
发文量
10882
期刊介绍: Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
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