中南林业科技大学学报

基于AMBOP-SRC模型的木质家具供应链市场需求预测

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屈俊林 , 庞燕 , 王忠伟

中南林业科技大学学报 | 林业工程 2026,46(7): 203-215

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中南林业科技大学学报 |林业工程 2026 , 46 (7) : 203 -215

基于AMBOP-SRC模型的木质家具供应链市场需求预测

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屈俊林, 庞燕, 王忠伟

作者信息

中南林业科技大学，湖南　长沙　410004

通讯作者:

王忠伟（wangpmp@163.com），博士，教授。

作者简介:

屈俊林（465980745@qq.com），博士研究生。

Research on market demand forecasting for wood furniture supply chain based on the AMBOP-SRC model

Junlin Qu, Yan Pang, Zhongwei Wang

Affiliations

Central South University of Forestry & Technology, Changsha 410004, Hunan, China

出版时间: 2026-07-25 doi: 10.14067/j.cnki.1673-923x.2026.07.019

文章导航

摘要

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【目的】

木质家具供应链市场需求受季节波动、突发事件冲击和多因素耦合影响明显，导致传统单一模型预测精度与稳定性不足。为此，本研究提出一种基于自适应游走矩优化与季节性、残差协同机制的木质家具供应链市场需求预测模型（AMBOP-SRC），以提高预测精度、鲁棒性和泛化能力。

【方法】

首先，对木质家具销售数据进行清洗、归一化及周粒度聚合，构建需求时间序列；其次，采用SARIMA模型刻画需求序列中的线性趋势与季节性规律，并提取初始预测残差；然后，进一步利用PSO优化SVR模型，对残差序列中的非线性波动进行拟合；在此基础上，引入自适应游走矩优化算法（AMBOP），对组合模型参数与协同预测过程进行动态优化，提高高维参数空间中的搜索效率与预测稳定性；最后，通过消融试验、模型对比试验和泛化试验，对所提模型进行系统验证，并采用RMSE、MAE等指标评价模型性能。

【结果】

消融试验表明，AMBOP-SRC预测模型中任何组件的缺失都会导致预测性能的明显下降；AMBOP不仅能对模型的参数进行优化，而且能够有效平衡季节性波动和突发事件响应之间的关系，实现算法与家具行业特性的深度适配。性能对比试验表明，AMBOP-SRC预测模型的综合性能明显优于相应领域的经典预测方法，有效提高了木质家具供应链需求预测的精度和可解释性。泛化试验表明，AMBOP-SRC预测模型在不同数据集上展现出良好适应能力，可应用于木质家具供应链各关键环节的需求预测。

【结论】

AMBOP-SRC预测模型高度适用于木质家具供应链需求预测，具备较高的预测精度、良好的泛化性能，预测结果能为家具企业在库存周转率优化、生产计划动态调整等方面提供重要的决策参考，从而增强供应链的韧性。

关键词

木质家具 / 供应链 / 需求预测 / AMBOP-SRC

Abstract

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【Objective】

Market demand in the wood furniture supply chain is strongly affected by seasonal fluctuations, unexpected disturbances, and multi-factor coupling, which often limits the accuracy and stability of traditional single forecasting models. To address this issue, an AMBOP-SRC model is proposed for market demand forecasting in the wood furniture supply chain, with the aim of improving forecasting accuracy, robustness, and generalization capability.

【Method】

First, the wood furniture sales data were cleaned, normalized, and aggregated at the weekly level to construct a demand time series. Then, a SARIMA model was employed to capture the linear trend and seasonal patterns of the demand series, and the initial residuals were extracted. Next, an SVR model optimized by particle swarm optimization (PSO-SVR) was used to fit the nonlinear fluctuations contained in the residual series. On this basis, an adaptive movement block optimization procedure (AMBOP) algorithm was introduced to dynamically optimize the parameters and collaborative forecasting process of the hybrid model, thereby improving search efficiency and prediction stability in a high-dimensional parameter space. Finally, ablation experiments, comparative experiments, and generalization experiments were conducted, and model performance was evaluated using indicators such as RMSE and MAE.

【Result】

Ablation experiments demonstrate that the absence of any component in the AMBOP-SRC forecasting model leads to a significant decline in predictive performance. The AMBOP method not only optimizes model parameters but also effectively balances the relationship between seasonal fluctuation modeling and emergency response, achieving deep adaptation to the characteristics of the furniture industry. Performance comparison experiments show that the AMBOP-SRC forecasting model significantly outperforms classical forecasting methods in the corresponding field, effectively improving the accuracy and interpretability of wooden furniture supply chain demand forecasting. Generalization experiments indicate that the AMBOP-SRC forecasting model exhibits strong adaptability across different datasets, making it applicable to demand forecasting in key stages of the wooden furniture supply chain.

【Conclusion】

The AMBOP-SRC forecasting model is highly suitable for wooden furniture supply chain demand forecasting, featuring high prediction accuracy and excellent generalization performance. Its results provide crucial decision-making references for furniture enterprises in optimizing inventory turnover rates and dynamically adjusting production plans, thereby enhancing supply chain resilience.

Key words

wood furniture / supply chain / demand forecasting / AMBOP-SRC

引用本文

屈俊林, 庞燕, 王忠伟. 基于AMBOP-SRC模型的木质家具供应链市场需求预测. 中南林业科技大学学报, 2026 , 46 (7) : 203 -215 . DOI: 10.14067/j.cnki.1673-923x.2026.07.019

Junlin Qu, Yan Pang, Zhongwei Wang. Research on market demand forecasting for wood furniture supply chain based on the AMBOP-SRC model[J]. Journal of Central South University of Forestry & Technology, 2026 , 46 (7) : 203 -215 . DOI: 10.14067/j.cnki.1673-923x.2026.07.019

基金

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湖南省重点研发计划项目(2022GK2025)
智慧物流技术湖南省重点实验室项目(2019TP1015)
中南林业科技大学社科非同级财政纵向科研项目(90202-69229013)

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2026年第46卷第7期

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文章信息

doi: 10.14067/j.cnki.1673-923x.2026.07.019

接收时间：2025-08-13
首发时间：2026-10-30
出版时间：2026-07-25

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收稿日期：2025-08-13
修回日期：2025-10-21

基金

湖南省重点研发计划项目(2022GK2025)

智慧物流技术湖南省重点实验室项目(2019TP1015)

中南林业科技大学社科非同级财政纵向科研项目(90202-69229013)

作者信息

中南林业科技大学，湖南　长沙　410004

通讯作者:

王忠伟（wangpmp@163.com），博士，教授。

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https://castjournals.cast.org.cn/joweb/znlykj/CN/10.14067/j.cnki.1673-923x.2026.07.019

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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