代际更替电子产品的废弃量预测与治理：小数据视角

doi:10.16381/j.cnki.issn1003-207x.2023.0017

Abstract

Abstract:

To address the issue of predicting the quantity of waste electronic products with small data， a prediction method based on the Susceptible-Infective-Removed （SIR） infectious disease model is proposed. Taking into account the small data characteristics of the time series of new generation electronic products， a Sales-Transfer-Adjustment （STa） optimization model with the minimum time-weighted average error is constructed. To estimate the parameters of the STa model， the Particle Swarm Optimization （PSO） algorithm is introduced. Furthermore， the reasonable lag time of the transfer quantity is determined via the concept of differential compensation prediction， which enhances the prediction accuracy of the new generation electronic product quantity. Based on the availability of electronic product data， the Estimation Model of Waste Quantity of Electronic Products （EWE） is used to predict the waste quantity of new generation electronic products. Through empirical analysis of 4 data sets of rural TV sets and mobile phones in China， it is found that the performance of the STa·PSO-EWE model is generally superior to that of other benchmark comparison models.

Key words: electronic waste, infectious disease model, small data, generational replacement, prediction

CLC Number:

F272

Fang Wang,Wenxin Cheng,Lean Yu, et al. Predicting and Managing E-waste with Characteristics of Generational Replacement: A Perspective of Small Data[J]. Chinese Journal of Management Science, 2025, 33(10): 350-360.

Figures/Tables 8

References 51

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时间	S_VN (t)	滞后期τ
时间	S_VN (t)	1	2	3	4	5	6	7
1985	0.8	-	-	-	-	-	-
1986	1.52	1.1336	1.1399	1.1530	1.1810	1.2421	1.3707	1.6088
1987	2.34	1.9297	1.9429	1.9709	2.0320	2.1605	2.3986	2.7700
1988	2.8	2.7025	2.7305	2.7916	2.9202	3.1582	3.5296	4.0301
1989	3.63	3.4742	3.5353	3.6638	3.9019	4.2733	4.7737	5.3954
1990	4.72	4.3236	4.4521	4.6902	5.0616	5.5620	6.1837	6.9272
1991	6.44	5.3414	5.5794	5.9508	6.4513	7.0729	7.8164	8.6923
	误差
1986	基准误差（%）	25.42	25.01	24.14	22.30	18.28	9.82	5.84
1987	预测误差（%）	17.53	16.97	15.77	13.16	7.67	2.50	18.37
1988		3.48	2.48	0.30	4.29	12.79	26.06	43.93
1989		4.29	2.61	0.93	7.49	17.72	31.51	48.63
1990		8.40	5.67	0.63	7.24	17.84	31.01	46.76
1991		17.06	13.36	7.60	0.17	9.83	21.37	34.97
测试集MAPE（%）		10.15	8.22	5.05	6.47	13.17	22.49	38.53

模型	MAPE	RMSE	SSE	TIC
GM(1,1)模型	3.1688	21.0087	2206.8198	0.7184
多项式拟合模型	0.1763	0.8627	3.7215	0.0926
HWNS模型	0.0773	0.6137	1.8829	0.0761
Norton-Bass模型	0.2273	1.0531	5.5455	0.1107
STa·PSO模型	0.1317	0.5803	1.6839	0.0643

Predicting and Managing E-waste with Characteristics of Generational Replacement: A Perspective of Small Data

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