考虑订单可拆分的第三方共享制造平台产能匹配策略

doi:10.16381/j.cnki.issn1003-207x.2022.2182

Abstract

Abstract:

With the development of the new generation of information technologies and shared economics， third-party shared manufacturing platforms （TPSMPs） are booming. Such platforms support the sharing of production capacity between manufacturing enterprises by integrating the dispersed and idle capacity of manufacturing enterprises in the market and matching capacity demand and supply. That is， manufacturing enterprises with overcapacity （capacity suppliers） in the TPSMP can share their surplus capacity， and manufacturing enterprises with insufficient production capacity （capacity demanders） can find suitable capacity suppliers and use their surplus capacity. In practice， considering the mismatch between capacity supply and demand in quantity， the order from one capacity demander on the TPSMP cannot usually be fully satisfied by a single capacity supplier， thus the capacity demander will allow the TPSMP to split its order to multiple capacity suppliers. Order splitting will incur additional cooperative costs， different capacity matching decisions will also affect the profitability of the TPSMP. Meanwhile， to guarantee that orders are fully satisfied， the TPSMP needs to decide not only which capacity suppliers to match with the capacity demanders， but also the matching quantities of the corresponding capacity suppliers， which complicates the TPSMP's capacity matching decisions.The main work of our paper includes the following three parts. First， focusing on the capacity matching problem of the TPSMP based on order splitting， the capacity split matching model maximizing the profit of the TPSMP is constructed considering the capacity of supply and demand quantity， price， minimum matching quantity， etc. Second， to solve the model， a two-stage heuristic method is designed combining greedy algorithm （GA） and large neighborhood search algorithm （LNS）. In the first stage， GA is applied to generate an initial feasible solution， in the second stage， LNS is adopted to improve the initial feasible solution. Finally， through numerical examples， the effectiveness of GA-LNS is verified， and some insights are obtained.The main results show that　（1） the revenue of the platform and the total revenue of the suppliers increase positively with the decrease of the minimum matching quantity acceptable for capacity suppliers；（2） the effectiveness of the two stage heuristic algorithm GA-LNS is verified by comparing with the results obtained by GUROBI and variable neighborhood search algorithm （VNS）；（3） the variation range of the relative deviation index （RDI） of the results obtained by GA-LNS is very small， and the GA-LNS algorithm has good stability. Our algorithm can obtain high quality solutions in a short time， which provides theoretical reference and decision-making basis for third-party shared manufacturing platform to solve the capacity matching problem considering order splitting.

Key words: third-party platform, shared manufacturing, capacity matching, order splitting

CLC Number:

C931

Duanyang Cao,Xumei Zhang,Bin Dan. Research on Capacity Matching Strategy of Third-party Shared Manufacturing Platform Considering Order Splitting[J]. Chinese Journal of Management Science, 2025, 33(10): 225-235.

Figures/Tables 8

算例	总收益(元)			产能利用率(%)
算例	$F 0$	$F G A - L N S$	$η$ (%)	$C U 0$	$C U G A - L N S$	$θ$
1	21142	29727	40.60	66.72	96.25	44.25
2	323214	387702	19.95	76.57	89.33	16.66
3	291740	369382	26.61	69.84	88.67	26.97
4	668252	728190	8.97	81.65	89.06	9.08
5	59034	61595	4.34	69.21	72.52	4.79
6	87703	99629	13.60	71.26	81.81	14.80
7	78768	82581	4.84	89.71	94.25	5.06
8	13608534	14096731	3.59	67.06	69.77	4.04
9	975870	1092961	12.00	86.17	96.48	11.97
10	1472017	1620734	10.10	82.50	91.17	10.51
11	99975	110175	10.20	79.48	87.25	9.77
12	140047	147385	5.24	72.06	76.09	5.59
13	5704075	6263378	9.81	86.95	95.54	9.88
14	92679	99946	7.84	88.63	95.75	8.04
15	4295845	4544538	5.79	87.37	92.12	5.43
16	1202491	1326336	10.30	80.19	89.06	11.06
17	11167970	11746442	5.18	88.61	93.29	5.29
18	45103390	46533241	3.17	84.06	86.10	2.42
19	2888583	3043971	5.38	81.29	85.11	4.70
20	24106717	25309585	4.99	87.05	91.42	5.01
平均	—	—	10.63	—	—	10.77

算例	目标函数(元)					计算时间(秒)
算例	$F ¯ *$	$F ¯ U B$	$F ¯ G A - L N S$	$η ¯ *$ (%)	$η ¯ U B$ (%)	$T ¯ G u r o b i$	$T ¯ G A - L N S$	$η ¯$ (%)
1	26887	26887	26887	0.00	0.00	5.71	0.28	-94.4
2	407731	407739	407719	0.00	0.00	14.46	0.35	-95.5
3	402822	402832	402811	0.00	-0.01	41.66	0.78	-98.0
4	583883	583891	583879	-0.00	-0.00	68.55	1.21	-95.7
5	77828	78283	77436	-0.49	-1.05	3419.01	3.93	-99.9
6	101423	101675	101194	-0.23	-0.48	3405.84	3.86	-99.9
7	80366	81058	80089	-0.35	-1.20	3600	5.29	-99.9
8	15611369	15614885	15576124	-0.23	-0.25	3600	10.85	-99.7
9	1111048	1119735	1096660	-1.29	-2.07	3600	10.90	-99.2
10	1598010	1609271	1585693	-0.78	-1.48	3600	17.93	-99.5
平均	—	—	—	-0.34	-0.65	—	—	-98.2

算例	目标函数(元)			计算时间(秒)
算例	$F ¯ G A - L N S$	$F ¯ V N S$	$ζ ¯ 1$ (%)	$T ¯ G A - L N S$	$T ¯ V N S$	$ζ ¯ 2$ (%)
11	103828	100311	3.6	20.71	220.78	-90.1
12	153779	148745	3.4	28.93	170.11	-82.9
13	6175227	5864517	5.3	34.25	144.12	-75.5
14	99830	97077	2.9	22.40	226.38	-89.7
15	4482789	4254766	5.4	39.90	648.61	-93.7
16	1326678	1208674	9.9	165.73	622.91	-70.6
17	11706187	11317925	3.5	267.04	5125.92	-94.2
18	45556541	44102298	3.3	151.69	5064.80	-97.0
19	3097965	2927046	5.9	394.49	4161.21	-88.8
20	25457873	24825917	2.6	373.99	9061.04	-95.8
平均	—	—	4.58	—	—	-87.83

算例	$q$	平台收益	产能供方总收益
1	500	29727	345311
	600	29696	344960
	700	29609	343949
	800	24586	283887
2	900	388305	4472403
	1000	387762	4466165
	1100	369542	4255484
	1200	365801	4212464
	1300	364258	4194718
	1400	364019	4191968
	1500	339487	3908701

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文献	匹配类型			数量拆分	方法
文献	一对一	一对多	多对多	数量拆分	方法
任磊和任明仑^[20]	√				考虑学习与协同效应的双向匹配方法
Li等^[21]	√				基于投影技术的双边匹配方法
He和Qian^[22]	√				基于模糊聚类的双边匹配算法
Delaram等^[23]	√				延迟接受算法
Pu和Yuan^[24]	√				TODIM方法
Tong和Zhu^[25]		√			改进布谷鸟算法
Tong和Zhu^[26]		√			RNN-MCDA方法、改进的布谷鸟算法
Guo和Dong^[27]			√		多目标粒子群算法
Lim等^[28]			√		改进NSGA-II算法
本文			√	√	贪婪算法、大邻域搜索算法

Research on Capacity Matching Strategy of Third-party Shared Manufacturing Platform Considering Order Splitting

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