混合契约下应急物资政企三级联合储备模型研究

doi:10.16381/j.cnki.issn1003-207x.2023.2130

摘要/Abstract

摘要：

新冠疫情等重大突发事件给政府的应急物资保障工作带来了严峻挑战。为确保应急物资能够及时供应，满足人民群众和社会各界的需求，政府与制造商签订了应急物资储备协议。然而，现有研究多集中于政府与制造商之间的合作，鲜有涉及原材料供应商在应急物资保障体系中的角色。基于此，本文利用数量柔性契约和期权契约，构建了包含政府、制造商、供应商的三级联合储备模型。其中，政府与制造商建立应急物资柔性储备契约，制造商与供应商建立原材料储备期权契约，分析了制造商将用于加急生产的原材料交由供应商代储的情境下，政府、制造商和供应商的储备决策。并在此基础上，与政府-制造商联合储备模型进行对比和敏感性分析。研究结果显示，在特定条件下，政企三级联合储备模型不仅能够提高整体应急物资储备量，减轻制造商的储备负担，还有助于提高制造商和供应商的利润，降低政府的储备成本，显著增强了应急物资保障能力。本研究为政府、制造商和供应商之间开展有效的应急物资联合储备合作提供了理论指导。

关键词: 应急物资, 生产能力储备, 期权契约, 数量柔性契约, 原材料储备

Abstract:

In recent years， various emergencies and natural disasters have occurred with alarming frequency， endangering the lives and property of the general public. In China， the government assumes the role of primary responder to disasters， bearing the significant responsibility of expeditiously dispatching supplies to affected regions. This requires the government to reserve sufficient emergency supplies before a disaster occurs in order to be prepared for potential crises. However， the government’s stockpiling capacity is limited， making it difficult to independently meet the surge in demand for emergency supplies triggered by a disaster. Additionally， in the event that no emergency occurs during the stockpiling period， the government may be compelled to dispose of its stockpile at salvage value， which would undoubtedly increase the government’s financial burden.To mitigate the risks and costs associated with sole government stockpiling， existing studies have proposed a joint stockpiling model between the government and manufacturers. This model leverages the strengths of social enterprises in material management while reducing the risks associated with government stockpiling. Concurrently， in order to reduce the risks and costs associated with stockpiling， manufacturers can also adopt a combination of physical stockpiling and capacity stockpiling， whereby some emergency supplies are stockpiled as reserved capacity and produced only when needed. This approach reduces the manufacturers’ stockpiling risk and cost without significantly delaying the delivery of emergency supplies post-disaster. However， the COVID-19 pandemic highlighted challenges such as the sharp increase in the price of meltblown nonwoven fabric， a key raw material for masks. During the crisis， the price surged from 18，000 yuan to 29，000 yuan per ton， an increase of 61.11%， which undeniably raised the conversion costs for mask manufacturers. To address this issue， an innovative three-tier joint reserve model is proposed that includes suppliers’ raw material reserves. This model is designed to mitigate the problem of raw material shortages and price increases post-disaster.A two-tier joint stockpiling model between the government and manufacturers is constructed based on a quantity flexibility contract as a baseline. Then， the supplier’s raw material stockpiling is integrated into the model， which is the main research model of this paper. The optimal stockpiling quantities and the respective profit and cost for each decision-maker are derived using the inverse solving method. The results of the study indicate that （1） the probability of a disaster is the most critical factor in determining whether the government initiates emergency stockpiling；（2） for materials with prices likely to fluctuate significantly post-disaster， the government should increase stockpiling before the disaster；（3） from an economic standpoint， there is an optimization interval that can improve the profit of manufacturers and suppliers while simultaneously reducing the cost of the government in the three-tier emergency stockpile supply chain；（4） the three-tier stockpiling strategy may not always be the optimal choice， so the government should flexibly adjust the strategy according to the option price between manufacturers and suppliers. Should the option price be higher， the government should adopt the new three-tier stockpiling strategy； otherwise， it should maintain the original two-tier stockpiling strategy. Theoretical guidance for the establishment of a three-tier joint stockpiling cooperative relationship between the government and enterprises is provided in this study.

Key words: emergency supplies, production capacity reserve, quantity flexibility contract, option contract, raw material reserve

中图分类号:

F252.8

郭柯廷,巩玲君. 混合契约下应急物资政企三级联合储备模型研究[J]. 中国管理科学, 2026, 34(6): 77-90.

Keting Guo,Lingjun Gong. Research on the Model of Government and Enterprise Three-level Joint Reserve of Emergency Supplies under Hybrid Contract[J]. Chinese Journal of Management Science, 2026, 34(6): 77-90.

图/表 9

图1

图2

图3

图4

图5

图6

表1

灾害事件发生概率的敏感性分析"

$ρ$	混合契约下应急物资政企三级联合储备模型						基准模型
$ρ$	$Q g *$	$q m *$	$q s *$	$E Π s *$	$E Π m *$	$E C g *$	$q a *$	$Q a *$	$E Π a s *$	$E Π a m *$	$E C a g *$
0.53	98	10411	5587	112513	720463	1610154	13182	98	66400	682088	1649799
0.63	3257	8759	4700	110527	923564	1858193	11089	3257	71733	891281	1891545
0.73	5551	7559	4056	109085	1071021	2040468	9570	5551	75605	1043160	2069251
0.83	7292	6648	3567	107990	1182946	2180749	8417	7292	78544	1158442	2206064
0.93	8658	5933	3184	107131	1270802	2292583	7512	8658	80851	1248932	2315176

表1

表2

柔性采购价格的敏感性分析"

$e$	混合契约下应急物资政企三级联合储备模型						基准模型
$e$	$Q g *$	$q m *$	$q s *$	$E Π s *$	$E Π m *$	$E C g *$	$q a *$	$Q a *$	$E Π a s *$	$E Π a m *$	$E C a g *$
253	45	11971	4700	110527	690657	1618862	13217	45	66310	678588	1699004
263	828	11188	4700	110527	747649	1677419	12698	828	67631	730408	1744576
273	1551	10465	4700	110527	800211	1731428	12219	1551	68853	778317	1787476
…	…	…	…	…	…	…	…	…	…	…	…
380	6858	5158	4700	110527	1180020	2121850	8705	6858	77812	1129715	2124656
390	7202	4814	4700	110527	1204124	2146642	8477	7202	78393	1152497	2147885
400	7528	4488	4700	110527	1226911	2170083	8260	7528	78944	1174117	2170083

表2

表3

期权执行价格的敏感性分析"

$c 2$	混合契约下应急物资政企三级联合储备模型						基准模型
$c 2$	$Q g *$	$q m *$	$q s *$	$E Π s *$	$E Π m *$	$E C g *$	$q a *$	$Q a *$	$E Π a s *$	$E Π a m *$	$E C a g *$
75	3257	2250	7690	92734	970895	1914093	11089	3257	71733	891281	1891545
85	3257	3671	7120	98507	972299	1897010	11089	3257	71733	891281	1891545
…	…	…	…	…	…	…	…	…	…	…	…
89	3257	4164	6910	100272	970988	1891822	11089	3257	71733	891281	1891545
90	3257	4282	6859	100674	970549	1890638	11089	3257	71733	891281	1891545
…	…	…	…	…	…	…	…	…	…	…	…
198	3257	10541	3734	111380	891815	1850795	11089	3257	71733	891281	1891545
199	3257	10570	3718	111379	891256	1850696	11089	3257	71733	891281	1891545
…	…	…	…	…	…	…	…	…	…	…	…
255	3257	11841	2997	110862	865138	1846921	11089	3257	71733	891281	1891545
265	3257	12015	2897	110718	861370	1846490	11089	3257	71733	891281	1891545

表3

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