组合融资策略下零售商多品补货与库存决策

doi:10.16381/j.cnki.issn1003-207x.2024.1786

Abstract

Abstract:

With the fast development of inclusive finance in supporting the supply chain operations， most financial service agencies are building platforms by developing a variety of flexible short-term supply chain financing strategy packages or products， such as early payment discounts， deferred payments， and reverse factoring， to serve the supply chain wholesalers and retailers， which enrich the payment means for smoothing the supply chain operations， thereby optimizing the operational cost of retailers under uncertain demand and unstable supply environments. For multi-item management， retailer needs to make a comprehensive decision on the order quantity and frequency regarding factors such as the holding level of capital， the replenishment price， and the timing of repayment. A variety of payment modes with different payment schedules to enhance the flexibility and support capabilities of financial services. However， it is a challenge for small and medium retailers to comprehensively analyze the returns and risks of different short-term financial products under various payment timelines.Under such background， a stochastic joint replenishment model （SJRP） is established considering three repayment modes， i.e.， early payment， delayed payment， and reverse factoring to minimize the total system cost by deciding the best replenishment cycle time， frequencies， safety inventory levels as well as the timing for repayment. By sharing the experience of Cuckoo Search in mimicking the brood parasitism behavior of cuckoos and the Lévy flight search characteristics， a hybrid Cuckoo Search-Adaptive Differential Evolution （CSADE） algorithm is developed to solve the proposed SJRP.Numerical experiments have been designed to confirm the effectiveness of CSADE for solving the SJRP model compared with Particle Swarm Optimization （PSO）， Genetic Algorithm （GA）， Cuckoo Search （CS）， and Adaptive Differential Evolution （ADE）. Furthermore， the numerical sensitivity analyses supplemented to reveal the in-depth management insights. Through numerical experiments， several key insights have been reported： First， price discounts and the timing adjustments of reverse factoring significantly influence retailers’ repayment decisions. Second， joint replenishment and inventory management offer substantial financial returns， but these returns tend to be unstable. Lastly， retailers may achieve additional capital gains through reverse factoring， provided that their credit risk remains low.

Key words: joint replenishment, early payment, delayed payment, reverse factoring, intelligent algorithm

CLC Number:

F406

Ligang Cui,Haili Ren,Jie Deng, et al. Multi-item Joint Replenishment Model Considering Financial Repayment Portfolio[J]. Chinese Journal of Management Science, 2026, 34(11): 313-325.

Figures/Tables 24

参数	定义	参数	定义
$n$	商品种类	$α i$	缺货商品 $i$ 延期交货剩余比例
$i$	商品品类编号， $i = 1 . . . n$	$ω i$	商品 $i$ 的价格折扣系数
$μ i$	商品 $i$ 的需求率均值	$x i$	间隔期（ $k i T + L i$ ）内的需求
$σ i$	商品 $i$ 的需求率标准差	$R i$	商品 $i$ 最高库存水平
$A$	主要订购成本	$b$	违约风险系数（正常数）
$a i$	次要订购成本	$π i$	单位商品 $i$ 的边际收益
$p i$	单位商品 $i$ 销售价格	$θ$	反向保理融资比例
$c i 1$	单位商品 $i$ 采购价格	$ρ i$	单位商品 $i$ 的缺货成本
$h i$	单位商品 $i$ 仓储成本	$I p$	单位商品 $i$ 的资金占用成本
$L i$	商品 $i$ 提前期	$I e$	商品 $i$ 的资金收益率
决策变量
$k i$	商品 $i$ 采购周期乘子	$z i$	商品 $i$ 安全库存因子
$T$	基础采购周期	$N i$	商品 $i$ 付款时间
$γ i$	$γ i = 0$ 不进行反向保理融资，反之 $γ i = 1$
函数
$x i +$	取0和 $x i$ 的最大值，即 $x i + = m a x 0, x i$	$E (⋅)$	数学期望
$f (⋅)$	标准正态分布概率密度函数	$F (⋅)$	标准正态分布累积分布函数
$C (⋅)$	零售商成本函数

参数	商品1	商品2	商品3	商品4	商品5	商品6
$L i$ (年)	0.02	0.02	0.02	0.02	0.02	0.02
$A$ (元/单)	200	200	200	200	200	200
$a i$ (元/单)	45	46	47	44	45	47
$μ i$ (元/件)	10000	5000	3000	1000	600	200
$σ i$ (元/件)	2500	1000	700	500	300	150
$h i$ (元/件/年)	1	1	1	1	1	1
$π i$ (元/件)	25	30	15	30	35	30
$ρ i$ (元/件)	25	20	15	30	35	28
$p i$ (元/件)	35	50	45	65	75	70
$c i$ (元/件)	10	20	30	35	40	40
$α i$	0.28	0.65	0.76	0.59	0.26	0.44

参数	数值	参数	数值
$I e$	0.1	$τ$	0.5
$I p$	0.15	$β$	0.99
$b$	0.1	$δ$	0.95
$θ$	1	$M$	90/365

算法	$T C B$	$k i$	$z i$	$T$
PSO	384243	1,1,1,2,2,2	2.4446,2.8186,2.3903,2.2096,2.0169,2.6258	0.0540
GA	384019	1,1,1,1,1,2	2.6890,2.7058,2.5399,2.6509,2.6640,2.3207	0.0611
ADE	384019	1,1,1,1,1,2	2.6870,2.7088,2.5343,2.6467,2.6615,2.3201	0.0611
CS	386014	1,1,1,1,1,2	2.7324,2.6154,2.5526,2.0954, 2.8868,2.8779	0.0439
ADECS	385020	1,1,1,2,1,2	2.5705,2.8452,2.4982,2.3759,2.2423,2.3787	0.0745
CSADE	384015	1,1,1,1,1,2	2.6796,2.7023,2.5194,2.6430,2.6413,2.3077	0.0629

算法	$T C D$	$k i$	$γ i$	$z i$	$N i$	$T$
PSO	335588	2,2,2,2,1,2	1,1,1,0,1,0	2.8257,2.5908,2.8294,2.3909,2.8421,2.0862	0.2430,0.2443,0.2277,0.1917,0.2275,0.1703	0.1127
GA	322964	2,2,2,2,2,2	1,1,1,1,1,1	2.5481,2.7186,2.6546,2.8095,2.8513,2.7744	0.2459,0.2462,0.2458,0.2461,0.2433,0.2463	0.1132
ADE	322956	2,2,2,2,2,2	1,1,1,1,1,1	2.5556,2.7946,2.7868,2.8558,2.9331,2.8653	0.2465,0.2465,0.2465,0.2462,0.2465,0.2464	0.1133
CS	346641	2,1,1,2,3,2	0,1,1,1,0,0	2.2234,2.0525,2.4922,2.0984,1.8364,2.4387	0.2344,0.1203,0.2261,0.1818,0.2403,0.0232	0.1209
ADECS	325468	2,2,2,2,2,2	1,1,1,1,1,1	2.5073,2.8532,2.3804,2.6594,2.7974,2.7089	0.2452,0.2442,0.2389,0.2396,0.2396,0.2122	0.1128
CSADE	322892	2,2,2,2,2,2	1,1,1,1,1,1	2.4507,2.8047,2.7667,2.8271,2.7273,2.9398	0.2462,0.2464, 0.2459,0.2457,0.2457,0.2435	0.1133

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模型	算法	AVE.TC	Std.Err	AVE.T	BEST. Freq
SJRP-B	PSO	384629	759	0.15	2
	GA	384020	6.13	0.45	18
	ADE	384174	352	0.26	21
	CS	386300	732	0.13	2
	ADECS	385267	671	0.16	1
	CSADE	384016	0.91	0.29	41
SJRP-D	PSO	336352	2692	0.31	1
	GA	324379	3222	0.68	4
	ADE	323410	483	0.62	5
	CS	347550	2897	0.34	1
	ADECS	325529	396	0.39	1
	CSADE	323534	152	0.65	8

Multi-item Joint Replenishment Model Considering Financial Repayment Portfolio

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