基于参与意愿的物流联盟资源优化配置模型

doi:10.11959/j.issn.2096-3750.2021.00163

物联网学报 ›› 2021, Vol. 5 ›› Issue (1): 108-116.doi: 10.11959/j.issn.2096-3750.2021.00163

基于参与意愿的物流联盟资源优化配置模型

李梦蓉¹^,⁴, 朱华瑜²^,⁴, 亓晋⁴^,⁴, 孙雁飞⁴^,⁴

¹ 南京邮电大学通信与信息工程学院，江苏南京 210023
² 南京邮电大学自动化学院，江苏南京 210023
³ 南京邮电大学物联网学院，江苏南京 210023
⁴ 江苏省高性能计算与智能处理工程研究中心，江苏南京 210023

修回日期:2020-04-04 出版日期:2021-03-30 发布日期:2021-03-01
作者简介:李梦蓉（1994- ），女，南京邮电大学通信与信息工程学院硕士生，主要研究方向为工业互联网、智能制造和智慧供应链
朱华瑜（1995- ），男，南京邮电大学自动化学院硕士生，主要研究方向为智能计算、深度强化学习和智慧云制造
亓晋（1983- ），男，博士，南京邮电大学副教授，主要研究方向为人工智能在工业互联网、能源互联网、智慧供应链和智慧健康等领域的关键技术与应用
孙雁飞（1976- ），男，博士，南京邮电大学研究员，主要研究方向为人工智能在工业互联网、能源互联网、智慧供应链和智慧健康等领域的关键技术与应用
基金资助:
国家自然科学基金资助项目(61772286);国家自然科学基金资助项目(61802208);中国博士后科学基金资助项目(2019M651923);江苏省自然科学基金资助项目(BK20191381);江苏省重点研发计划(BE2019742)

Optimal allocation model of logistics alliance resources based on willingness to participate

Mengrong LI¹^,⁴, Huayu ZHU²^,⁴, Jin QI⁴^,⁴, Yanfei SUN⁴^,⁴

¹ College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
² College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
³ College of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
⁴ Jiangsu Engineering Research Center of HPC and Intelligent Processing, Nanjing 210023, China

Revised:2020-04-04 Online:2021-03-30 Published:2021-03-01
Supported by:
The National Natural Science Foundation of China(61772286);The National Natural Science Foundation of China(61802208);The Postdoctoral Science Foundation of China(2019M651923);The Natural Science Foundation of Jiangsu Province of China(BK20191381);The Primary Research ＆ Developement Plan of Jiangsu Province(BE2019742)

摘要/Abstract

摘要：

企业通过组建物流联盟的方式协同完成大型物流任务从而降低物流成本、提高物流效率，物流联盟协同运作的稳定程度主要与企业参与联盟合作的积极性相关。因此，考虑物流联盟成员的参与意愿对物流联盟资源配置的影响，构建了基于参与意愿的资源优化配置模型，并提出了基于后悔理论的最优解排序方法求解模型，依据参与意愿指标对该方法进行验证。结果表明，所提方法排序结果的参与意愿分布得分比传统方法提高了3.5倍，可有效提升资源配置的科学性与合理性，并有助于巩固供应链协同运作模式的稳定性。

关键词: 物流联盟, 资源配置, 物流任务分配, 最优解排序

Abstract:

In order to reduce the costs and improve the efficiency during large-scale logistics tasks, logistics alliances are established by enterprises to achieve scale effects.The stability of the cooperative operation of the logistics alliances is mainly reflected in the enthusiasm of enterprises for participating in cooperation.Therefore, a resource optimal allocation model based on the willingness to participate was proposed, considering the impact of the participation willingness of alliance members on the resource allocation of the alliance.A sorting method with the optimal solution based on the regret theory was proposed to solve the model, and the method was verified based on the participation willingness index.The results show that the participation willingness distribution score of the sorting method based on the regret theory is 3.5 times higher than that of the traditional method.It can be seen that the proposed method can effectively improve the scientificity and rationality of resource allocation, and can contribute to consolidate the stability of the collaborative operation of the supply chain.

Key words: logistics alliance, resource allocation, logistics task allocation, ranking of optimal solution

中图分类号:

TP305

李梦蓉, 朱华瑜, 亓晋, 孙雁飞. 基于参与意愿的物流联盟资源优化配置模型[J]. 物联网学报, 2021, 5(1): 108-116.

Mengrong LI, Huayu ZHU, Jin QI, Yanfei SUN. Optimal allocation model of logistics alliance resources based on willingness to participate[J]. Chinese Journal on Internet of Things, 2021, 5(1): 108-116.

图/表 9

图1

表1

基于后悔理论的排序法决策矩阵"

方案	属性O₁	属性O₂	…	属性O_n
A₁	${\bar{d}}_{11}^{}$	${\bar{d}}_{12}^{}$	…	${\bar{d}}_{1 n}^{}$
A₂	${\bar{d}}_{21}^{}$	${\bar{d}}_{22}^{}$	…	${\bar{d}}_{2 n}^{}$
$⋮$	$⋮$	$⋮$	$⋮$	$⋮$
A_m	${\bar{d}}_{m 1}^{}$	${\bar{d}}_{m 2}^{}$	…	${\bar{d}}_{m n}^{}$

表1

表2

图2

图3

表3

表4

表5

表6

参考文献 16

[1]	李志刚, 周兴社 . 物联网软件平台及其智能化发展[J]. 物联网学报, 2017,1(1): 40-49.
	LI Z G , ZHOU X S . IoT software platform and its intelligent development[J]. Chinese Journal on Internet of Things, 2017,1(1): 40-49.
[2]	王志宏, 杨震 . 人工智能技术研究及未来智能化信息服务体系的思考[J]. 电信科学, 2017,33(5): 1-11.
	WANG Z H , YANG Z . Research on artificial intelligence technology and the future intelligent information service architecture[J]. Telecommunications Science, 2017,33(5): 1-11.
[3]	WANG X Y . Study on problems and countermeasures of wisdom logistics development in China[J]. Railway Transport ＆ Economy, 2017(4): 41-45.
[4]	JI S W , ZHANGL Y , YUZ Z . Single-link task allotment in dynamic logistics alliance[J]. Logistics Technology, 2016,35(7): 53-56.
[5]	王旭, 田帅辉, 王振锋 . 面向物流任务的跨组织边界物流资源优化配置[J]. 计算机集成制造系统, 2012,18(2): 389-395.
	WANG X , TIAN S H , WANG Z F . Inter-organizational boundaries’ logistics resource optimized allocation oriented to logistics task[J]. Computer Integrated Manufacturing Systems, 2012,18(2): 389-395.
[6]	GUILLERMO L-D , CHRISTIAN C-C , DAVOR L ,et al. Mir proteins coordinate microtubule-and actin-dependent mitochondrial transport and distribution[J]. EMBO Journal, 2018,37(3): 321-336.
[7]	CHEN J , LU J , QI S L . Generating scenarios for simulation modeling of container ship arrivals[J]. Applied Mechanics and Materials, 2011: 591-595.
[8]	刘伟华, 季建华, 周乐 . 两级物流服务供应链任务分配模型[J]. 上海交通大学学报, 2008,42(9): 1524-1528.
	LIU W H , JI J H , ZHOU L . Two-level logistics service supply chain task allocation model[J]. Journal of Shanghai Jiaotong University, 2008,42(9): 1524-1528.
[9]	刘建昌, 李飞, 王洪海 ,等. 进化高维多目标优化算法研究综述[J]. 控制与决策, 2018,33(5): 114-122.
	LIU J C , LI F , WANG H H ,et al. Survey on evolutionary many-objective optimization algorithms[J]. Control and Decision, 2018,33(5): 114-122.
[10]	ZHANG J , CHO H , MAGO P J ,et al. Multi-objective particle swarm optimization (MOPSO) for a distributed energy system integrated with energy storage[J]. Journal of Thermal Science, 2019,28(6): 1221-1235.
[11]	YANG S X , LI M Q , LIU X H ,et al. A grid-based evolutionary algorithm for many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2013,17(5): 721-736.
[12]	YUAN Y , XU H , WANG B . An improved NSGA-III procedure for evolutionary many-objective optimization[C]// Proceedings of 2014 Annual Conference on Genetic and Evolutionary Computation.[S.l.:s.n.], 2014: 661-668.
[13]	BHAGAVATULAS S , SANJEEVIS G , KUMAR D ,et al. Multi-objective indicator based evolutionary algorithm for portfolio optimization[C]// Proceedings of 2014 IEEE International Advance Computing Conference (IACC). Piscataway:IEEE Press, 2014: 1206-1210.
[14]	张晓, 樊治平, 陈发动 . 基于后悔理论的风险型多属性决策方法[J]. 系统工程理论与实践, 2013,33(9): 139-146.
	ZHANG X , FAN Z P , CHEN F D . Method for risky multiple attribute decision making based on regret theory[J]. Systems Engineering—Theory ＆ Practice, 2013,33(9): 139-146.
[15]	曾华, 刘雁鸣, 熊德之 . 概率论与数理统计[M]. 北京: 科学出版社, 2014.
	ZENG H , LIU Y M , XIONG D Z . Probability and mathematical statistics[M]. Beijing: Science Press, 2014.
[16]	张笛, 孙涛, 陈洪转 ,等. 多种形式不确定偏好信息下考虑后悔行为的双边匹配方法[J]. 系统工程与电子技术, 2019,41(1): 123-128.
	ZHANG D , SUN T , CHEN H Z ,et al. Method for two-sided matching with multi-form preference information considering regret behavior[J]. Systems Engineering and Electronics, 2019,41(1): 123-128.

组号	C_max	T_max	S_min	W_min	函数值	除去W的函数值
1	3	7	0.5	200	-16.775 7	-23.901
2	2.95	6.95	0.55	199	-16.909 1	-31.013 1
3	3.1	6.95	0.45	202	-17.918 3	-19.192 6
4	3	6	0.48	200	-16.001 3	-20.001 3
5	3.2	7	0.49	201	-17.012 1	-22.918

子任务	任务名称	候选资源
LT₁	售卖机由生产工厂运至码头	车队R₁₁
		车队R₁₂
		车队R₁₃
LT₂	码头装箱（宁波）	码头R₂₁
		码头R₂₂
LT₃	水运至广州港	集装船R₃₁
		集装船R₃₂
		集装船R₃₃
LT₄	港口堆存	堆场R₄₁
		堆场R₄₂
LT₅	航空运输至海口	航空物流R₅₁
		航空物流R₅₂
		航空物流R₅₃
LT₆	功能检测	检测人员R₆₁
		检测人员R₆₂

方案	成本	时间	客户评价	参与意愿
A₁	17.5	8.5	97	0.86
A₂	18	9	99	0.85
A₃	16	10	98	0.91
A₄	18.4	8	95	0.87
A₅	20	6.5	98	0.88
A₆	19.2	9.5	96	0.79

方案	成本	时间	客户评价	参与意愿
A₁	[0.056 313, 0.822 791]	[0.035 556, 0.850 939]	[0.074 116, 0.938 066]	[0.0, 0.995 034]
A₂	[0.066 246, 0.832 481]	[0.117 430, 0.892 286]	[0.0, 0.877 970]	[0.040 166, 0.983 558]
A₃	[0.0, 0.732 127]	[0.238 733, 1.0]	[0.0431 36, 0.831 887]	[0.087 084, 1.0]
A₄	[0.078 376, 0.893 287]	[0.052 428, 0.875 190]	[0.172 890, 1.0]	[0.031 346, 0.945 084]
A₅	[0.087 347, 1.0]	[0.0, 0.694 640]	[0.092 438, 0.849 071]	[0.044 654, 0.950 375]
A₆	[0.019 653, 0.884 121]	[0.166 442, 0.852 952]	[0.174 341, 0.958 364]	[0.135 581, 0.946 663]

方案	成本	时间	客户评价	参与意愿
A₁	0.874 692	1.176 13	0.826 628	0.409 448
A₂	0.908 854	1.168 187	0.801 677	1.447 233
A₃	1.236 315	1.079 21	0.905 436	1.135 093
A₄	1.093 932	1.075 366	1.027 153	0.912 541
A₅	1.148 872	0.905 849	0.752 284	0.781 477
A₆	1.132 27	1.122 209	0.845 691	1.181 022

基于参与意愿的物流联盟资源优化配置模型

Optimal allocation model of logistics alliance resources based on willingness to participate

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