考虑订单偏好的外卖众包配送优化策略研究

doi:10.16381/j.cnki.issn1003-207x.2021.1262

Abstract

Abstract:

With the gradual development of the sharing economy model of food delivery， the current food delivery has changed from the original stage of market expansion to the connotative development stage of improving service quality. The mobile crowdsourcing distribution model in the current mainstream food delivery is focused on， and the optimization of the takeaway crowdsourcing distribution strategy is discussed considering the order destination preference. Firstly， three models are established that consider the service speed of the distribution system， the total income of the distributor and the waiting time of the customer as the optimization goals. Secondly， a calculation example is designed to verify the model. According to the order matching range radius， the ideal order distribution rate and the delivery staff's basic income and other parameters， the optimization strategy of the delivery system to minimize the waiting time of customers and maximize the income of the delivery staff is discussed. The simulation results show that the model proposed in this paper based on queuing theory can better describe the characteristics of actual food delivery crowdsourcing， and the relevant conclusions have theoretical value and practical significance in the operation and management of the food delivery industry.

Key words: food delivery, crowdsourcing delivery, matching of supply and demand, efficiency, queuing theory

CLC Number:

C931

Xingguang Chen,Xinyu Li,Luqiang Cheng. Research on Optimization Strategy of Food Delivery Crowdsourcing Delivery Considering Order Preference[J]. Chinese Journal of Management Science, 2024, 32(6): 86-97.

Figures/Tables 12

符号	含义
$θ$	众包配送员的目的地偏好程度
$G$	抢单系统
S	派单系统
$ρ$	众包配送员密度
v	众包配送员配送行驶速度
$q Z$	选择Z系统的众包配送员比例（Z = G, S）
h	众包订单的期望送餐距离
$k Z$	Z系统众包订单的期望取餐时长（Z = G, S）
$Q Z$	匹配范围内选择Z系统的众包配送员数量（Z = G, S）
$μ Z$	每平方公里Z系统的服务速度（Z = G, S）
$U Z$	Z系统众包配送员的个人总收益（Z = G, S）
a	众包配送员的配送基础收益
b	众包配送员送餐单位时间收益
c	众包配送员配送单位时间成本
$τ$	目的地位置理想的订单对众包配送员的偏好效益系数
$γ i$	每平方公里i订单的下单速度（i = 1, 2）
$δ$	目的地位置理想的订单分配给G系统的比例
$Γ Z$	每平方公里系统Z的订单下单速度（Z = G, S）
$w Z$	Z系统众包订单的期望排队匹配时长（Z = G, S）
T	外卖众包系统顾客总体等待时长
$Γ ? Z$	匹配范围内系统Z的订单下单速度（Z = G, S）
$μ ? Z$	匹配范围内Z系统的服务速度（Z = G, S）
W	众包订单的期望排队匹配时长
$t Z$	Z系统顾客总体等待时长（Z = G, S）
$Γ$	众包订单总量
K	众包订单的期望取餐时长
R	众包订单的匹配范围半径
P	G系统中目的地位置理想的订单的占比

仿真场景	仿真参数属性
仿真场景	每平方公里下单速度 $Γ$ (单/m)	每单配送基础收益a (RMB)	送餐单位时间收益b (RMB/m)	配送单位时间成本c (RMB/m)	理想订单偏好效益 $τ$ (RMB)	期望送餐距离h (km)	配送员密度 $ρ$ (人/km²)	平均行驶速度v (km/m)	目的地位置理想订单的占比（%）
S1	4	4	0.25	0.15	4	1.5	55	0.2	25
S2	4	4	0.25	0.15	4	1.5	55	0.2	50
S3	4	4	0.25	0.15	4	1.5	55	0.2	75
S4	4	4	0.25	0.15	4	1.5	55	0.3	25
S5	4	4	0.25	0.15	4	1.5	55	0.3	50
S6	4	4	0.25	0.15	4	1.5	55	0.3	75

References 19

1	崔岩.就业质量视角下的外卖骑手就业脆弱性研究［J］.山东社会科学，2021，5：93-99.
	Cui Y. Research on employment vulnerability of takeaway riders from the perspective of employment quality［J］. Shandong Social Science，2021，5：93-99.
2	何振华.我国即时物流行业研究［J］.商业经济研究，2020，10：100-102.
	He Z H. Research on my country's Instant Logistics Industry［J］. Journal of Commercial Economics，2020，10：100-102.
3	王韬，崔彬，吴秋爽，等.消费者在线外卖消费行为影响因素研究——基于江苏省的调查数据［J］.美食研究，2018，35（4）：15-21.
	Wang T， Cui B， Wu Q S，et al. Ｒesearch of consumption behaviors and influence factors on consumers’online meal ordering： Based on survey data form Jiangsu Province［J］. Journal of Researches on Dietetic Science and Culture， 2018，35（4）：15-21.
4	张永棠.一种移动众包系统在线激励机制优化算法［J］.计算机应用研究，2019，36（9）：2588-2589+2595.
	Zhang Y T. Optimization algorithm of online incentive mechanism for mobile crowdsourcing system［J］. Application Ｒesearch of Computers， 2019，36（9）：2588-2589+2595.
5	Alp M A， Niels A， Leo K，et al. Crowdsourced delivery-a dynamic pickup and delivery problem with Ad Hoc drivers［J］. Transportation Science， 2019， 53（1）： 222-247.
6	Liu Y， Guo B， Chen C，et al. FooDNet： Toward an optimized food delivery network based on spatial crowdsourcing［J］. IEEE Transactions on Mobile Computing， 2019， 18（6）： 1288-1301.
7	蒋丽，王静，梁昌勇，等.基于改进蚁群算法的众包配送路径研究［J］.计算机工程与应用， 2019， 55（8）： 244-249.
	Jiang L， Wang J， Liang C Y， et al. Research on crowdsourcing distribution path based on improved antcolony algorithm［J］. Computer Engineering and Applications， 2019， 55（8）：244-249.
8	李桃迎，吕晓宁，李峰，等.考虑动态需求的外卖配送路径优化模型及算法［J］.控制与决策， 2019， 34（2）： 406-413.
	Li T Y， Lv X N， Li F，et al. Routing optimization model and algorithm for takeout distribution with multiple fuzzy variables under dynamics demand［J］. Control and Decision， 2019， 34（2）： 406-413.
9	Chen C， Tian Z H， Yao B Z. Optimization of two-stage location-routing-inventory problem with time-windows in food distribution network［J］. Annals of Operations Research， 2019， 273（1-2）： 111-134.
10	Yildiz B， Savelsbergh M. Provably high-quality solutions for the meal delivery routing problem［J］. Transportation Science， 2019， 53（5）： 1372-1388.
11	Reyes D， Erera A L， Savelsbergh M W P. Complexity of routing problems with release dates and deadlines［J］. European Journal of Operational Research， 2018， 266（1）： 29-34.
12	王水莲，李志刚，杜莹莹.共享经济平台价值创造过程模型研究——以滴滴、爱彼迎和抖音为例［J］.管理评论， 2019， 31（7）： 45-55.
	Wang S L， Li Z G， Du Y Y. Research on value creation process model of shared economic platform： A case study of didi，airbnb and douyin［J］. Management Review， 2019， 31（7）： 45-55.
13	Koutsoupias E， Papadimitriou C. Worst-case equilibria［J］. Computer Science Review， 2009， 3（2）：65-69.
14	应焕红.分享经济发展与制度创新［J］.学习论坛，2018， 34（1）：41-45.
	Ying H H. Sharing economy development and system innovation［J］. Tribune of Study， 2018， 34（1）： 41-45.
15	马丽.共享经济模式产品供需匹配问题研究［J］.商业经济研究， 2018（14）： 187-189.
	Ma L. Research on the matching problem of product supply and demand in sharing economy model［J］. Journal of Commercial Economics， 2018（14）： 187-189.
16	Rouba I. Managing queueing systems where capacity is random and customers are impatient［J］. Production and Operations Management， 2018， 27（2）： 234-250.
17	Bai J， So K C， Tang C S， et al. Coordinating supply and demand on an on-demand service platform with impatient customers［J］.Manufacturing & Service Operations Management， 2018， 21（3）， 556-570.
18	Yang H， Qin X R， Ke J T， et al. Optimizing matching time interval and matching radius in on-demand ride-sourcing markets［J］. Transportation Research Part B Methodological， 2020， 131： 84-105.
19	Sun L Y， Teunter R H， Hua G W， et al. Taxi-hailing platforms： Inform or assign drivers？［J］. Transportation Research Part B： Methodological， 2020， 142： 197-212.

[1]	Yinyun Yu,Congdong Li,Yelin Fu,Kinkeung Lai. A Cross-efficiency Evaluation Method Considering Decision Maker Preference Structure [J]. Chinese Journal of Management Science, 2024, 32(8): 117-126.
[2]	Zhongqun Wu,Ruijin Zheng. The Generators Bidding Model Introducing Financial Transmission Rights under the USP Electricity Price Mechanism [J]. Chinese Journal of Management Science, 2024, 32(3): 48-59.
[3]	Lisheng Jiang,Huchang Liao. Risk-efficiency Mixed Consensus Model Considering Consensus Efficiency and Misjudgment Risk [J]. Chinese Journal of Management Science, 2024, 32(2): 23-30.
[4]	Shuai LU,Shou-wei LI,Jian-min HE. Multistage Efficiency Considering Network Status of Funds and its Predictive Ability [J]. Chinese Journal of Management Science, 2023, 31(9): 22-34.
[5]	KANG Wen-jin, ZHANG Kang. A Study on the Relationship between Liquidity Shock and Stock Return in Chinese A-Share Market [J]. Chinese Journal of Management Science, 2023, 31(7): 68-77.
[6]	QIAO Jian, CHEN Meng-tian, HE Meng-ying, CHEN Xin-jun. A Multi-intersection Coordinated Control with Equal Emphasis on Efficiency, Fairness, and Spillback [J]. Chinese Journal of Management Science, 2023, 31(7): 214-226.
[7]	LIU Zi-Long, LI Xiao-Han, TANG Jia-Fu. The Impact of Major Public Health Emergency and Its Prevention and Control Policy on Labor Productivity of Internet Gig Economy Platform: A Case Study of Food Deliveryman [J]. Chinese Journal of Management Science, 2023, 31(3): 81-91.
[8]	CHEN Guo-quan, WANG Jing-yi, LIN Yan-ling, LIU Wei, ZHOU Qi-wei, XU Fen. A Study on the Model of Multi-individual Learning from Experience: From the Perspective of Matching between Information and Analytic Method [J]. Chinese Journal of Management Science, 2023, 31(3): 259-267.
[9]	YANG Huan, LV Cheng-chao. Ten Years of “New Medical Reform”: The Regional Differences, Dynamic Evolution and Influencing Factors of China’s Medical and Health Service Efficiency [J]. Chinese Journal of Management Science, 2023, 31(2): 162-172.
[10]	Jian-jun PAN,Guang-ming HOU,Ji-fa GU,Hua-feng ZHANG,Jun-peng WANG,Ze-wei CHEN. Empirical Study on the Impact of Technological Progress on the Environmental Efficiency of China's Power Industry: Based on SBM Super Efficiency-ML-Tobit [J]. Chinese Journal of Management Science, 2023, 31(12): 215-227.
[11]	Yan HUANG,Li-ye CHEN,Nan WU,Ying-ming WANG,Yong-wu DAI. Study on Interval Benchmarking Based on Efficiency Compensation Mechanism——Taking Forest Carbon Sink Efficiency as An Example [J]. Chinese Journal of Management Science, 2023, 31(12): 317-328.
[12]	LI Mei-juan, , LU Jin-cheng. A New Cross-efficiency Evaluation Method Based on Double Frontier and Its Application [J]. Chinese Journal of Management Science, 2023, 31(1): 168-175.
[13]	CHEN Guo-quan, WANG Jing-yi, LIN Yan-ling, LIU Wei, ZHOU Qi-wei, XU Fen. A Study on the Model of Multi-individual Learning from Experience: From the Perspectives of Information Heterogeneity and Analytic Method Heterogeneity [J]. Chinese Journal of Management Science, 2023, 31(1): 187-199.
[14]	XIAO He-lu, WANG Shan-ping. Estimation of Portfolio Efficiency Using a Chance-constrained Stochastic Index-DEA Approach [J]. Chinese Journal of Management Science, 2022, 30(9): 94-104.
[15]	HU Dan, LIANG liang. IPO Issuer Rent-Seeking Motivation, Pricing Efficacy and MarketScreening—— From the Perspective of Pre-IPO Audit [J]. Chinese Journal of Management Science, 2022, 30(9): 116-127.

Research on Optimization Strategy of Food Delivery Crowdsourcing Delivery Considering Order Preference

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 12

References 19

Related Articles 15

Metrics

Comments

Recommended 0