Towards enhancing the crowdsourcing door-to-door delivery: An effective model in beijing

Fuqiang Lu; Zichao Du; Ziteng Wang; Leizhen Wang; Suxin Wang

doi:10.3934/jimo.2024175

Article Contents

2025, Volume 21, Issue 3: 2371-2395. Doi: 10.3934/jimo.2024175

This issue Previous Article Analysis of supply chain environmental policy under uncertain demand Next Article A hybrid metaheuristic method to optimize the total weighted tardiness and delivery cost for an integrated production and distribution scheduling model in supply chain management

Towards enhancing the crowdsourcing door-to-door delivery: An effective model in beijing

1.
School of Economics and Management, Yanshan University, Qinhuangdao, Hebei 066004, China
2.
College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China
3.
Department of Industrial and Systems Engineering, Northern Illinois University, DeKalb, IL 60115, USA
4.
School of Management, Northeastern University at Qinhuangdao, Qinhuangdao, Hebei 066004, China

^*Corresponding author: Zichao Du
^*Corresponding author: Zichao Du

Received: April 1, 2024

Revised: October 1, 2024

Early access: November 26, 2024

Published online: November 26, 2024

Abstract / Introduction Full Text(HTML) Figure(14) / Table(3) Related Papers Cited by

Abstract

Door-to-door delivery is becoming more prevalent in practice. However, crowdsourcing couriers' uncertainties (delivery distance, monthly income, and the monthly volume of tasks) make it hard for logistics companies to maintain a stable crowdsourcing couriers' participation rate and guarantee a good level of delivery coverage rate. To solve this problem, this paper proposed the crowdsourcing delivery optimization model, which considers the bounded rationality of crowdsourcing couriers, analyses the crowdsourcing couriers' participation rate under three crowdsourcing couriers' uncertainties, and helps logistics companies to decrease the delivery cost by adjusting the delivery price per parcel and delivery distance while guaranteeing a certain level of delivery coverage rate. Further, this paper combined the whale optimization algorithm (WOA) with the model to design a heuristic algorithm for solving the crowdsourcing delivery problem. The experiments were conducted according to a data set from Beijing (China). Experiment results shows that 4.07 million USD a month is enough to provide door-to-door delivery services in 94.22% area of the city, which is feasible for giant companies.

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Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

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Figure 1. Traditional delivery and crowdsourcing delivery mode

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Figure 2. Crowdsourcing delivery area

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Figure 3. Impact of delivery distance on the participation rate

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Figure 4. Impact of monthly income on the participation rate

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Figure 5. The volume of tasks harms the participation rate

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Figure 6. The process of WOA

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Figure 7. Iterative convergence curve of algorithms

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Figure 8. Optimization results with different algorithms

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Figure 9. Curve of the cost under different targets of delivery coverage

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Figure 10. Curve of the delivery gain under different targets of delivery coverage

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Figure 11. Curve of monthly incomes under different targets of delivery coverage

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Figure 12. Curve of the number of crowdsourcing couriers under different targets of delivery coverage

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Figure 13. Curve of the monthly task volume of crowdsourcing couriers under different targets of delivery coverage

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Figure 14. Curve of the delivery distance of crowdsourcing couriers under different targets of delivery coverage

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Table 1. Optimization results of the cases

Variables or Parameters	Meaning
$ {g}_{par} $	The delivery gain per parcel
r	The delivery radius of couriers
$ {N}_{\text{par}} $	Total number of packages that need to be delivered in the area
$ {N}_{crowd} $	The number of potential crowdsourcing couriers on the platform
$ {N}_{actual} $	The actual number of crowdsourcing couriers involved in the delivery
$ N_{par}^{crowd} $	Number of parcels delivered by per crowdsourcing couriers per month
D	Population density
P	Total number of residents in the area
S	The area of urban
$ {{P}_{r}} $	Number of customers within the delivery radius
$ {{\delta }_{\text{in}come}} $	Impact factor of crowdsourcing couriers' monthly income on the participation rate
$ {{\delta }_{dis\tan ce}} $	Impact factor of crowdsourcing couriers' delivery distance on the participation rate
$ {{\delta }_{\text{task}}} $	Impact factor of crowdsourcing couriers' volume of tasks on the participation rate
$ \delta $	The participation rate of crowdsourcing couriers on the platform
K	The set of income level of the city
k	The index in set K
$ M_{ave}^{k} $	The $ k_{th} $ level average monthly income of residents in the city
$ {{L}_{\max }} $	The maximum acceptable delivery distance of crowdsourcing couriers
$ {{L}_{\text{re}fer}} $	The average delivery distance of full-time job couriers
$ {{C}_{crowd}} $	The maximum acceptable volume of tasks for crowdsourcing couriers
$ {{g}_{crowd}} $	The average monthly income of crowdsourcing courier
$ \alpha $	The crowdsourcing door-to-door delivery coverage rate
$ Q $	The total cost of crowdsourcing delivery
$ {{n}_{dis\tan ce}} $	Sensitivity coefficient of crowdsourcing couriers to the delivery distance
$ {{n}_{income}} $	Sensitivity coefficient of crowdsourcing couriers to the monthly income
$ {{m}_{i}} $	The proportion of the ith level monthly income of residents in the city
$ {{n}_{task}} $	The monthly task volume per crowdsourcing courier
$ {{\eta }_{task}} $	The monthly task volume aversion coefficient of crowdsourcing couriers
$ \Phi $	The target delivery coverage rate of logistic company
$ \partial $	The maximum delivery volume of tasks per crowdsourcing courier

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Table 2. Basic experimental parameters value

Index	Value
$ \alpha $	90
$ {{N}_{\text{par}}} $	8614.4(10000 pieces / month)
D	1041($ person / k{{m}^{2}} $)
P	2153.6(10000 persons)
S	16, 140($ k{{m}^{2}} $)
$ M_{ave}^{1} $	315.71(USD/month)
$ M_{ave}^{2} $	557.71(USD/month)
$ M_{ave}^{3} $	795.39(USD/month)
$ M_{ave}^{4} $	1094.39(USD/month)
$ M_{ave}^{5} $	1814.27(USD/month)
m	0.2
$ {{L}_{\max }} $	5km
$ {{C}_{crowd}} $	1, 000(pieces/month)
$ {{N}_{crowd}} $	1680.9(10000 persons)
$ N_{par}^{_{crowd}} $	0-10(CNY/piece)
$ {{t}_{\max }} $	100
b	2
$ {{r}_{1}} $	Random number in [0, 1]
$ {{r}_{2}} $	Random number in [0, 1]
$ l $	Random number in [-1, 1]

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Table 3. Basic results of the crowdsourcing delivery optimization model

Index	Value
The number of couriers participating in crowdsourcing delivery	49335
Delivery distance of crowdsourcing delivery personnel	313(m)
The delivery area of each crowdsourcing courier	0.31($ k{{m}^{2}} $)
The number of customers to be served by each crowdsourcing courier	321(persons)
Delivery gain per parcel	0.05USD/parcel
Monthly task volume of each crowdsourcing courier	1645(parcels)
Monthly income of crowdsourcing couriers	82.51(USD)
The monthly cost paid by the logistics company	4, 073, 160(USD)
Delivery coverage rate	94.22%

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