Genetic Algorithm-Based Fair Order Assignment Optimization of Food Delivery Platform

Min Yan Tsai; Guo Yu Lin; Jiang Yi Zeng; Chia Mu Yu; Chi Yuan Chen; Hsin Hung Cho

doi:10.1007/978-3-031-43135-7_12

Genetic Algorithm-Based Fair Order Assignment Optimization of Food Delivery Platform

Min Yan Tsai, Guo Yu Lin, Jiang Yi Zeng, Chia Mu Yu, Chi Yuan Chen, Hsin Hung Cho^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Most existing food delivery platforms lack responsibility when it comes to route planning. This often results in uneven assignment of orders or difficulty in arranging orders for delivery drivers. These issues have led to loss of consumer rights and reduced revenue for delivery platforms, as well as negative feedback and evaluations. To address this problem, it is necessary to first resolve the issue of uneven distribution of orders. In this paper, we propose using the Genetic Algorithm (GA) to solve the order assignment optimization problem. By utilizing GA’s strong global search ability, we can achieve fair assignment of orders, optimize delivery routes, and balance revenue distribution. This approach creates a fair competition environment for delivery drivers and improves service quality, ultimately leading to positive feedback from consumers and creating a win-win situation.

Original language	English
Title of host publication	Bio-inspired Information and Communications Technologies - 14th EAI International Conference, BICT 2023, Proceedings
Editors	Yifan Chen, Dezhong Yao, Tadashi Nakano
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	122-132
Number of pages	11
ISBN (Print)	9783031431340
DOIs	https://doi.org/10.1007/978-3-031-43135-7_12
State	Published - 2023
Event	14th EAI International Conference on Bio-inspired Information and Communications Technologies, BICT 2023 - Okinawa, Japan Duration: 11 Apr 2023 → 12 Apr 2023

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	512 LNICST
ISSN (Print)	1867-8211
ISSN (Electronic)	1867-822X

Conference

Conference	14th EAI International Conference on Bio-inspired Information and Communications Technologies, BICT 2023
Country/Territory	Japan
City	Okinawa
Period	11/04/23 → 12/04/23

Keywords

Artificial intelligence
delivery route planning
genetic algorithm
traveling salesperson

Access to Document

10.1007/978-3-031-43135-7_12

Cite this

Tsai, M. Y., Lin, G. Y., Zeng, J. Y., Yu, C. M., Chen, C. Y., & Cho, H. H. (2023). Genetic Algorithm-Based Fair Order Assignment Optimization of Food Delivery Platform. In Y. Chen, D. Yao, & T. Nakano (Eds.), Bio-inspired Information and Communications Technologies - 14th EAI International Conference, BICT 2023, Proceedings (pp. 122-132). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 512 LNICST). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43135-7_12

Tsai, Min Yan ; Lin, Guo Yu ; Zeng, Jiang Yi et al. / Genetic Algorithm-Based Fair Order Assignment Optimization of Food Delivery Platform. Bio-inspired Information and Communications Technologies - 14th EAI International Conference, BICT 2023, Proceedings. editor / Yifan Chen ; Dezhong Yao ; Tadashi Nakano. Springer Science and Business Media Deutschland GmbH, 2023. pp. 122-132 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{318788d785394884a00bbffbd9de2d14,

title = "Genetic Algorithm-Based Fair Order Assignment Optimization of Food Delivery Platform",

abstract = "Most existing food delivery platforms lack responsibility when it comes to route planning. This often results in uneven assignment of orders or difficulty in arranging orders for delivery drivers. These issues have led to loss of consumer rights and reduced revenue for delivery platforms, as well as negative feedback and evaluations. To address this problem, it is necessary to first resolve the issue of uneven distribution of orders. In this paper, we propose using the Genetic Algorithm (GA) to solve the order assignment optimization problem. By utilizing GA{\textquoteright}s strong global search ability, we can achieve fair assignment of orders, optimize delivery routes, and balance revenue distribution. This approach creates a fair competition environment for delivery drivers and improves service quality, ultimately leading to positive feedback from consumers and creating a win-win situation.",

keywords = "Artificial intelligence, delivery route planning, genetic algorithm, traveling salesperson",

author = "Tsai, {Min Yan} and Lin, {Guo Yu} and Zeng, {Jiang Yi} and Yu, {Chia Mu} and Chen, {Chi Yuan} and Cho, {Hsin Hung}",

note = "Publisher Copyright: {\textcopyright} 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 14th EAI International Conference on Bio-inspired Information and Communications Technologies, BICT 2023 ; Conference date: 11-04-2023 Through 12-04-2023",

year = "2023",

doi = "10.1007/978-3-031-43135-7_12",

language = "English",

isbn = "9783031431340",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Science and Business Media Deutschland GmbH",

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}

Tsai, MY, Lin, GY, Zeng, JY, Yu, CM, Chen, CY & Cho, HH 2023, Genetic Algorithm-Based Fair Order Assignment Optimization of Food Delivery Platform. in Y Chen, D Yao & T Nakano (eds), Bio-inspired Information and Communications Technologies - 14th EAI International Conference, BICT 2023, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 512 LNICST, Springer Science and Business Media Deutschland GmbH, pp. 122-132, 14th EAI International Conference on Bio-inspired Information and Communications Technologies, BICT 2023, Okinawa, Japan, 11/04/23. https://doi.org/10.1007/978-3-031-43135-7_12

Genetic Algorithm-Based Fair Order Assignment Optimization of Food Delivery Platform. / Tsai, Min Yan; Lin, Guo Yu; Zeng, Jiang Yi et al.
Bio-inspired Information and Communications Technologies - 14th EAI International Conference, BICT 2023, Proceedings. ed. / Yifan Chen; Dezhong Yao; Tadashi Nakano. Springer Science and Business Media Deutschland GmbH, 2023. p. 122-132 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 512 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Genetic Algorithm-Based Fair Order Assignment Optimization of Food Delivery Platform

AU - Tsai, Min Yan

AU - Lin, Guo Yu

AU - Zeng, Jiang Yi

AU - Yu, Chia Mu

AU - Chen, Chi Yuan

AU - Cho, Hsin Hung

PY - 2023

Y1 - 2023

N2 - Most existing food delivery platforms lack responsibility when it comes to route planning. This often results in uneven assignment of orders or difficulty in arranging orders for delivery drivers. These issues have led to loss of consumer rights and reduced revenue for delivery platforms, as well as negative feedback and evaluations. To address this problem, it is necessary to first resolve the issue of uneven distribution of orders. In this paper, we propose using the Genetic Algorithm (GA) to solve the order assignment optimization problem. By utilizing GA’s strong global search ability, we can achieve fair assignment of orders, optimize delivery routes, and balance revenue distribution. This approach creates a fair competition environment for delivery drivers and improves service quality, ultimately leading to positive feedback from consumers and creating a win-win situation.

AB - Most existing food delivery platforms lack responsibility when it comes to route planning. This often results in uneven assignment of orders or difficulty in arranging orders for delivery drivers. These issues have led to loss of consumer rights and reduced revenue for delivery platforms, as well as negative feedback and evaluations. To address this problem, it is necessary to first resolve the issue of uneven distribution of orders. In this paper, we propose using the Genetic Algorithm (GA) to solve the order assignment optimization problem. By utilizing GA’s strong global search ability, we can achieve fair assignment of orders, optimize delivery routes, and balance revenue distribution. This approach creates a fair competition environment for delivery drivers and improves service quality, ultimately leading to positive feedback from consumers and creating a win-win situation.

KW - Artificial intelligence

KW - delivery route planning

KW - genetic algorithm

KW - traveling salesperson

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DO - 10.1007/978-3-031-43135-7_12

M3 - Conference contribution

AN - SCOPUS:85174551809

SN - 9783031431340

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 122

EP - 132

BT - Bio-inspired Information and Communications Technologies - 14th EAI International Conference, BICT 2023, Proceedings

A2 - Chen, Yifan

A2 - Yao, Dezhong

A2 - Nakano, Tadashi

PB - Springer Science and Business Media Deutschland GmbH

T2 - 14th EAI International Conference on Bio-inspired Information and Communications Technologies, BICT 2023

Y2 - 11 April 2023 through 12 April 2023

ER -

Tsai MY, Lin GY, Zeng JY, Yu CM, Chen CY, Cho HH. Genetic Algorithm-Based Fair Order Assignment Optimization of Food Delivery Platform. In Chen Y, Yao D, Nakano T, editors, Bio-inspired Information and Communications Technologies - 14th EAI International Conference, BICT 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 122-132. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-031-43135-7_12

Genetic Algorithm-Based Fair Order Assignment Optimization of Food Delivery Platform

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