Study of Fair Strategy for Merchant Self-Operated Takeaway Delivery Based on Delivery Plan Optimization
Source abstract
Abstract
With the increasing demand for takeaway delivery, more merchants are developing their takeaway delivery system to manage order fulfillment and enhance the consumer online experience. This study presents a mathematical model for merchant-operated takeaway delivery, using an improved ant colony algorithm integrated with K-means to cluster customer locations and determine optimal routes. We propose two fairness strategies—order quantity and travel distance—to ensure equitable workload distribution among riders. The K-means algorithm is enhanced by reallocating cluster assignments based on the nodes’ distances to all cluster centers. The simulation results demonstrate that the designed algorithm and strategies generate efficient optimal delivery plans for merchants.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Manufacturing and Logistics Optimization · Optimization and Packing Problems · Vehicle Routing Optimization Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Civil Engineering and Transport, Northeast Forestry University, Harbin 150040, China
College of Mechatronics Engineering, Northeast Forestry University, Harbin 150040, China
Funding
Harbin Manufacturing Technology Innovation Project · National Natural Science Foundation of China · Fundamental Research Funds for the Central Universities of China
References (34)
Show source-provided references
1. Ramanathan, J., Subramanian, N., and Abdulrahman, M.D. (2019, January 23–25). Decisive Engaging Factors in Crowd Logistics: The Case of China’s Takeaway/Home Delivery Industry. Proceedings of the 24th ISL Conference on Supply Chain Networks vs Platforms: Innovations, Challenges and Opportunities, Würzburg, Germany.
2. Optimisation of Takeaway Delivery Routes Considering the Mutual Satisfaction of Merchants and Customers10.1016/j.cie.2021.107728 ↗
3. The Evaluation of Customer Experience Using BP Neural Network-Taking Catering O2O Takeaway as an Example10.1002/cpe.5515 ↗
4. Consumer Experiences, Attitude and Behavioral Intention Toward Online Food Delivery (OFD) Services10.1016/j.jretconser.2016.12.013 ↗
5. Cloud Computing Resource Scheduling Based on Improved ANN Model Takeaway Order Volume Forecast
6. FooDNet: Toward an Optimized Food Delivery Network Based on Spatial Crowdsourcing10.1109/TMC.2018.2861864 ↗
7. Hybrid Evolutionary Optimization for Takeaway Order Selection and Delivery Path Planning Utilizing Habit Data
8. A Metaheuristic Method for Vehicle Routing Problem Based on Improved Ant Colony Optimization and Tabu Search10.3934/jimo.2012.8.469 ↗
9. Joint Distribution Center Location Problem for Restaurant Industry Based on Improved K-Means Algorithm with Penalty10.1109/ACCESS.2020.2975449 ↗
10. Optimal Delivery Routing with Wider Drone-Delivery Areas Along a Shorter Truck-Route10.1016/j.eswa.2018.03.032 ↗
11. Zou, P., Rajora, M., and Liang, S.Y. (2021). Multimodal Optimization of Permutation Flow-Shop Scheduling Problems Using a Clustering-Genetic-Algorithm-Based Approach. Appl. Sci., 11.10.3390/app11083388 ↗
12. Order Batching Problem of Unmanned Warehouse System and k-Max Clustering Algorithm
13. An Application of k-Means Clustering to Vehicle Routing Problems10.11627/jkise.2015.38.3.01 ↗
14. A Hybrid K-Means and Integer Programming Method for Commercial Territory Design: A Case Study in Meat Distribution10.1007/s10479-017-2742-6 ↗
15. Study on Assign Mode of O2O Takeaway Order Delivery Tasks
16. A Mathematical Formulation and Heuristic Approach for the Heterogeneous Fixed Fleet Vehicle Routing Problem with Simultaneous Pickup and Delivery10.3934/jimo.2020012 ↗
17. A Particle Swarm Optimization for the Vehicle Routing Problem with Simultaneous Pickup and Delivery10.1016/j.cor.2008.04.003 ↗
18. Multi-Objective Modeling of Production and Pollution Routing Problem with Time Window: A Self-Learning Particle Swarm Optimization Approach10.1016/j.cie.2015.07.003 ↗
19. A Genetic Algorithm for the Vehicle Routing Problem10.1016/S0305-0548(02)00051-5 ↗
20. Optimised Genetic Algorithm Crossover and Mutation Stage for Vehicle Routing Problem Pick-Up and Delivery with Time Windows10.1088/1757-899X/1071/1/012025 ↗
21. AlMuhaideb, S., Alhussan, T., Alamri, S., Altwaijry, Y., Aljarbou, L., and Alrayes, H. (2021). Optimization of Truck-Drone Parcel Delivery Using Metaheuristics. Appl. Sci., 11.10.3390/app11146443 ↗
22. Genetic Algorithm for Heuristic Algorithms for the Vehicle Routing Problem with Simultaneous Pick-Up and Delivery10.1016/j.cor.2005.03.014 ↗
23. A Fissile Ripple Spreading Algorithm to Solve Time-Dependent Vehicle Routing Problem via Coevolutionary Path Optimization10.1155/2020/8815983 ↗
24. Adaptive Large Neighborhood Search Algorithm for Route Planning of Freight Buses with Pickup and Delivery10.3934/jimo.2020045 ↗
25. Optimization of Rider Scheduling for a Food Delivery Service in O2O Business10.1155/2021/5515909 ↗
26. Ma, X., and Liu, C. (2024). Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem. Appl. Sci., 14.10.3390/app14125090 ↗
27. Study of Delivery Path Optimization Solution Based on Improved Ant Colony Model10.1007/s11042-021-11142-1 ↗
28. The Optimization of Path Planning for Express Delivery Based on Clone Adaptive Ant Colony Optimization10.1155/2022/4825018 ↗
29. A Hybrid ACS-VTM Algorithm for the Vehicle Routing Problem with Simultaneous Delivery & Pickup and Real-Time Traffic Condition10.1016/j.cie.2021.107747 ↗
30. Data Clustering: 50 Years Beyond K-Means10.1016/j.patrec.2009.09.011 ↗
31. The Global k-Means Clustering Algorithm10.1016/S0031-3203(02)00060-2 ↗
32. Analysis of Global k-Means, an Incremental Heuristic for Minimum Sum-of-Squares Clustering10.1007/s00357-005-0018-3 ↗
33. Ant Colony Optimization10.1109/MCI.2006.329691 ↗
34. Ant Colonies for the Travelling Salesman Problem10.1016/S0303-2647(97)01708-5 ↗