Two-stage optimization of instant distribution of fresh products based on improved NSGA-III algorithm

Abstract: As an important part of the fresh produce business format, fresh food instant delivery encounters numerous challenges. Issues like high losses, complex cold chains and time sensitivity lead to increased costs. Additionally, the living space of end-delivery personnel is under pressure and the talent market is saturated. The platform algorithms focus on the interests of themselves and customers while relatively overlooking those of delivery personnel, which affects the overall operation quality, resulting in a significant reduction in delivery efficiency and a remarkable decline in service quality, and further leading to the loss of user stickiness. Therefore, optimizing the fresh food delivery route and considering the interests of multiple parties to improve efficiency and service quality is a crucial research issue in the field of fresh food instant delivery. This paper designs a three-objective static model for fresh food instant delivery aiming at minimizing the total cost, maximizing customer satisfaction and maximizing riders satisfaction. Considering the dynamic changes of orders during the actual operation process and in combination with the dynamics of newly added orders, a multi-objective dynamic model with the goals of minimizing the total cost, minimizing the average customer dissatisfaction and maximizing the income fairness of riders is further established. Based on the constructed models and by incorporating the SPBO strategy, the NSGA-III algorithm is improved and designed to make it more adaptable to the multi-objective optimization requirements in the fresh food instant delivery scenario. This study selects five operational points within a specific region of a fresh food self-operated platform and the order data from a particular day as research cases to obtain the relevant parameters required for the model and conduct case analysis. Based on the platform's business priorities and development needs, appropriate Pareto solutions are selected. Additionally, the feasibility and effectiveness of the improved algorithm are verified through algorithmic comparison. The research aims to provide valuable references and insightful implications for the management decisions of relevant fresh food self-operated platforms, as well as to continuously optimize the management and service of the instant delivery process.

How to cite this paper

 Wang, Y & Sheng, Y. (2025). Two-stage optimization of instant distribution of fresh products based on improved NSGA-III algorithm.International Journal of Industrial Engineering Computations , 16(3), 535-556.

Refrences

Bi, H., Meng, J., & Li, C. (2021). Application and promotion path of new retail mode of fresh e-commerce. Commercial Economics Research, 22, 101-104. Cui, S., Sun, Q., & Zhang, Q. (2022). A time-dependent vehicle routing problem for instant delivery based on memetic algorithm. Computational Intelligence and Neuroscience, 2022(1), 5099008. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197. Deb, M., Banerjee, R., Majumder, A., & Sastry, G. R. K. (2014). Multi objective optimization of performance parameters of a single cylinder diesel engine with hydrogen as a dual fuel using pareto-based genetic algorithm. International journal of hydrogen energy, 39(15), 8063-8077. Fan, C., Zhang, Q., & Chen, Y. (2022). Fresh supply chain under the new retail channel integration and coordination of pricing strategies. Journal of Management Science in China, 30(02), 118-126. Goodarzi, A. H., Tavakkoli-Moghaddam, R., & Amini, A. (2020). A new bi-objective vehicle routing-scheduling problem with cross-docking: Mathematical model and algorithms. Computers & Industrial Engineering, 149(3). Gouraji, R. Ebrahimi, Soleimani, H., & Najafi, B. Afshar. (2025). Optimization of sustainable vehicle routing problem taking into account social utility and employing a strategy with multiple objectives. International Journal of Engineering, 38(7), 1631-1658. Hou, Y., Guo, X., Han, H., & Wang, J. (2023). Knowledge-driven ant colony optimization algorithm for vehicle routing problem in instant delivery peak period. Applied Soft Computing, 145, 110551. Huang, M., Liu, M., & Kuang, H. (2024). Vehicle routing problem for fresh products distribution considering customer satisfaction through adaptive large neighborhood search. Computers & Industrial Engineering (Apr.), 190. Jiang, X., Dan, B., Wu, S., & Xu, D. (2023). How to break through the "Cost-time-efficiency-Experience" dilemma of logistics in fresh food e-commerce: Based on the case study of Hema Fresh Food. Journal of Management Engineering, 02, 222-239. Li, Y. (2024). "Algorithm Taking the middle" of take-out platform: Institutional logic and specification construction. Shanghai Economic Studies, 8, 97-114. Liang, X., Chen, H., Wang, N. , & Zhang, M. (2024). Bi-objective vehicle routing for perishable products delivery with consideration of customers’ priorities and customized delivery time windows. IEEE Transactions on Intelligent Transportation Systems, 25. Ling, S., Yang, J., Sun, P., & Jia, N. (2024). Multi-objective collaborative instant delivery route optimization. Journal of Transportation Engineering and Information, 1-24. Menares, F., Montero, E., Paredes-Belmar, G., & Bronfman, A. (2023). A bi-objective time-dependent vehicle routing problem with delivery failure probabilities. Computers & Industrial Engineering, 185. Ming, L., Guo, H. Z., & Wei, W. (2021). Study of the game model of e-commerce information sharing in an agricultural product supply chain based on fuzzy big data and LSGDM. Technological Forecasting & Social Change, 172, 1-10. Rao, W., Sun, Y., Liu, P., et al. (2025). Research on cooperative distribution mode of fresh products considering product loss. System Engineering Theory and Practice, 1-23. Srinivas, N., & Deb, K. (1994). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221-248. Sun, X., & Li, X. (2024). A drone-driven delivery network design for an on-demand O2O platform considering hazard risks and customer heterogeneity. Asia-Pacific Journal of Operational Research, 41(04). Wang, C., Chen, C., & Fang, L. (2023). A real-time delivery terminal path guidance strategy based on spatiotemporal trajectory mining. Journal of Geomatics, 48(1), 20-23. Wang, Y., Zhang, J., Liu, Y., & Xu, M. (2022). Multi-center vehicle routing problem of fresh goods based on resource sharing and temperature control. Chinese Management Science, 30(11), 272-285. Xia, Y., Deng, Y., Pang, Y., Wang, Z., & Gao, L. (2021). Fresh food vehicle routing problem with split deliveries and customer classification. Computer Integrated Manufacturing Systems, 27(4), 1238-1248. Xu, H., Yu, Y., & Zhang, Y. (2024). The birth of the rider: Digital platform, directional matching and job creation. China Industrial Economy, 4, 114-132. Yang, Q., Xiong, L., Li, Y., Chen, Q., Yu, Y., & Wang, J. (2022). Contract coordination of fresh agri-product supply chain under O2O model. Sustainability, 14(14), 8771. Yu, H., Wang, S., & Li, H. (2022). Optimization of instant delivery for crowdsourced platforms. Industrial Engineering, 25(04), 100-107. Yu, J., Bian, Z., & Luo, T. (2022). Multi-objective instant delivery route optimization model based on balance criteria. Modernization of Management, 42(02), 94-99. Yu, Y. L., & Xiao, T. J. (2021). Analysis of cold-chain service outsourcing modes in a fresh agri-product supply chain. Transportation Research Part E: Logistics and Transportation Review, 148, 102264. Zhang, H.-F., Ge, H.-W., Li, T., Su, S. Z., & Tong, Y. B. . (2024). Three-stage multi-modal multi-objective differential evolution algorithm for vehicle routing problem with time windows. Intelligent Data Analysis, 28(2), 485–506.. Zhang, L., Zhang, J., & Xiao, B. (2021). Multi-objective O2O instant delivery route optimization considering customer priority. Industrial Engineering and Management, 26(02), 196-204. Zhang, Y., Yuan, C., & Wu, J. (2020). Vehicle routing optimization of instant distribution routing based on customer satisfaction. Information, 11(1), 36. Zhang, Y., Chu, F., Che, A., & Li, Y. (2024). Closed-loop inventory routing problem for perishable food with returnable transport items selection. International Journal of Production Research, 62(1-2), 501-521. Zhao, W., Bian, X., & Mei, X. (2024). An adaptive multi-objective genetic algorithm for solving heterogeneous green city vehicle routing problem. Applied Sciences-Basel, 14(15). Zhao, Z., Ma, Y., Tian, Y., Ding, Z., Zhang, H., & Tong, S. (2025). Research on integrated design method of wide-range hypersonic vehicle/engine based on dynamic multi-objective optimization. Aerospace Science and Technology, 159. Zhen, L., Wu, J., Laporte, G., & Tan, Z (2023). Heterogeneous instant delivery orders scheduling and routing problem. Computers & Operations Research, 157.