Adaptive Customization of Electronic Commerce Packaging for Sustainable Business Development

Shenglin Liu; Gongchao Wan

doi:10.71113/JMSS.v2i1.158

Authors

Shenglin Liu College of Science and Technology Ningbo Unversity, Ningbo, Zhejiang, China
Gongchao Wan Graduate School of International, East Asian Studies , Hanyang University, Korea

DOI:

https://doi.org/10.71113/JMSS.v2i1.158

Keywords:

Adaptive Customization, Electronic Commerce, NSGA-II Algorithm, Unconstraint Mixed-integer Linear Programming

Abstract

To address the growing demand for sustainable practices in e-commerce logistics, this research explores the innovative application of the NSGA-II algorithm for customized packaging optimization in distribution. A novel unconstraint mixed-integer linear programming mathematical model was developed and integrated with the NSGA-II algorithm to optimize packaging design dimensions and material properties. The approach emphasizes flexibility, compressibility, and adaptability to achieve an optimal balance between resource efficiency and product protection. Through rigorous simulation experiments, the NSGA-II algorithm demonstrated significant material savings while maintaining packaging integrity, achieving reductions of 1.87% in packaging quantity, 8.97% in volume, and 3.33% in weight. The results underscore the model’s alignment with e-commerce objectives of cost reduction and environmental impact minimization, offering a scalable framework for resource-efficient and sustainable distribution packaging solutions.

Downloads

Download data is not yet available.

References

[1]Escursell, S., Llorach-Massana, P., & Roncero, M. B. (2021). Sustainability in e-commerce packaging: A review. Journal of cleaner production, 280, 124314.

[2]Zhou, L., Wang, X., Ni, L., & Lin, Y. (2016). Location-routing problem with simultaneous home delivery and customer’s pickup for city distribution of online shopping purchases. Sustainability, 8(8), 828.

[3]Pålsson, H., Pettersson, F., & Hiselius, L. W. (2017). Energy consumption in e-commerce versus conventional trade channels-Insights into packaging, the last mile, unsold products and product returns. Journal of cleaner production, 164, 765-778.

[4]Rizet, C., Cornélis, E., Browne, M., & Léonardi, J. (2010). GHG emissions of supply chains from different retail systems in Europe. Procedia-Social and Behavioral Sciences, 2(3), 6154-6164.

[5]Lu, S., Yang, L., Liu, W., & Jia, L. (2020). User preference for electronic commerce overpackaging solutions: Implications for cleaner production. Journal of Cleaner Production, 258, 120936.

[6]Duan, H., Song, G., Qu, S., Dong, X., & Xu, M. (2019). Post-consumer packaging waste from express delivery in China. Resources, Conservation and Recycling, 144, 137-143.

[7]Rochman, C. M., Browne, M. A., Halpern, B. S., Hentschel, B. T., Hoh, E., Karapanagioti, H. K., ... & Thompson, R. C. (2013). Classify plastic waste as hazardous. Nature, 494(7436), 169-171.

[8]Fan, W., Xu, M., Dong, X., & Wei, H. (2017). Considerable environmental impact of the rapid development of China's express delivery industry. Resources, Conservation and Recycling, 126, 174-176.

[9]Pinos, J., Hahladakis, J. N., & Chen, H. (2022). Why is the generation of packaging waste from express deliveries a major problem?. Science of The Total Environment, 830, 154759.

[10]Duan, H., Song, G., Qu, S., Dong, X., & Xu, M. (2019). Post-consumer packaging waste from express delivery in China. Resources, Conservation and Recycling, 144, 137-143.

[11]Chen, Y., Awasthi, A. K., Wei, F., Tan, Q., & Li, J. (2021). Single-use plastics: Production, usage, disposal, and adverse impacts. Science of the total environment, 752, 141772.

[12]Rong Zhou. (2017). Discussion on the development and problems of logistics packaging under e-commerce environment. Finance and Management, 1(1), 123-124.

[13]Zhang Juan. (2024). Problems and countermeasures of green logistics packaging based on e-commerce. E-Commerce Letters, 13, 5812.

[14]Zhao Lin, & Guo Zhiwen. (2022). Research on the development of green express packaging of e-commerce in my country under the goal of “dual carbon”. Sustainable Development, 12, 931.

[15]Gurumoorthy, K. S., Sanyal, S., & Chaoji, V. (2020). Think out of the package: Recommending package types for e-commerce shipments. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 290-305). Cham: Springer International Publishing.

[16]Yang, G., & Mu, C. (2020). A machine learning approach to shipping box design. In Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 1 (pp. 402-407). Springer International Publishing.

[17]Martin, M., de Queiroz, T. A., & Morabito, R. (2024). Solving the three-dimensional open-dimension rectangular packing problem: A constraint programming model. Computers & Operations Research, 167, 106651.

[18]Yang, J., Liu, H., Liang, K., Zhou, L., & Zhao, J. (2024). Variable neighborhood genetic algorithm for multi-order multi-bin open packing optimization. Applied Soft Computing, 163, 111890.

[19]Xin, J., Meng, C., D’Ariano, A., Wang, D., & Negenborn, R. R. (2021). Mixed-integer nonlinear programming for energy-efficient container handling: Formulation and customized genetic algorithm. IEEE Transactions on Intelligent Transportation Systems, 23(8), 10542-10555.

[20]Fang, J., Rao, Y., Liu, P., & Zhao, X. (2021). Sequence transfer-based particle swarm optimization algorithm for irregular packing problems. IEEE Access, 9, 131223-131235.

[21]Zhang, C., & Zhai, Y. (2021). Air container loading based on improved genetic algorithm. Journal of Beijing University of Aeronautics and Astronautics, 47(7), 1345-1352.

[22]Kucukyilmaz, T., & Kiziloz, H. E. (2018). Cooperative parallel grouping genetic algorithm for the one-dimensional bin packing problem. Computers & Industrial Engineering, 125, 157-170.

[23]Berahas, A. S., Xie, M., & Zhou, B. (2025). A Sequential Quadratic Programming Method With High-Probability Complexity Bounds for Nonlinear Equality-Constrained Stochastic Optimization.

[24]SIAM Journal on Optimization, 35(1), 240-269.

[25]Alhijawi, B., & Awajan, A. (2024). Genetic algorithms: Theory, genetic operators, solutions, and applications. Evolutionary Intelligence, 17(3), 1245-1256.

[26]Bagchi, T. P., & Bagchi, T. P. (1999). The nondominated sorting genetic algorithm: NSGA. Multiobjective Scheduling by Genetic Algorithms, 171-202.

[27]Yin, Q., Wu, G., Sun, G., & Gu, Y. (2025). Multi-objective orbital maneuver optimization of multi-satellite using an adaptive feedback learning NSGA-II. Swarm and Evolutionary Computation, 93, 101835.