An Optimization Model for a Two-Echelon Remanufacturing Reverse Supply Chain with Carbon Emission Reduction Consideration

Mingxin Hou; Qiaolun Gu

doi:10.6919/ICJE.202606_12(6).0017

Authors

Mingxin Hou
Qiaolun Gu

DOI:

https://doi.org/10.6919/ICJE.202606_12(6).0017

Keywords:

Remanufacturing Reverse Supply Chain; Multimodal Transportation; Carbon Emissions; Network Optimization.

Abstract

To address the high level of carbon emissions in the transportation stage of remanufacturing reverse supply chains, this study develops a two-echelon network optimization model that incorporates multiple transportation modes. Two mixed-integer linear programming (MILP) models are formulated with the objectives of minimizing total cost and carbon emissions, respectively. A case study based on the Yangtze River Delta region is conducted to compare road transportation with multimodal transportation. The results indicate that under a single road transportation mode, the cost-minimization and emission-minimization objectives are aligned. In contrast, multimodal transportation achieves approximately a 48.9% reduction in carbon emissions with only a marginal increase in total cost. These findings demonstrate that multimodal transport provides an effective pathway for low-carbon optimization in remanufacturing supply chains.

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References

[1] Alidaee, B., & Wang, H. (2026). Multilevel facility location optimisation: a novel integer programming formulation and approaches to heuristic solutions. International Journal of Production Research, 64(6), 2087–2108.

[2] Abadi, H. A. M., Saraswat, K., Adhikari, S., et al. (2025). Designing a Sustainable Multi-Objective Mixed-Integer Linear Programming (MILP) Model for Shrimp Supply Chains. In IISE Annual Conference Proceedings (pp. 1–6).

[3] Zhang, W., Huang, C., Gao, J., et al. (2025). Robust location-allocation decision considering casualty prioritization in multi-echelon humanitarian logistics network. Information Sciences, 695, 121731.

[4] Ahmadchali, A. M., Afrouzi, E. M., Javadian, N., et al. (2024). A robust location-allocation model for optimizing a multi-echelon blood supply chain network under uncertainty. OPSEARCH, Advance online publication, 1–53.

[5] Izadian, B. A., Pasandideh, R. H. S., & Abad, K. K. R. A. (2024). A new approach for reliability modeling in green closed-loop supply chain design under post-pandemic conditions: A case study. Computers & Chemical Engineering, 189, 108803.

[6] Juanjuan, P. (2023). Effectiveness of Mixed Fuzzy Time Window Multi-objective Allocation in E-Commerce Logistics Distribution Path. International Journal of Computational Intelligence Systems, 16(1).

[7] Ram, P. S. A., Billard, H., Perriere, F., et al. (2026). Viral and grazer regulation of bacterial mediated carbon cycling in a temperate eutrophic freshwater ecosystem. Limnologica, 118, 126334.

[8] Bastos, T., Teixeira, L., & Nunes, R. J. L. (2025). Digitalization of the residual biomass supply chain: A sustainable analysis of the current state using a hybrid model approach. Energy Sources, Part B: Economics, Planning, and Policy, 20(1).

[9] Ali, S. S., Hossain, S. S., & Mohsin, A. M. (2025). Sustainable and Cost-Effective Cooling: A Case Study of Night-Time Heat Pump Operation and Thermal Energy Storage Integration. Journal of Chemical Engineering of Japan, 58(1).

[10] Ye, B., Wang, Y., Lei, X., et al. (2025). Multi-Depot Vehicle Routing Problem with Collaborative Replenishment Using ALNS–ABC Algorithm. International Journal of Software Engineering and Knowledge Engineering, 36(4).

[11] Khujamberdiev, R., & Cho, M. H. (2025). Hybrid Fuels for CI Engines with Biofuel Hydrogen Ammonia and Synthetic Fuel Blends. Energies, 18(11), 2758. https://doi.org/10.3390/en18112758

[12] Victor, N., & Nichols, C. (2024). Future of hydrogen in the U.S. energy sector: MARKAL modeling results. Applications in Energy and Combustion Science, 18, 100259.

[13] Costa, R. O., Marquet, O., Fu, X., et al. (2026). A longitudinal study on the emotional and behavioural impacts of banning e-scooters on public transport. Transportation Research Interdisciplinary Perspectives, 37, 101963.

[14] Luan, S., Ma, J., Gan, D., et al. (2026). Decarbonizing regional air transport: Strategic pathways for sustainable multi-airport systems in developing economies. Journal of Air Transport Management, 135, 103023.

[15] Batikh, S. A., Alzahrani, A. Y., Battikh, S. M., et al. (2026). A framework for multi-target temperature profile generation using functional PCA and Gaussian mixture models for Fire PRA. Annals of Nuclear Energy, 235, 112333.

[16] Zizi, M., Hmamou, Y., Chafi, A., et al. (2026). Dynamic route optimization in smart logistics using hybrid machine learning models. Smart and Resilient Transportation, 8(1), 79–100.