Skip to main content
Article | Industrial and Domestic Waste Management | Volume 5 - Issue 2 - 2025 | 97-109 | https://doi.org/10.53623/idwm.v5i2.752
© 2025 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Optimization of Manufacturing-Remanufacturing Model in Circular Supply Chain Considering Warehouse Capacity Constraints by Using Chinese Pangolin Optimizer Algorithm

Author(s): Dana Marsetiya Utama ORCID https://orcid.org/0000-0002-3214-9753 , Hanum Salsabila Djirimu
Author(s) information:
Department of Industrial Engineering, Universitas Muhammadiyah Malang, Malang City, East Java 65144, Indonesia

Corresponding author

Industrial and Domestic Waste Management

Volume 5 - Issue 2 - 2025

 About the journal
Submit your article

This research developed an optimization model within a circular supply chain framework incorporating factors such as carbon emissions, social sustainability, and warehouse capacity limitations. The model adopted a modified Economic Order Quantity (EOQ) approach, with a comprehensive cost assessment that included production cost, remanufacturing cost, storage cost, disposal cost, and penalty cost for emissions, all formulated within a Mixed Integer Nonlinear Programming (MINLP) structure. To address the complex nonlinear problem, the metaheuristic Chinese Pangolin Optimizer (CPO) algorithm was applied, as it effectively balanced solution exploration and exploitation. The simulation results indicated the optimal combination of production lot size, remanufacturing, and the share of reusable goods, achieving the minimum total system cost. The sensitivity analysis showed the significant influence of production and remanufacturing costs, emissions, and the rate of product returns on system efficiency. Overall, this research demonstrated more credible, cost-efficient, and sustainable inventory control approaches in a circular supply chain by considering warehouse constraints and applying the CPO.

Read Full-Text
Previous article
Next article

Jayalath, M.M.; Perera, H.N.; Ratnayake, R.M.C.; Thibbotuwawa, A. (2025). Harvesting sustainability: Transforming traditional agri-food supply chains with circular economy in developing economies. Cleaner Waste Systems, 11, 100264. https://doi.org/10.1016/j.clwas.2025.100264.

Mahmoud, H.A.; Essam, S.; Hassan, M.H.; Sobh, A.S. (2024). Modeling circular supply chains as an approach for waste management: A systematic review and a conceptual framework. Journal of Engineering Research. https://doi.org/10.36909/jer.v12i5.19837.

Vidal, U.; Obregon, M.; Ramos, E.; Verma, R.; Coles, P.S. (2024). Sustainable and risk-resilient circular supply chain: A Peruvian paint manufacturing supply chain model. Sustainable Futures, 7, 100207. https://doi.org/10.1016/j.sftr.2024.100207.

Pongen, I.; Ray, P.; Govindan, K. (2024). Creating a sustainable closed-loop supply chain: An incentive-based contract with third-party E-waste collector. Journal of Cleaner Production, 462, 142351. https://doi.org/10.1016/j.jclepro.2023.142351.

Corsini, R.R.; Cannella, S.; Dominguez, R.; Costa, A. (2024). Closed-loop supply chains: How do production capacity and production control policies impact the performance? Computers & Industrial Engineering, 189, 109939. https://doi.org/10.1016/j.cie.2023.109939.

Fernández-Arribas, C.L.; Ponte, B.; Fernández, I. (2024). Shaping closed-loop supply chain dynamics: Mitigating the bullwhip effect and improving customer satisfaction in production systems with material reuse. Computers & Industrial Engineering, 195, 110407. https://doi.org/10.1016/j.cie.2024.110407.

Wan, P.; Xie, Z. (2024). Decision making and benefit analysis of closed-loop remanufacturing supply chain considering government subsidies. Heliyon, 10(19), e38487. https://doi.org/10.1016/j.heliyon.2024.e38487.

Govindan, K.; Salehian, F.; Kian, H.; Hosseini, S.T.; Mina, H. (2023). A location-inventory-routing problem to design a circular closed-loop supply chain network with carbon tax policy for achieving circular economy: An augmented epsilon-constraint approach. International Journal of Production Economics, 257, 108771. https://doi.org/10.1016/j.ijpe.2022.108771.

Crîstiu, D.; d'Amore, F.; Mocellin, P.; Bezzo, F. (2023). Multi-objective optimisation of a carbon capture and sequestration supply chain under seismic risk constraints. A case study considering industrial emissions in Italy. International Journal of Greenhouse Gas Control, 129, 103993. https://doi.org/10.1016/j.ijggc.2023.103993.

Cutore, E.; Volpe, R.; Gonzalez Alriols, M.; Antxustegi, M.M.; Fichera, A. (2024). Multi-objective optimization of a hydrogen supply chain network: Wind and solid biomass as primary energy sources for the public transport in Sicily. Energy Conversion and Management, 314, 118717. https://doi.org/10.1016/j.enconman.2024.118717.

Poonia, V.; Kulshrestha, R.; Sangwan, K.S. (2024). A comparative study of ε-constraint, LP-metric, and weighted sum multi-objective optimization methods in a circular economy. Procedia CIRP, 122, 294–299. https://doi.org/10.1016/j.procir.2023.12.048.

Zhao, S.; Wang, M.; Zhou, Q.; Xia, X. (2025). Managing manufacturer encroachment and product conflicts in a closed-loop supply chain: The case of information asymmetry. Omega, 132, 103236. https://doi.org/10.1016/j.omega.2024.103236.

Fang, Z.; Wang, M.; Ji, L.; Xie, Y.; Zhen, J. (2025). Multi-objective inexact optimization of the biomass supply chain from an energy-land-carbon nexus perspective. Energy Nexus, 17, 100358. https://doi.org/10.1016/j.nexus.2024.100358.

Rouhani, S.; Wardley, L.J.; Amin, S.H. (2025). A comprehensive survey into reverse logistics and closed-loop supply chain aspects to provide analyses and insights for implementation. Journal of Cleaner Production, 490, 144743. https://doi.org/10.1016/j.jclepro.2024.144743.

Miyangaskary, M.K.; Keivanpour, S.; Safari, H. (2025). A multi-objective optimization model of a closed-loop supply chain for supplier selection and order allocation under uncertainty: A case study of retail stores for protein products. Transportation Research Procedia, 82, 323–341. https://doi.org/10.1016/j.trpro.2024.12.175.

Bui, M.; et al. (2018). Carbon capture and storage (CCS): the way forward. Energy & Environmental Science, 11(5), 1062–1176. https://doi.org/10.1039/C7EE02342A.

Battini, D.; Glock, C.; Grosse, E.; Persona, A.; Sgarbossa, F. (2017). Ergo-lot-sizing: An approach to integrate ergonomic and economic objectives in manual materials handling. International Journal of Production Economics, 185, 120–129. https://doi.org/10.1016/j.ijpe.2016.12.012.

Battini, D.; Faccio, M.; Persona, A.; Sgarbossa, F. (2011). New methodological framework to improve productivity and ergonomics in assembly system design. International Journal of Industrial Ergonomics, 41(1), 30–42. https://doi.org/10.1016/j.ergon.2010.12.001.

Taleizadeh, A.A.; Soleymanfar, V.R.; Govindan, K. (2018). Sustainable economic production quantity models for inventory systems with shortage. Journal of Cleaner Production, 174, 1011–1020. https://doi.org/10.1016/j.jclepro.2017.11.024.

Ghasemi, P.; Ali, S.M.; Abolghasemian, M.; Malakoot, R.A.; Chobar, A.P. (2025). A stochastic sustainable closed-loop supply chain networks for used solar photovoltaic systems: Meta-heuristic comparison and real case study. Sustainable Operations and Computers, 6, 15–33. https://doi.org/10.1016/j.susoc.2024.100152.

Utama, D.M.; Kusuma, I.R.; Amallynda, I.; Baroto, T.; Jauhari, W.A. (2024). A single-vendor multi-buyer inventory model with multiple raw material and quality degradation: A case study on agri-food industry. Results in Control and Optimization, 14, 100353. https://doi.org/10.1016/j.rico.2024.100353.

Jauhari, W.A.; Wangsa, I.D.; Sofiana, A.; Utama, D.M. (2024). An integrated inventory model for a supply chain system with two competing retailers, carbon emissions, and price-and-service dependent demand. Sustainability Analytics and Modeling, 4, 100029. https://doi.org/10.1016/j.samod.2023.100029.

Read Full-Text
About this article

SUBMITTED: 02 July 2025
ACCEPTED: 19 August 2025
PUBLISHED: 12 September 2025
SUBMITTED to ACCEPTED: 48 days
DOI: https://doi.org/10.53623/idwm.v5i2.752

Cite this article
Utama, D. M., & Djirimu, H. S. (2025). Optimization of Manufacturing-Remanufacturing Model in Circular Supply Chain Considering Warehouse Capacity Constraints by Using Chinese Pangolin Optimizer Algorithm. Industrial and Domestic Waste Management, 5(2), 97–109. https://doi.org/10.53623/idwm.v5i2.752
Keywords
EOQ, manufacturing-remanufacturing, warehouse capacity, carbon emissions, Chinese Pangolin Optimizer, circular supply chain
Citations
0
Share this article