(402ag) Integrating Circular Economy Policies into Product Supply Chains: A Bi-Level Optimization Formulation

The transition towards a circular economy requires collaborative efforts between the multiple stakeholders participating in product supply chains, including multiple industries and governmental bodies that often have conflicting objectives. A key aspect of this transition is the expansion of supply chains to incorporate previously disconnected stages, such as waste collection and recycling, along with the stakeholders responsible for these processes. Policy implementation plays a crucial role in driving the selection of sustainable alternatives as the introduction of new regulations or incentives can change the operating behavior and impact the profits of stakeholders within a supply chain. ¹ Therefore, assessing whether policies drive meaningful change remains essential for ensuring their success. The interconnected stakeholders and their conflicting objectives introduce challenges in the modeling of optimal decision-making within supply chain design and operation.² To address this, game-theoretic approaches are required to find the optimal decision-making process for all stakeholders^3,4, and capture the hierarchical interaction between them.^5,6

In this study, we present a bi-level optimization model formulation to explore the integration of circular economy policies into product supply chains. By integrating different policy instruments, including taxes and financial incentives, the study aims to identify strategies that promote sustainable solutions while ensuring economic feasibility for businesses. The model is applied to a coffee packaging case study,⁷ analyzing multiple packaging alternatives and waste management pathways under three scenarios: (i) implementing carbon taxes, (ii) considering financial incentives, and (iii) combining carbon taxes with financial incentives. In the problem formulation, the upper-level decision-maker (local government) seeks to reduce environmental impacts by maximizing circularity and/or minimizing greenhouse gas emissions through policy interventions. Meanwhile, the lower-level decision-makers (coffee roasting companies) aim to minimize costs when selecting packaging and waste management alternatives under the extended producer responsibility strategy. The resulting bi-level mixed-integer optimization problem is solved using the data-driven optimization framework DOMINO.⁶ Whitin this framework, the upper-level problem is solved using a particle swarm optimization⁸, and the lower-level problem is solved using an analytical global optimization solver (gurobi). A sensitivity analysis is conducted to evaluate the impact of varying government budget constraints on industrial operations and sustainability outcomes. The findings provide valuable insights into optimal policy strategies that drive circular economy adoption while ensuring financial viability for businesses.

References

(1) Jin, M.; Granda-Marulanda, N. A.; Down, I. The Impact of Carbon Policies on Supply Chain Design and Logistics of a Major Retailer. J Clean Prod 2014, 85, 453–461. https://doi.org/10.1016/j.jclepro.2013.08.042.

(2) Avraamidou, S.; Baratsas, S. G.; Tian, Y.; Pistikopoulos, E. N. Circular Economy - A Challenge and an Opportunity for Process Systems Engineering. Comput Chem Eng 2020, 133. https://doi.org/10.1016/j.compchemeng.2019.106629.

(3) Chen, W.; Hu, Z. H. Using Evolutionary Game Theory to Study Governments and Manufacturers’ Behavioral Strategies under Various Carbon Taxes and Subsidies. J Clean Prod 2018, 201, 123–141. https://doi.org/10.1016/j.jclepro.2018.08.007.

(4) Yu, M.; Cruz, J. M.; Li, D. “Michelle.” The Sustainable Supply Chain Network Competition with Environmental Tax Policies. Int J Prod Econ 2019, 217, 218–231. https://doi.org/10.1016/j.ijpe.2018.08.005.

(5) Camacho-Vallejo, J.-F.; Corpus, C.; Villegas, J. G. Metaheuristics for Bilevel Optimization: A Comprehensive Review. Comput Oper Res 2024, 161, 106410. https://doi.org/10.1016/j.cor.2023.106410.

(6) Beykal, B.; Avraamidou, S.; Pistikopoulos, I. P. E.; Onel, M.; Pistikopoulos, E. N. DOMINO: Data-Driven Optimization of Bi-Level Mixed-Integer NOnlinear Problems. Journal of Global Optimization 2020, 78 (1), 1–36. https://doi.org/10.1007/s10898-020-00890-3.

(7) Munoz-Briones, P. A.; Munguia-Lopez, A. del C.; Sanchez-Rivera, K. L.; Zavala, V. M.; Huber, G. W.; Avraamidou, S. Optimal Design of Food Packaging Considering Waste Management Technologies to Achieve Circular Economy. In Foundations of Computer Aided Process Design (FOCAPD 2024); PSE PRESS, 2024; pp 820–828. https://doi.org/10.69997/sct.154335.

(8) Kennedy, J.; Eberhart, R. Particle Swarm Optimization. In Proceedings of ICNN’95 - International Conference on Neural Networks; IEEE; pp 1942–1948. https://doi.org/10.1109/ICNN.1995.488968.