Circular supply chain network design: A robust probabilistic multi-objective optimisation approach

Circular supply chains (CSCs) are essential to advancing the circular economy (CE), which aims to minimise waste and maximise resource reuse through regenerative natural systems. CSCs connect actors in primary industries with those in the recycling, secondary materials, and energy sectors. However, the transition to effective CSCs requires a systemic shift that emphasises cross-sectoral collaboration and balances economic, environmental, and social objectives in an uncertain environment. Therefore, this study proposes a tri-objective multi-sector CSC network design model, formulated as a mixed-integer linear programming problem and solved using the ϵ-constraint method. To address both fuzzy and stochastic uncertainties in the CE context, a robust-fuzzy stochastic programming (RFSP) approach is used to integrate uncertain parameters such as demand and return rates, remanufacturing, recovery, recycling rates, and production costs. A sensitivity analysis of critical parameters, such as waste coefficients and the prices of primary and secondary products, further assesses the readiness of the network for a circular transition. The practical relevance of the model is demonstrated through a case study of the French tyre industry, which reveals complex interdependencies in the circular system and provides valuable insights for stakeholders in the French primary and secondary markets. The findings highlight the interrelated roles of these industries in circular transitions and underline the importance of integrating uncertainty into the design of CSC to effectively balance competing objectives. This study contributes to the academic literature by applying a robust methodological approach and provides policymakers and industry leaders with key insights for strategic decision-making in pursuit of comprehensive sustainability and resource circularity.