<p>In today’s competitive and uncertain business environment, the design of efficient supply chain networks is a critical challenge. This study develops a comprehensive mathematical model for closed-loop supply chain network planning that consolidates buy-back and option contracts under fuzzy uncertainty. The mathematical formulation incorporates buy-back contracts to incentivize product returns and option contracts to provide flexibility in capacity planning. The proposed bi-objective optimization framework simultaneously minimizes total supply chain costs and maximizes network reliability, providing decision-makers with a balanced approach to performance evaluation. To address the ambiguity of real-world parameters, fuzzy set theory is applied, allowing the integration of vague and imprecise expert information into the decision-making process. A novel fuzzy multi-objective solution methodology is developed that effectively handles the flexibility of constraints and fuzzy parameters while preserving computational efficiency. Numerical illustrations depict the effectiveness of the methodology and highlight the role of contract design and uncertainty modeling in improving supply chain performance. The integration of contractual mechanisms with fuzzy optimization enables more realistic modeling of supply chain coordination under uncertainty. The study offers insights with relevance to theory as well as practice by offering a reliable and applicable decision-facilitation tool for managers facing uncertainty in CLSC planning.</p>