Optimizing product return timing in integrated viable circular manufacturing systems: a two-stage stochastic approach

This study examines how product return timing links configuration selection and recovery planning in circular manufacturing systems under disruption. We develop a two-stage stochastic mixed-integer linear programming model that selects a product configuration before uncertainty is revealed and adapts return timing, production, and recovery allocation to each scenario. Return timing changes prescribed inspection-grade shares and thus the quantities available to eligible recovery routes. The model incorporates a configuration sustainability requirement, production and recovery capacities, market absorption limits, and conditional value-at-risk (CVaR) of recovery-duration-weighted disruption impacts. In an illustrative LED luminaire case, adaptive timing reduces the penalized objective under a feasible projected-plan rule. Most of this difference comes from avoiding shortage penalties. Adaptive timing matches the earliest-feasible rule at the base early-return cost; a separate benefit from optimizing timing appears at higher early-return costs. The results identify when controllable withdrawal timing can preserve service and support recovery, while distinguishing shortage avoidance from operating-cost savings.