The plastics industry faces critical challenges in resource management, particularly under circular economy models. Global demand for plastic products reached 280 million tons in 2018, twice the level of 2000 [1]. In Chile, apparent consumption has grown 3.6% annually, creating both opportunities and challenges for recycling-based companies [2]. Procesadora de Plásticos Puelche (PPP), which transforms plastic waste into new products, reports that 42.9% of its monthly orders are delayed due to manual planning. This study proposes a mixed-integer linear programming model to optimize production planning by allocating orders to machines and shifts while considering setup times, sequencing, and capacity constraints. Operational data were obtained from company records, observations, and interviews. The model was implemented in AMPL and solved with CPLEX. Validation with historical data showed a reduction of late orders from 51 to 5 (a 90% decrease), improved machine utilization, and 94% accuracy in shift assignments. These results demonstrate the model’s value as a practical planning tool for PPP and similar recycling-based manufacturers. Future work could extend the model to address dynamic demand and preventive maintenance.