Energy cost optimization for the Flexible Job Shop Scheduling Problem considering Photovoltaic and Battery Energy Storage Systems

This paper proposes an optimization model based on MILP that was developed, focused on energy efficiency in an industrial context (textile), to solve an FJSSP (Flexible Job Shop Scheduling Problem). The aim is to analyze the impact of the integration of energy resources in production planning and evaluate possible reductions in energy costs under different conditions. The search for new perspectives and advantages is essential to reduce costs, maintain competitiveness, and consider sustainability. The formulation that considers production processes and energy aspects, including machine operational states such as working and standby conditions, allowing the inclusion or absence of energy resources such as PV (Photovoltaic) and BESS (Battery Energy Storage System). The case studies focus on cost optimization, which, compared to the baseline (makespan optimization), revealed a 18.87% cost reduction. While with the PV integration into the system generated cost reductions up to 44.75% by allowing self-consumption and reducing dependence on grid energy. The isolated use of the BESS revealed (20.23%) reduction, while providing additional flexibility to shift energy. The combination of PV and BESS achieved the highest savings of 46.11% (and 24.90% for the makespan solution), emphasizing the cost-oriented, energy-aware scheduling approach. In general, the model developed is proven to solve FJSSP and reduce energy costs, supporting manufacturing decisions.