Energy-Aware Production Planning and Control in Container Glass Manufacturing with Hydrogen-Fueled Furnace

This paper presents a mixed-integer linear programming (MILP) model for energy-aware production planning and control (PPC) in container glass manufacturing, focusing on furnaces that supply molten glass to parallel molding machines. The model represents a real-world setting in which furnaces operate continuously, utilizing hybrid energy configurations that combine hydrogen (H₂) or natural gas (NG) with electrical boosting (EB). The model also accounts for energy-related characteristics, such as melting efficiency and moisture rate, that influence furnace performance and molten glass quality. The proposed model incorporates key operational features, such as color campaign scheduling, product changeovers, machine efficiency, and inventory dynamics, while ensuring demand satisfaction across multiple periods. The objective is to minimize total costs, including energy consumption, CO₂ emissions penalties, inventory holding, and setup costs. The MILP formulation is implemented in Python and solved using the Gurobi Optimizer. A case study involving container glass production (e.g., bottles and jars) demonstrates the model’s capability to generate sustainable, cost-efficient production plans that account for energy configurations and furnace-machine interdependencies. The findings suggest that technological advancements and/or pricing adjustments are crucial to facilitate a cost-effective transition toward more sustainable manufacturing practices.