As global industrial sectors face increasing pressure to align with the Paris Agreement, the Sustainable Development Goals (SDGs), and the European Green Deal, the manufacturing industry must reconcile the historically conflicting objectives of maximizing production throughput and minimizing carbon intensity. This study proposes a robust Multi-Objective Mixed-Integer Linear Programming (MOMILP) model designed to optimize production scheduling in a carbon-constrained environment. Unlike traditional models that prioritize operational cost or makespan alone, our approach integrates real-time energy consumption data, machine-state power profiles, and dynamic carbon pricing mechanisms to determine the Pareto-optimal frontier between operational efficiency and environmental sustainability. We validate the proposed model using a high-fidelity case study from a Tier-1 automotive component manufacturing plant specializing in high-pressure engine block casting. Results indicate that a significant 15% reduction in carbon emissions can be achieved with a marginal 3.2% decrease in total throughput. This is attained by strategically shifting energy-intensive processes to non-peak hours, optimizing machine startup-shutdown cycles to minimize non-productive energy waste, and re-sequencing jobs to leverage machine thermal inertia. This research provides a scalable, quantitative framework for industrial managers to navigate the transition from Industry 4.0's automation focus to the human-centric and sustainable paradigm of Industry 5.0.
Kavitha Rani N (Fri,) studied this question.