Energy-Aware Flexible Flow-Shop Scheduling for Sustainable Manufacturing: A Multi-Objective Approach

Abstract

Traditional manufacturing scheduling often focuses on minimizing makespan, overlooking rising energy costs and sustainability pressures. Global energy demand is expected to grow by 11-18% by 2050, with electricity becoming the dominant source due to industrial electrification and Net Zero goals. UK manufacturing electricity prices have increased by up to 87% since 2019, highlighting the need for energy-aware production strategies to reduce costs and environmental impact. This study addresses flexible flow-shop scheduling problems (FFSPs) by developing an energy-aware approach that minimizes both total electricity consumption (TEC) and makespan. A multi-objective mathematical model is proposed, incorporating sequence-dependent setup times to reflect real-world complexity in energy-intensive manufacturing. To efficiently solve large-scale instances of the proposed model, a multi-objective genetic algorithm (MOGA) is developed and evaluated using a diverse set of randomly generated test problems covering different problem sizes and parameter settings. Its performance is compared against two well-known evolutionary algorithms, SPEA2 and PESA2, using three widely adopted multi-objective evaluation metrics. The results show that MOGA achieves more effective trade-offs between energy consumption and production time. By incorporating energy considerations into scheduling decisions, the proposed approach supports cost reduction, environmental sustainability, and improved operational robustness in modern manufacturing systems. Overall, the findings contribute to advancing green production scheduling and energy-aware smart factory development.