Scheduling for energy-efficient manufacturing using quantum annealing

An increasing number of manufacturers are adopting on-site renewable energy systems to reduce energy costs, which introduces new challenges for process scheduling in energy-efficient manufacturing. In flexible job shop systems, the fluctuating availability of renewable sources makes energy supply decisions closely dependent on production schedules, increasing scheduling complexity. Traditional optimization methods face rapidly growing computational demands as problem size and complexity increase. To address these challenges, this study proposes a quantum annealing based scheduling approach for energy efficient manufacturing integrating process and energy supply scheduling. The objective is to minimize energy cost, energy consumption, and makespan in flexible job shop systems with an integrated energy generation system. The approach is analyzed on benchmark instances with real energy data and compared with a classical method combined with simulated annealing and tabu search. The evaluation, based on widely used performance metrics in multi-objective optimization, demonstrates that the quantum annealing-based method achieves better solution quality in cardinality, convergence, distribution, and spread. Additionally, a comparative analysis of computing times shows that the proposed method outperforms the classical method, demonstrating its potential for practical applications.