A Quantum Annealing-Based Approach for Multi-Objective Optimization in Flexible Job Shop Scheduling using Quantum- Adaptive Flex Scheduler

The advent of quantum computing has introduced transformative potential for solving complex optimization problems, particularly in the field of manufacturing. This paper presents the Quantum-Adaptive Flex Scheduler (QAFS), a hybrid algorithm that leverages Quantum Annealing (QA) to address the Flexible Job Shop Scheduling Problem (FJSSP) with multiple objectives. QAFS integrates quantum mechanics with traditional heuristic methods to efficiently navigate the vast solution space of FJSSP, overcoming the computational limitations faced by classical algorithms. Our computational study demonstrates that QAFS significantly improves key performance metrics: it reduces makespan by 10–15%, enhances workload distribution by 5–10%, and achieves priority adherence 10–20% more effectively than Classical Scheduling Algorithms (CSA). Moreover, QAFS achieves superior solution quality, attaining an 85% C-metric coverage and a HyperVolume Ratio (HVR) of 0.92, underscoring its capability to generate Pareto-efficient solutions in multi-objective scenarios. These findings highlight the practical potential of QAFS to revolutionize production planning and control in manufacturing, offering a scalable and adaptive solution that responds effectively to real-time constraints and fluctuating market demands.