Quantum-classical hybrid algorithm using quantum annealing for multi-objective job shop scheduling

• URL: http://arxiv.org/abs/2511.03257v1
• Date: Wed, 05 Nov 2025 07:39:09 GMT
• Title: Quantum-classical hybrid algorithm using quantum annealing for multi-objective job shop scheduling
• Authors: Kenta Sawamura, Kensuke Araki, Naoki Maruyama, Renichiro Haba, Masayuki Ohzeki,
• Abstract summary: Efficient production planning is essential in modern manufacturing to improve performance indicators such as lead time and to reduce reliance on human intuition.<n>We develop a quantum-classical hybrid algorithm that decomposes the problem into two subproblems: resource allocation and task scheduling.<n> Experimental results demonstrate that our hybrid approach achieves superior solution quality and computational efficiency compared to traditional monolithic methods.
• Score: 0.5872014229110214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient production planning is essential in modern manufacturing to improve performance indicators such as lead time and to reduce reliance on human intuition. While mathematical optimization approaches, formulated as job shop scheduling problems, have been applied to automate this process, solving large-scale production planning problems remains computationally demanding. Moreover, many practical scenarios involve conflicting objectives, making traditional scalarization techniques ineffective in finding diverse and useful Pareto-optimal solutions. To address these challenges, we developed a quantum-classical hybrid algorithm that decomposes the problem into two subproblems: resource allocation and task scheduling. Resource allocation is formulated as a quadratic unconstrained binary optimization problem and solved using annealing-based methods that efficiently explore complex solutions. Task scheduling is modeled as a mixed-integer linear programming problem and solved using conventional solvers to satisfy detailed scheduling constraints. We validated the proposed method using benchmark instances based on foundry production scenarios. Experimental results demonstrate that our hybrid approach achieves superior solution quality and computational efficiency compared to traditional monolithic methods. This work offers a promising direction for high-speed, multi-objective scheduling in industrial applications.

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