Multi-objective Quantum Annealing approach for solving flexible job shop scheduling in manufacturing

TL;DR

This paper presents QASA, a quantum annealing-based algorithm for flexible job shop scheduling that outperforms classical methods in solution quality by integrating quantum and classical techniques.

Contribution

It introduces a novel quantum annealing approach for multi-objective FJSSP, combining Hamiltonian formulation with classical decomposition and decision logic.

Findings

QASA outperforms classical algorithms in solution quality.

QASA achieves better Pareto solutions with reasonable computational effort.

The method effectively handles large problem instances through decomposition.

Abstract

Flexible Job Shop Scheduling (FJSSP) is a complex optimization problem crucial for real-world process scheduling in manufacturing. Efficiently solving such problems is vital for maintaining competitiveness. This paper introduces Quantum Annealing-based solving algorithm (QASA) to address FJSSP, utilizing quantum annealing and classical techniques. QASA optimizes multi-criterial FJSSP considering makespan, total workload, and job priority concurrently. It employs Hamiltonian formulation with Lagrange parameters to integrate constraints and objectives, allowing objective prioritization through weight assignment. To manage computational complexity, large instances are decomposed into subproblems, and a decision logic based on bottleneck factors is used. Experiments on benchmark problems show QASA, combining tabu search, simulated annealing, and Quantum Annealing, outperforms a classical solving algorithm (CSA) in solution quality (set coverage and hypervolume ratio metrics). Computational efficiency analysis indicates QASA achieves superior Pareto solutions with a reasonable increase in computation time compared to CSA.