Application of quantum annealing for scalable robotic assembly line optimization: a case study

TL;DR

This paper explores using quantum annealing on a D-Wave quantum computer to optimize robotic assembly line balancing, demonstrating potential benefits and current limitations in manufacturing efficiency.

Contribution

It introduces a novel quantum annealing approach for RALB, transforming the problem into a QUBO and applying a hybrid quantum-classical algorithm.

Findings

Quantum annealing can produce near-optimal solutions for RALB.

Quantum approach shows potential for cost reduction and efficiency improvement.

Hardware limitations currently restrict large-scale application.

Abstract

The even distribution and optimization of tasks across resources and workstations is a critical process in manufacturing aimed at maximizing efficiency, productivity, and profitability, known as Robotic Assembly Line Balancing (RALB). With the increasing complexity of manufacturing required by mass customization, traditional computational approaches struggle to solve RALB problems efficiently. To address these scalability challenges, we investigate applying quantum computing, particularly quantum annealing, to the real-world based problem. We transform the integer programming formulation into a quadratic unconstrained binary optimization problem, which is then solved using a hybrid quantum-classical algorithm on the D-Wave Advantage 4.1 quantum computer. In a case study, the quantum solution is compared to an exact solution, demonstrating the potential for quantum computing to enhance manufacturing productivity and reduce costs. Nevertheless, limitations of quantum annealing, including hardware constraints and problem-specific challenges, suggest that continued advancements in quantum technology will be necessary to improve its applicability to RALB manufacturing optimization.