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Multi-objective rescheduling of job shop scheduling problems in manufacturing using Quantum Annealing

Year: 2024

Type: article

Abstract: Unforeseen events in manufacturing necessitate rescheduling to minimize lead times and maintain continuous production. Reducing rescheduling effort is also crucial otherwise additional setup times are incurred. This paper extends a dynamic rescheduling workflow, previously demonstrating success with Quantum Annealing through a multicriterial optimization. It introduces a second objective function considering rescheduling effort alongside makespan. The study addresses job arrivals and machine failures, employing a binary quadratic model for optimization. Results highlight the superiority of Quantum Annealing for larger problems compared to classical metaheuristics. Nevertheless, parameter selection complexities urge further exploration such as exploring deep learning techniques to tackle this challenge.

Source: Manufacturing Letters

DOI: 10.1016/j.mfglet.2024.09.066

Authors: Philipp Schworm, Xiangqian Wu, Matthias Klar, Jan C. Aurich

Institution: Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $1,690

OpenAlex ID: W4402747977

FWCI: 0.621

Cites: 14

Cited by: 3

Related to: 10