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

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.