Learning to Solve Job Shop Scheduling under Uncertainty

arXiv:2404.01308v1 Announce Type: cross
Abstract: Job-Shop Scheduling Problem (JSSP) is a combinatorial optimization problem where tasks need to be scheduled on machines in order to minimize criteria such as makespan or delay. To address more realistic scenarios, we associate a probability distribution with the duration of each task. Our objective is to generate a robust schedule, i.e. that minimizes the average makespan. This paper introduces a new approach that leverages Deep Reinforcement Learning (DRL) techniques to search for robust solutions, …

abstract arxiv cs.ai cs.lg delay distribution job machines optimization probability scheduling solve stat.ml tasks type uncertainty