Applying Decision Transformers to Enhance Neural Local Search on the Job Shop Scheduling Problem

Applying Decision Transformers to Enhance Neural Local Search on the Job Shop Scheduling Problem

<i>Background</i>: The job shop scheduling problem (JSSP) and its solution algorithms have been of enduring interest in both academia and industry for decades. In recent years, machine learning (ML) has been playing an increasingly important role in advancing existing solutions and build...

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Bibliographic Details
Main Authors: Constantin Waubert de Puiseau, Fabian Wolz, Merlin Montag, Jannik Peters, Hasan Tercan, Tobias Meisen
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:AI
Subjects:
neural combinatorial optimization
job shop scheduling
decision transformers
deep reinforcement learning
neural networks
operations research
Online Access:https://www.mdpi.com/2673-2688/6/3/48
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