Degree Type
Thesis
Date of Award
2014
Degree Name
Master of Science
Department
Industrial and Manufacturing Systems Engineering
First Advisor
Siggi Olafsson
Abstract
We present a policy based reinforcement learning scheduling algorithm with high level deadlock detection for job-shop discrete manufacturing systems without buffer being equipped. Deadlock is a highly undesirable phenomenon resulting from resource sharing and competition. Hence, we first propose detection algorithms for second and third level deadlocks. Subsequently, based on these high level deadlock detection algorithms, a new policy based reinforcement learning scheduling algorithm is developed in the context of buffer-less job-shop systems. Applying our reinforcement learning approach into scheduling algorithm to a set of 40 widely-used buffer-less job shop benchmark, satisfactory makespan can be obtained, which, to our knowledge, have never been published before. It is safe to conclude that our policy based reinforcement learning scheduling algorithm can be applied to other discrete event systems (e.g., computer operation systems, communication systems, and traffic systems).
DOI
https://doi.org/10.31274/etd-180810-1488
Copyright Owner
Mengmeng Chen
Copyright Date
2014
Language
en
File Format
application/pdf
File Size
50 pages
Recommended Citation
Chen, Mengmeng, "Policy based reinforcement learning approach Of Jobshop scheduling with high level deadlock detection" (2014). Graduate Theses and Dissertations. 13785.
https://lib.dr.iastate.edu/etd/13785
