We conducted the experiments on seven different size instances for training and testing.

We used the notation |J| X |M| to denote that a DFJSP instance consists of |J| jobs and |M| machines.

Specifically, 10X10, 20X20, 30X30 are three static instances and four dynamic instances of various scales considering continuous arrival of jobs are set as 20X10, 30X15, 50X20, 100X20.

DOI Link: 
10.21227/z199-mj36
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[1] jiepin ding, "reinforcement learning for DFJSP", IEEE Dataport, 2022. [Online]. Available: http://dx.doi.org/10.21227/z199-mj36. Accessed: Dec. 30, 2024.
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doi = {10.21227/z199-mj36},
url = {http://dx.doi.org/10.21227/z199-mj36},
author = {jiepin ding },
publisher = {IEEE Dataport},
title = {reinforcement learning for DFJSP},
year = {2022} }
TY - DATA
T1 - reinforcement learning for DFJSP
AU - jiepin ding
PY - 2022
PB - IEEE Dataport
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jiepin ding. (2022). reinforcement learning for DFJSP. IEEE Dataport. http://dx.doi.org/10.21227/z199-mj36
jiepin ding, 2022. reinforcement learning for DFJSP. Available at: http://dx.doi.org/10.21227/z199-mj36.
jiepin ding. (2022). "reinforcement learning for DFJSP." Web.
1. jiepin ding. reinforcement learning for DFJSP [Internet]. IEEE Dataport; 2022. Available from : http://dx.doi.org/10.21227/z199-mj36
jiepin ding. "reinforcement learning for DFJSP." doi: 10.21227/z199-mj36