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.

Dataset Files

You must be an IEEE Dataport Subscriber to access these files. Subscribe now or login.

[1] jiepin ding, "reinforcement learning for DFJSP", IEEE Dataport, 2022. [Online]. Available: http://dx.doi.org/10.21227/z199-mj36. Accessed: Feb. 04, 2023.
@data{z199-mj36-22,
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
UR - 10.21227/z199-mj36
ER -
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