Graph JSP Environment Example¶

[4]:

from jsp_instance_utils.instances import ft06, ft06_makespan
from graph_jsp_env.disjunctive_graph_jsp_env import DisjunctiveGraphJspEnv
import numpy as np

[5]:

def main():
    env = DisjunctiveGraphJspEnv(
        jps_instance=ft06,
        reward_function_parameters={
            "scaling_divisor": ft06_makespan
        },
    )

    done = False
    info = {}
    while not done:
        # get valid action mask. sample expects it to be a numpy array of type int8
        mask = np.array(env.valid_action_mask()).astype(np.int8)
        action = env.action_space.sample(mask=mask)
        state, reward, terminated, truncated, info = env.step(action)

        done = terminated or truncated

    # console rendering
    env.render(show=["gantt_console", "graph_console"])
    print(f"makespan: {info['makespan']}")

[6]:

if __name__ == '__main__':
    main()

Job 0     ●-●-●-●-●-●     Machine 0 █
Job 1     ●-●-●-●-●-●     Machine 1 █
Job 2     ●-●-●-●-●-●     Machine 2 █
Job 3     ●-●-●-●-●-●     Machine 3 █
Job 4     ●-●-●-●-●-●     Machine 4 █
Job 5     ●-●-●-●-●-●     Machine 5 █
         ╔═══════════════════════════════════════════════════════╗
Job 0    ║           ████ ██████████            ███       ████   ║ Machine 0   █
Job 1    ║          ██████████████████  ███████████████████      ║ Machine 1   █
Job 2    ║        ███████  ████████████████████                  ║ Machine 2   █
Job 3    ║████████    ███████                      ██████████████║ Machine 3   █
Job 4    ║████████████████████                                   ║ Machine 4   █
Job 5    ║    █████              ███████████████████             ║ Machine 5   █
         ╚╦════╤════╤════╤════╤════╦════╤════╤════╤════╤════╦════╝
          0.0                      31.8                     63.6
makespan: 69