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run_continuous.py
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run_continuous.py
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from environment_continuous import Environment_continuous
from env_activities import *
from tools import gathering_data, remove_warmup_period, write_to_dataframe
from analysis import calculate_averages
from concurrent.futures.process import ProcessPoolExecutor
from concurrent.futures import wait
def run_sim(exp_no):
environment = Environment_continuous()
environment.setup()
# KPI data storage
containerinfo = {}
shipmentinfo = {}
transporterinfo = {}
producer_storage_info = {}
matching_distances = []
for day in range(environment.config.run_length): # run model!
# Perform daily actions and save KPI matching distance info
continuous_actions(environment, matching_distances, day)
# save KPI results per simulation day
gathering_data(environment, day, containerinfo, shipmentinfo,
transporterinfo, producer_storage_info)
# rewrite gathered data into dataframe
data = write_to_dataframe(environment,containerinfo,shipmentinfo,
transporterinfo, producer_storage_info)
# remove data during warmup period
data = remove_warmup_period(environment, data)
# Calculate KPI scores
calculate_averages(data, matching_distances, exp_no)
def job(space):
for exp_no in range(*space):
print("Job: {0}".format(exp_no))
run_sim(exp_no)
if __name__ == "__main__":
'''Determine total number of experiments and specify the number of threads
to use'''
no_threads = 1
jobs_per_thread = 1
executor = ProcessPoolExecutor(no_threads)
items = [[start, start + jobs_per_thread] for start in range(
0, jobs_per_thread * no_threads, jobs_per_thread)]
futures = [executor.submit(job, item) for item in items]
wait(futures)