def assign(self):
#print(initiation.Job)
for y in range(initiation.job_counter):
as_time = PriorityQueue()
for x in range(initiation.job_counter):
time = initiation.Job[x][3]
for t in range(10 * initiation.max_time.astype(int)):
if (initiation.Job[x][4] == -1):
if (Functions.check_availability(
y % initiation.drone_counter, t, t +
Functions.g(x, y % initiation.drone_counter) +
Functions.r(x, y % initiation.drone_counter) +
time.astype(int)) == 1):
temp = method.max(self, t,
initiation.Job[x][5].astype(int))
as_time.insert(
(x, temp,
Functions.g(x, y % initiation.drone_counter) +
Functions.r(x, y % initiation.drone_counter) +
time.astype(int)))
break
# print(as_time)
tem = as_time.delete_dif()
#j,t=tem
time = initiation.Job[tem[0]][3]
method.assign_operation(self, tem[0], y % initiation.drone_counter,
tem[1])
#print(y%initiation.drone_counter ,tem[0] ,tem[1],tem[1]+Functions.g(tem[0],y%initiation.drone_counter)+Functions.r(tem[0],y%initiation.drone_counter)+time.astype(int))
total_waiting = 0
for i in range(initiation.job_counter):
total_waiting += initiation.Job[i][4] - initiation.Job[i][5]
if (initiation.Job[i][4] == -1):
total_waiting = 10000000
break
print("Result For My New Method",
total_waiting / initiation.job_counter)
#print(initiation.Job)
#print(initiation.result)
for i in range(initiation.job_counter):
initiation.Job[i][4] = -1
initiation.Drone = np.zeros(
(initiation.drone_counter, 20 * initiation.max_time.astype(int)))
initiation.result = np.zeros(
(initiation.job_counter, initiation.drone_counter))
return 0
def assign_operation(self, job, t):
time = initiation.Job[job][3]
for y in range(initiation.drone_counter):
if (Functions.check_availability(
y, t, t + Functions.g(job, y) + Functions.r(job, y) +
time.astype(int)) == 1):
initiation.result[job][y] = 1
initiation.Job[job][4] = t + Functions.g(job, y)
#print(y ,job ,t,Functions.g(job,y),Functions.r(job,y),time.astype(int))
for x in range(
time.astype(int) + Functions.g(job, y) +
Functions.r(job, y)):
initiation.Drone[y][x + t] = 1
return 1
return 0
def assign(self):
number_feature = 1
batch_size = initiation.job_counter * initiation.drone_counter * 200
agent = DQN.DQNAgent(initiation.job_counter, number_feature)
reward = 0
best = 1000
for m in range(EPISODES):
max_time = 0
for i in range(initiation.job_counter):
max_time += initiation.Job[i][3]
initiation.Drone = np.zeros(
(initiation.drone_counter, 20 * max_time.astype(int)))
for i in range(initiation.job_counter):
initiation.Job[i][4] = -1
initiation.Drone = np.zeros(
(initiation.drone_counter, 20 * max_time.astype(int)))
initiation.result = np.zeros(
(initiation.job_counter, initiation.drone_counter))
done = False
total_time = 0
arr = [0]
state = np.array(arr * initiation.job_counter)
state = [
np.reshape(state[i], (
1,
1,
)) for i in range(initiation.job_counter)
] ## state for jobs 0(wait) or 1(assigned)
action_list = [] ## list to predict job
total_time = 0 ## total time of waiting time of job and going + returning time of drone
avarage_wtime = 0
assigned_job = 0
for i in range(
1000
): # because of the random assign, there should be more iterations # REVIEW: should find better way
action = agent.act(state) # act function decides to the action
if (initiation.Job[action][4].astype(int) == -1
): # if the selected job is not assigned before
time = initiation.Job[action][3]
work_time = Functions.g(
action, i % initiation.drone_counter) + Functions.r(
action,
i % initiation.drone_counter) + time.astype(int)
state[[
action
][0]] = 1 ## assigned the "action" th cell of state as 1
state = [
np.reshape(state[k], (
1,
1,
)) for k in range(initiation.job_counter)
] # reshape state suitable for act function array of arrays
for j in range(initiation.Job[action][5].astype(int),
10 * initiation.max_time.astype(int)):
if (
Functions.check_availability(
i % initiation.drone_counter, j,
j + work_time) == 1
): ##check_availability of the drone to "action"th job at first possible time
initiation.Job[action][4] = j
break
method.assign_operation(
self, action, i % initiation.drone_counter,
initiation.Job[action][4].astype(int)) ## assign
#print(i%initiation.drone_counter ,action ,initiation.Job[action][4].astype(int)-Functions.g(action,i%initiation.drone_counter),initiation.Job[action][4].astype(int)+work_time-Functions.g(action,i%initiation.drone_counter))
total_time = initiation.Job[action][4] - initiation.Job[
action][5]
assigned_job = assigned_job + 1
avarage_wtime = (avarage_wtime + total_time) / assigned_job
reward = reward + (
avarage_wtime - total_time
) * 500 ## reward if given according to total time; if the current total time is smaller than the previous reward is bigger
agent.remember(state, action, reward, state,
done) ## save the state to agent
action_list.append(action) #append action to list
done = True
else:
reward = reward - 100 # punishment to select already selected job
agent.remember(state, action, reward, state,
done) ## save the state to agent
done = True
if len(agent.memory) > batch_size:
agent.replay(batch_size)
total_waiting = 0
for i in range(initiation.job_counter):
if (initiation.Job[i][4] == -1):
total_waiting = 1000
break
total_waiting += initiation.Job[i][4] - initiation.Job[i][
5] ## calculate the waiting time / i tried to minimize that
#print(initiation.Job)
#print(initiation.result)
for n in range(initiation.job_counter):
initiation.Job[n][4] = -1
initiation.Drone = np.zeros((initiation.drone_counter,
20 * initiation.max_time.astype(int)))
initiation.result = np.zeros(
(initiation.job_counter, initiation.drone_counter))
flag = 0
test = total_waiting / initiation.job_counter
if (best > test):
best = test
print(test)
print(best)
return 0
