def simulation_run_v2(num_demand_nodes,num_nurses,time_horizon,arrival_rate,fixed_service_time=True, shifts=False, restrictions=False):
"""This function runs one simulation trial using the new routing function.
Returns the lists of nurses and nodes generated for visualization purposes.
Demand nodes are fake and randomly generated
The user can decide if the nurses should have restrictions or shifts based on boolean inputs.
Asks for user input to decide other parameters."""
# generate demand nodes, customers and nurses
node = False
demand_node_list = randomly_generate_uniform_demand_nodes(num_demand_nodes, arrival_rate)
distance_matrix = distance_between_fake_nodes(demand_node_list)
customer_list = generate_customers(demand_node_list, time_horizon, fixed_service_time)
if shifts is True:
shift_lower_bound = input('Input the minimum shift length: ')
shift_upper_bound = input('Input the maximum shift length: ')
else:
shift_lower_bound = 0
shift_upper_bound = time_horizon
if restrictions is True:
# build probabilities dictionary
probabilities = {}
for node in demand_node_list:
print("Latitude: " % s) % node.lat
print("Longitude: " % s) % node.lon
prob = input('Input the probability of a nurse being restricted from this node: ')
probabilites[node.id_number] = prob
nurse_list = generate_nurses_with_restrictions(probabilities, node_list,num_nurses, time_horizon,
shift_lower_bound, shift_upper_bound, node)
else:
nurse_list = generate_nurses(num_nurses, num_demand_nodes, demand_node_list, time_horizon, shift_lower_bound, shift_upper_bound)
# customers are served in the order they arrive (at the moment)
current_time = 0
for customer in customer_list:
# next event dispatch after a customer arrives and at least one nurse is available
current_time = max(current_time, customer.arrival_time)
# choose which nurse to dispatch
nurse_to_dispatch, dispatch_time, nurses_working = updated_dispatch_nurse(nurse_list, customer,
current_time, distance_matrix,
fixed_service_time,
time_horizon)
# serve customer and update metrics
current_time = updated_serve_customer(nurse_to_dispatch, customer, distance_matrix, dispatch_time,
nurses_working)
# report on simulation
time_varying_system_metrics(nurse_list, customer_list, fixed_service_time, time_horizon)
aggregate_system_metrics(nurse_list, customer_list)
return nurse_list, demand_node_list
def simulation_run_real_nodes_part_two(num_demand_nodes,num_nurses,demand_node_list,time_horizon,file_name, customer_type, fixed_service_time=True, shifts=False, restrictions=False):
"""Second part of simulation version 4. Uses a CSV of travel time data downloaded from part one."""
node = True
distance_matrix = distance_between_nodes_csv(demand_node_list, file_name)
if customer_type == "random" or "random from data":
customer_list = generate_customers(demand_node_list, time_horizon, fixed_service_time)
elif customer_type == "actual":
start = input("Input a start time (hour in military time): ")
stop = start + (time_horizon / 60)
customer_list = generate_customers_from_data(node_list, fixed_service_time, start, stop)
if shifts is True:
shift_lower_bound = input('Input the minimum shift length: ')
shift_upper_bound = input('Input the maximum shift length: ')
else:
shift_lower_bound = 0
shift_upper_bound = time_horizon
if restrictions is True:
# build probabilities dictionary
probabilities = {}
for node in demand_node_list:
print("Latitude: " % s) % node.lat
print("Longitude: " % s) % node.lon
prob = input('Input the probability of a nurse being restricted from this node: ')
probabilites[node.id_number] = prob
nurse_list = generate_nurses_with_restrictions(probabilities, node_list, num_nurses, time_horizon,
shift_lower_bound, shift_upper_bound, node)
else:
nurse_list = generate_nurses(num_nurses, num_demand_nodes, demand_node_list, time_horizon, shift_lower_bound, shift_upper_bound,node)
# customers are served in the order they arrive (at the moment)
current_time = 0
for customer in customer_list:
# next event dispatch after a customer arrives and at least one nurse is available
current_time = max(current_time, customer.arrival_time)
# choose which nurse to dispatch
nurse_to_dispatch, dispatch_time, nurses_working = updated_dispatch_nurse(nurse_list, customer,
current_time, distance_matrix,
fixed_service_time,
time_horizon)
# serve customer and update metrics
current_time = updated_serve_customer(nurse_to_dispatch, customer, distance_matrix, dispatch_time,
nurses_working)
# report on simulation
time_varying_system_metrics(nurse_list, customer_list, fixed_service_time, time_horizon)
aggregate_system_metrics(nurse_list, customer_list)
return nurse_list, demand_node_list
def simulation_run_v4(num_demand_nodes,num_nurses,time_horizon, locations, fixed_service_time=True, shifts=False, restrictions=False):
"""This function runs one simulation trial using the new routing function.
Returns the lists of nurses and nodes generated for visualization purposes.
Demand nodes are generated from GPS coordinates.
The user can decide if the nurses should have restrictions or shifts based on boolean inputs.
Asks for user input to decide other parameters.
Fetches travel times from an API."""
# generate demand nodes, customers, and nurses
node = True
node_type = input('Input "actual" for nodes to be actual locations, "random" for nodes to be randomly generated locations: ')
customer_type = input('Input "random" for random arrival rate, "rate from data", or "actual" for actual data: ')
if node_type == "actual":
demand_node_list = generate_demand_nodes_from_data(locations, customer_type)
elif node_type == "random":
radius = float(input("Input a radius for the demand nodes: "))
demand_node_list = generate_demand_nodes_in_zip(locations, num_demand_nodes, radius, customer_type)
else:
raise InputError
file_name = write_file(demand_node_list,demand_node_list)
distance_matrix = distance_between_nodes_api(demand_node_list)
if customer_type == "random" or "random from data":
customer_list = generate_customers(demand_node_list, time_horizon, fixed_service_time)
elif customer_type == "actual":
start = input("Input a start time (hour in military time): ")
stop = start+(time_horizon/60)
customer_list = generate_customers_from_data(node_list, fixed_service_time, start, stop)
day = input("Input a number 0-6 corresponding to a day Sun-Sat: ")
customer_list = customer_list[day]
if shifts is True:
shift_lower_bound = input('Input the minimum shift length: ')
shift_upper_bound = input('Input the maximum shift length: ')
else:
shift_lower_bound = 0
shift_upper_bound = time_horizon
if restrictions is True:
# build probabilities dictionary
probabilities = {}
for node in demand_node_list:
print("Latitude: " % s) % node.lat
print("Longitude: " % s) % node.lon
prob = input('Input the probability of a nurse being restricted from this node: ')
probabilites[node.id_number] = prob
nurse_list = generate_nurses_with_restrictions(probabilities, node_list,num_nurses, time_horizon,
shift_lower_bound, shift_upper_bound, node)
else:
nurse_list = generate_nurses(num_nurses, num_demand_nodes, demand_node_list, time_horizon, shift_lower_bound, shift_upper_bound,node)
# customers are served in the order they arrive (at the moment)
current_time = 0
for customer in customer_list:
# next event dispatch after a customer arrives and at least one nurse is available
current_time = max(current_time, customer.arrival_time)
# choose which nurse to dispatch
nurse_to_dispatch, dispatch_time, nurses_working = updated_dispatch_nurse(nurse_list, customer,
current_time, distance_matrix,
fixed_service_time,
time_horizon)
# serve customer and update metrics
current_time = updated_serve_customer(nurse_to_dispatch, customer, distance_matrix, dispatch_time,
nurses_working)
# report on simulation
time_varying_system_metrics(nurse_list, customer_list, fixed_service_time, time_horizon)
aggregate_system_metrics(nurse_list, customer_list)
return nurse_list, demand_node_list