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utils/batch/simulation_batch.py

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+from utils.batch.mapping_batch import *
+from utils.cluster.mapping_cluster import *
+from utils.routing.routes import *
+
+def simulation_wave(y_low, y_high, origin_loc, orders_number, df_orderlines, list_wid, list_dst, list_route, list_ord):
+	''' Simulate of total picking distance with n orders per wave'''
+	distance_route = 0 
+	# Create waves
+	df_orderlines, waves_number = orderlines_mapping(df_orderlines, orders_number)
+	for wave_id in range(waves_number):
+		# Listing of all locations for this wave 
+		list_locs, n_locs, n_lines, n_pcs = locations_listing(df_orderlines, wave_id)
+		# Results
+		wave_distance, list_chemin = create_picking_route(origin_loc, list_locs, y_low, y_high)
+		distance_route = distance_route + wave_distance
+		list_wid.append(wave_id)
+		list_dst.append(wave_distance)
+		list_route.append(list_chemin)
+		list_ord.append(orders_number)
+	return list_wid, list_dst, list_route, list_ord, distance_route
+
+def simulate_batch(n1, n2, y_low, y_high, origin_loc, orders_number, df_orderlines):
+	''' Loop with several scenarios of n orders per wave'''
+	# Lists for results
+	list_wid, list_dst, list_route, list_ord = [], [], [], []
+	# Test several values of orders per wave
+	for orders_number in range(n1, n2 + 1):
+		list_wid, list_dst, list_route, list_ord, distance_route = simulation_wave(y_low, y_high, origin_loc, orders_number, 
+		df_orderlines, list_wid, list_dst, list_route, list_ord)
+		print("Total distance covered for {} orders/wave: {:,} m".format(orders_number, distance_route))
+
+	# By Wave
+	df_waves = pd.DataFrame({'wave': list_wid,
+				'distance': list_dst,
+				'routes': list_route,
+				'order_per_wave': list_ord})
+
+	# Results aggregate
+	df_results = pd.DataFrame(df_waves.groupby(['order_per_wave'])['distance'].sum())
+	df_results.columns = ['distance']
+	return df_waves, df_results.reset_index()