Simulation of product distribution - Revision history

Pavl08: /* Code */

2024-01-21T19:47:00Z

‎Code

Pavl08: /* Results and conclusion */

2024-01-21T19:43:26Z

‎Results and conclusion

Pavl08: /* Results and conclusion */

2024-01-21T19:43:14Z

‎Results and conclusion

Pavl08: /* Results and conclusion */

2024-01-21T19:42:43Z

‎Results and conclusion

Pavl08: /* Results and conclusion */

2024-01-21T19:29:46Z

‎Results and conclusion

Pavl08 at 10:38, 21 January 2024

2024-01-21T10:38:52Z

Pavl08: /* Model */

2024-01-21T10:38:06Z

‎Model

Pavl08: /* Model */

2024-01-21T10:24:48Z

‎Model

Pavl08: /* Model */

2024-01-21T10:17:21Z

‎Model

Pavl08: /* Model */

2024-01-21T10:16:52Z

‎Model

← Older revision		Revision as of 19:47, 21 January 2024
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	=Code=		=Code=
		+	[[File:Seminarna praca Pavlikova.nlogo]]

	=Sources=		=Sources=

@@ Line 95: / Line 95: @@
 =Results and conclusion=
 '''Results'''
 The transport vehicle effectively navigated the city to make deliveries under various conditions. The user is able to control key factors such as weather, day of the week, time of the day, and traffic intensity to observe the impact on the vehicle’s performance. The simulation records the number of customers that still need to be served, also how many times the vehicle had to return to the storage facility to finish all the deliveries. The recorded data provided insights into how external factors influenced the operational pattern of the transport vehicle.
 '''Conclusion'''
 This simulation successfully modeled the dynamic environment in which a transport vehicle is making deliveries in the city. The results demonstrated that external factors (weather, day of the week, time and traffic) had effects on the delivery process and the delivery was slower. The needed returns to the storage facility to fill the car again was also influenced by these factors, making the time even longer - showing that real time adjustments, optimizing the shortest way possible and schedules are significant for an effective delivery.

@@ Line 94: / Line 94: @@
 =Results and conclusion=
-'''Results:''' The transport vehicle effectively navigated the city to make deliveries under various conditions. The user is able to control key factors such as weather, day of the week, time of the day, and traffic intensity to observe the impact on the vehicle’s performance. The simulation records the number of customers that still need to be served, also how many times the vehicle had to return to the storage facility to finish all the deliveries. The recorded data provided insights into how external factors influenced the operational pattern of the transport vehicle.
+'''Results'''
-'''Conclusion:''' This simulation successfully modeled the dynamic environment in which a transport vehicle is making deliveries in the city. The results demonstrated that external factors (weather, day of the week, time and traffic) had effects on the delivery process and the delivery was slower. The needed returns to the storage facility to fill the car again was also influenced by these factors, making the time even longer - showing that real time adjustments, optimizing the shortest way possible and schedules are significant for an effective delivery.
+'''Conclusion'''
 This is a simplified version of a real world problem, and there could be more variables added, for example availability/unavailability of customers, time required to fill the car again in the storage facility, multiple vehicles and so on.

@@ Line 95: / Line 95: @@
 =Results and conclusion=
 '''Results:''' The transport vehicle effectively navigated the city to make deliveries under various conditions. The user is able to control key factors such as weather, day of the week, time of the day, and traffic intensity to observe the impact on the vehicle’s performance. The simulation records the number of customers that still need to be served, also how many times the vehicle had to return to the storage facility to finish all the deliveries. The recorded data provided insights into how external factors influenced the operational pattern of the transport vehicle.
 =Code=

← Older revision		Revision as of 19:29, 21 January 2024
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	=Results and conclusion=		=Results and conclusion=
		+	'''Results:''' The transport vehicle effectively navigated the city to make deliveries under various conditions. The user is able to control key factors such as weather, day of the week, time of the day, and traffic intensity to observe the impact on the vehicle’s performance. The simulation records the number of customers that still need to be served, also how many times the vehicle had to return to the storage facility to finish all the deliveries. The recorded data provided insights into how external factors influenced the operational pattern of the transport vehicle.

	=Code=		=Code=

@@ Line 93: / Line 93: @@
 The simulation concludes when all scheduled deliveries are completed. The model provides data on delivery costs and potential bottlenecks in the distribution process.
-=Results=
+=Results and conclusion=
 =Code=

← Older revision		Revision as of 10:38, 21 January 2024
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	The movement of vehicles is a crucial aspect of the model. Vehicles navigate through the city, contending with variables such as traffic congestion, day of the week, weather or time of the day. Their movement patterns are influenced by all of the variables.		The movement of vehicles is a crucial aspect of the model. Vehicles navigate through the city, contending with variables such as traffic congestion, day of the week, weather or time of the day. Their movement patterns are influenced by all of the variables.
		+
		+	the go function. First step is to identify the closest customer, then go to him. The vehicle has capacity 20 packages, so if the car is not empty, it delivers the package to the customer. Otherwise it goes to the storage to get more packages. If the delivery is finished, the functions stop.
		+	identify-closest
		+	make-step
		+
		+	fill-car
		+
		+	if (filled_car != 20) [deliver]
		+	if (finish_delivery = 1) [stop]

	'''Distribution Process'''		'''Distribution Process'''

← Older revision		Revision as of 10:24, 21 January 2024
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	In this model, agents represent different elements of the distribution network. Delivery vehicle is the primary agents. Storage facility and delivery locations are also represented, serving as starting and ending points for the distribution journey.		In this model, agents represent different elements of the distribution network. Delivery vehicle is the primary agents. Storage facility and delivery locations are also represented, serving as starting and ending points for the distribution journey.
		+
		+	This function sets up the town. It has a colour grey, bacause London is a pretty grey.
		+	ask patches [
		+	set pcolor grey
		+	]
		+
		+	This is set up for the delivery vehicle. It always starts the day in the storage.
		+	create-cars 1
		+	ask cars [
		+	setxy storagex storagey
		+	set size 2
		+	set shape "car"
		+	]
		+
		+	Here the storage is created - it has a shape of "pentagon", since it should be differenciated from the customers.
		+	create-storages 1
		+	ask storages [
		+	setxy storagex storagey
		+	set size 2
		+	set shape "pentagon"
		+	]
		+
		+	Cuustomers are created in random places and the number of them is set by user using slider before the simulation setup.
		+	create-customers number_of_customers
		+	ask customers [
		+	setxy random-xcor random-ycor
		+	set size 2
		+	set shape "house"
		+	]

	'''Movement'''		'''Movement'''

← Older revision		Revision as of 10:17, 21 January 2024
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	Both weather and day variables are chosen based on the data of transport in London and its average weather. Users can chose these variables based on a description below the "chooser" to make a more educated decision.		Both weather and day variables are chosen based on the data of transport in London and its average weather. Users can chose these variables based on a description below the "chooser" to make a more educated decision.
−

	'''Agents'''		'''Agents'''

@@ Line 26: / Line 26: @@
 User can decide about various variables:
-*'Number_of_customers:' how many customers will have a package delivered during the day.
+*'''Number_of_customers:''' how many customers will have a package delivered during the day.
-*'Time_of_day:' whether the delivery car drives in the morning, lunch or afternoon.
+*'''Time_of_day:''' whether the delivery car drives in the morning, lunch or afternoon.
-*'Day:' the user can chose what day of the week it is.
+*'''Day:''' the user can chose what day of the week it is.
-*'Weather:' for simplification there are 4 types of weather that influence how fast the delivery vehicle can move.
+*'''Weather:''' for simplification there are 4 types of weather that influence how fast the delivery vehicle can move.
 Both weather and day variables are chosen based on the data of transport in London and its average weather. Users can chose these variables based on a description below the "chooser" to make a more educated decision.