Difference between revisions of "Simulation of product distribution"

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The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.
 
The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.
  
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The core of the model is the distribution process, where vehicles collect packages from storage facilities and deliver them to designated stores or direct to customers
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The core of the model is the distribution process, where vehicles collect packages from storage facilities and deliver them to designated stores or direct to customers.
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'''External Factors'''
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External factors like weather conditions and traffic congestion are integrated into the model. The model allows for the customization of these variables to simulate different scenarios.
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'''End of the Simulation'''
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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=

Revision as of 10:49, 21 January 2024

Title: Simulation of product distribution

Author: Lucia Pavlíková

Method: Agent-based model

Tool: NetLogo

Problem definition

This project explores the optimization of delivery times for distribution vehicles in a city (London) with a complex and challenging logistics environment. This study focuses on key variables that impact delivery efficiency, such as traffic congestion, weather conditions, variability in package handling, and scenarios of unavailability of recipients. The aim is to provide insights and strategies for businesses to enhance their urban distribution operations, making this research relevant for both academic and practical applications in urban logistics.

Method

Simulation - delivery vehicle in the middle, next to the storage facility.

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

Model

The model for this project simulates the distribution process in London using an agent-based approach in NetLogo. It begins with a map of the world - onto which various distribution elements, such as vehicles, storage facilities, and delivery locations, are placed.

Environment

The simulation takes place within the city. Delivery locations are randomly generated within the simulation, each assigned a position in a Cartesian coordinate system. Each household is designated to receive one package. Once the final package is delivered, the vehicle returns to the storage facility, using the button “go to storage”.

Agents

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.

Movement

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.

Distribution Process

The model for this project simulates the distribution process in a city (London) using an agent-based approach in NetLogo. In this model, agents represent different elements of the distribution network (delivery vehicle, customers, storage facility). External factors include weather conditions or customers availability.

The core of the model is the distribution process, where vehicles collect packages from storage facilities and deliver them to designated stores or direct to customers.

External Factors

External factors like weather conditions and traffic congestion are integrated into the model. The model allows for the customization of these variables to simulate different scenarios.

End of the Simulation

The simulation concludes when all scheduled deliveries are completed. The model provides data on delivery costs and potential bottlenecks in the distribution process.

Results

Conclusion

Code

Sources

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