Difference between revisions of "Simulation of product distribution"

From Simulace.info
Jump to: navigation, search
(Sources)
(Sources)
Line 27: Line 27:
 
*https://www.holiday-weather.com/london/averages/
 
*https://www.holiday-weather.com/london/averages/
 
*https://www.quora.com/How-common-are-thunderstorms-in-London
 
*https://www.quora.com/How-common-are-thunderstorms-in-London
 +
*https://weatherspark.com/y/45061/Average-Weather-in-City-of-London-United-Kingdom-Year-Round
 
*https://content.tfl.gov.uk/travel-in-london-2023-road-traffic-trends-acc.pdf
 
*https://content.tfl.gov.uk/travel-in-london-2023-road-traffic-trends-acc.pdf
 
*https://ccl.northwestern.edu/netlogo/docs/programming.html
 
*https://ccl.northwestern.edu/netlogo/docs/programming.html
 
*https://logistiko.eu/help/goods-delivery-procedure
 
*https://logistiko.eu/help/goods-delivery-procedure

Revision as of 09:30, 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

Results

Conclusion

Code

Sources