Towards Sustainable Food Logistics : An Open-Source Web Application for Optimizing Transportation Scheduling and Vehicle Routing
Mahmud, Riaz (2024-08-09)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2024080964360
https://urn.fi/URN:NBN:fi-fe2024080964360
Tiivistelmä
The logistics industry faces significant challenges in optimizing transportation scheduling and vehicle routing, particularly in reducing operational costs, maintaining efficiency, and minimizing environmental impact. This thesis presents RouteShaper, a web-based tool designed to address these challenges, specifically within the context of Honkajoki Oy, a leading animal by-product processor in Finland. This application uses the VRP CLI Python package as the optimization engine, which is built on top of heuristic algorithms. Other key technologies employed in this research include the Python-based Django framework for backend development and the JavaScript-based React framework for frontend development.
The transportation operation at Honkajoki Oy is defined using a Multi-Depot Vehicle Routing Problem with Time Window model, and a novel parameter, Freshness Penalty, is introduced to measure the impact of delay on product quality during transit. Using data analytics and an intuitive reporting dashboard, the RouteShaper tool empowers logistics managers and stakeholders with tangible insights to enhance their operational efficiency. The practical implementation of this tool at Honkajoki Oy shows substantial improvement in reducing costs and traveling distance by 16.27% compared to manual scheduling. The result further shows a 26.91% reduction in overall traveling time. These enhancements directly contribute to efficient use of resources and reduced emissions for a positive environmental impact, particularly when there are a large number of delivery or pickup tasks involved.
This research contributes to the academic and practical fields by demonstrating the effectiveness of integrating open-source technologies, advanced optimization algorithms, and data analytics in logistics management. It underscores the importance of technological innovation in achieving operational efficiency and sustainability. Future work will focus on developing a new variant of the vehicle routing problem incorporating the Freshness Penalty constraint and exploring broader applicability across different industries.
The transportation operation at Honkajoki Oy is defined using a Multi-Depot Vehicle Routing Problem with Time Window model, and a novel parameter, Freshness Penalty, is introduced to measure the impact of delay on product quality during transit. Using data analytics and an intuitive reporting dashboard, the RouteShaper tool empowers logistics managers and stakeholders with tangible insights to enhance their operational efficiency. The practical implementation of this tool at Honkajoki Oy shows substantial improvement in reducing costs and traveling distance by 16.27% compared to manual scheduling. The result further shows a 26.91% reduction in overall traveling time. These enhancements directly contribute to efficient use of resources and reduced emissions for a positive environmental impact, particularly when there are a large number of delivery or pickup tasks involved.
This research contributes to the academic and practical fields by demonstrating the effectiveness of integrating open-source technologies, advanced optimization algorithms, and data analytics in logistics management. It underscores the importance of technological innovation in achieving operational efficiency and sustainability. Future work will focus on developing a new variant of the vehicle routing problem incorporating the Freshness Penalty constraint and exploring broader applicability across different industries.