Commercial organisations commonly use operational research tools to solve vehicle routing problems. This practice is less commonplace in charity and voluntary organisations. In this paper, we provide an elementary app...Commercial organisations commonly use operational research tools to solve vehicle routing problems. This practice is less commonplace in charity and voluntary organisations. In this paper, we provide an elementary approach for solving the Vehicle Routing Problem (VRP) that we believe can be easily implemented in these types of organisations. The proposed model leverages mixed integer linear programming to optimize the pickup sequence of all customers, each with distinct time windows and locations, transporting them to a final destination using a fleet of vehicles. To ensure ease of implementation, the model utilises Python, a user-friendly programming language, and integrates with the Google Maps API, which simplifies data input by eliminating the need for manual entry of travel times between locations. Troubleshooting methods are incorporated into the model design to ensure easy debugging of the model’s infeasibilities. Additionally, a computation time analysis is conducted to evaluate the efficiency of the code. A node partitioning approach is also discussed, which aims to reduce computational times, especially when handling larger datasets, ensuring this model is realistic and practical for real-world application. By implementing this optimized routing strategy, logistics companies or organisations can expect significant improvements in their day-to-day operations, with minimal computational cost or need for specialised expertise. This includes reduced travel times, minimized fuel consumption, and thus lower operational costs, while ensuring punctuality and meeting the demands of all passengers.展开更多
文摘Commercial organisations commonly use operational research tools to solve vehicle routing problems. This practice is less commonplace in charity and voluntary organisations. In this paper, we provide an elementary approach for solving the Vehicle Routing Problem (VRP) that we believe can be easily implemented in these types of organisations. The proposed model leverages mixed integer linear programming to optimize the pickup sequence of all customers, each with distinct time windows and locations, transporting them to a final destination using a fleet of vehicles. To ensure ease of implementation, the model utilises Python, a user-friendly programming language, and integrates with the Google Maps API, which simplifies data input by eliminating the need for manual entry of travel times between locations. Troubleshooting methods are incorporated into the model design to ensure easy debugging of the model’s infeasibilities. Additionally, a computation time analysis is conducted to evaluate the efficiency of the code. A node partitioning approach is also discussed, which aims to reduce computational times, especially when handling larger datasets, ensuring this model is realistic and practical for real-world application. By implementing this optimized routing strategy, logistics companies or organisations can expect significant improvements in their day-to-day operations, with minimal computational cost or need for specialised expertise. This includes reduced travel times, minimized fuel consumption, and thus lower operational costs, while ensuring punctuality and meeting the demands of all passengers.
