Vehicle Routing Optimization System with Smart Geopositioning Updates

Abstract

Solving the vehicle routing problem (VRP) is one of the best-known optimization issues in the TLS (transport, logistic, spedition) branch market. Various variants of the VRP problem have been presented and discussed in the literature for many years. In most cases, batch versions of the problem are considered, wherein the complete data, including customers’ geographical distribution, is well known. In real-life situations, the data change dynamically, which influences the decisions made by optimization systems. The article focuses on the aspect of geopositioning updates and their impact on the effectiveness of optimization algorithms. Such updates affect the distance matrix, one of the critical datasets used to optimize the VRP problem. A demonstration version of the optimization system was developed, wherein updates are carried out in integration with both open source routing machine and GPS tracking services. In the case of a dynamically changing list of destinations, continuous and effective updates are required. Firstly, temporary values of the distance matrix based on the correction of the quasi-Euclidean distance were generated. Next, the impact of update progress on the proposed optimization algorithms was investigated. The simulation results were compared with the results obtained “manually” by experienced planners. It was found that the upload level of the distance matrix influences the optimization effectiveness in a non-deterministic way. It was concluded that updating data should start from the smallest values in the distance matrix.

Route Planning for Chain Restaurants With Improved Delivery Mode Using an Adaptive Genetic Algorithm

“…Asghar [21] combined gravity simulation based local search (gels) and GA to solve the capacitive vehicle distribution problem (CVRP). Belka [22] optimized the VRP Problem by studying the impact of updating the customer's geographical location on the distance matrix. Xia Xiaoyun [23] proposed a hybrid algorithm named dsmo-ga to solve the vehicle routing problem (VRPSD) in which customer requirements follow a known probability distribution.…”