Abstract
Accelerating product flow, improving service level, lowering logistics costs, reducing the possibility of product losses in circulation, and thus optimizing the logistics distribution system are the issues that enterprise managers should consider in logistics distribution. Traditional algorithms can only solve simple problems, while intelligent algorithms can solve the most complex combinatorial optimization problems. The optimization problem of logistics vehicle scheduling path with different constraints is studied in this paper using the SVM algorithm, and the improved algorithm is simulated to verify its effectiveness. The simulation results show that the logistics distribution path optimization method based on the SVM algorithm has good global searching ability, effectively avoids the algorithm falling into local optimum, and reduces total distribution cost, proving the algorithm’s effectiveness. This scheme can optimize vehicle routes, increase distribution efficiency, and reduce logistics costs, and it can be used in a wide range of logistics distribution route optimization applications.
Highlights
Traditional logistics and distribution methods are inefficient, but they have high distribution costs, and meeting people’s demands for fast and efficient distribution is difficult [2]. e physical displacement of transporting goods to customers by vehicles and other modes of transportation at a specified time is the central task of logistics distribution [3]. e upstream suppliers in a logistics system can be factories, for example, and the downstream users can include wholesalers and retailers
A vehicle routing optimization model based on Support Vector Machine (SVM) algorithm is established by imposing constraints on standard Vehicle Routing Problem (VRP), in order to shorten the solution time and improve the solution quality, and the correctness and effectiveness of the algorithm are analyzed by simulation results
Full-load type means that the customer’s demand is greater than or equal to the cargo capacity of the vehicle, and one or more vehicles need to cooperate to complete a task, and the vehicle is in a full-load state during the distribution process. e problem model of the two situations mentioned above is the mixed problem of nonfull load and full load. e transportation capacity of vehicles can only meet the needs of some customers, so some vehicles are not full load while others are full load, and the two states exist at the same time., VRP is a complex combinatorial optimization problem with many components and types which is difficult to solve
Summary
With the development of market globalization and economic informatization, international trade exchanges are becoming increasingly prosperous and market competition is becoming increasingly fierce. Information-based logistics technology can help logistics enterprises make decisions, shortening the distribution time, improving the distribution efficiency, and reducing the transportation cost. Diversified customer demand, traffic and transportation road conditions, and other factors all influence logistics and distribution vehicle route optimization [12]. Literature [26] studies the common Vehicle Routing Problems in road freight transportation, especially those related to reverse logistics, considering the fuel consumption and carbon emission costs in the model. A vehicle routing optimization model based on SVM algorithm is established by imposing constraints on standard VRP, in order to shorten the solution time and improve the solution quality, and the correctness and effectiveness of the algorithm are analyzed by simulation results
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