Multi-Objective Optimization System For Less-Than-Truck-Cargo Matching

In the traditional logistics industry,there are problems such as waste of transportation resources and untimely update of logistics information.With the continuous development of e-commerce industry,the total amount of freight transportation increases year by year,and the waste of resources is becoming increasingly serious.The volume and weight of less-than-truckload goods do not meet the conditions of vehicle transportation,and they need to be transported in the form of carpooling,which has low distribution efficiency.In order to save distribution resources and improve transportation efficiency,the problem of less-than-truckload vehicle-cargo matching has gradually become an important research direction in the logistics industry.This paper first introduces the relevant technologies involved in the LTL truck-cargo matching problem,and summarizes the current research status.Then,considering the characteristics of actual LTL vehicle-cargo matching,a multi-objective optimization system for LTL-vehicle-cargo matching is preliminarily designed.Aiming at the problems of information asymmetry in less-than-truckload logistics and the time-consuming waiting for the goods to fill up the whole vehicle,this paper introduces the reservation mechanism and proposes a multi-objective optimization model RM-LTLM for less-than-truckload vehicle-cargo matching.The goods that will arrive at the distribution center in the future are added to the vehicle-cargo matching at the current stage,and a reasonable vehiclecargo matching plan is planned.At the same time,the model considers the cancellation of the reservation form and the change of the reservation time that may occur under the reservation mechanism,and designs a dynamic cargo matching method for emergencies.In this paper,artificial hummingbird algorithm is chosen to solve RM-LTLM.The artificial hummingbird algorithm is analyzed and optimized from three aspects: the generation method of the initial solution,the adaptive algorithm weight,and the generation method of the next generation solution,so that it can better adapt to the model of the matching problem of less-than-truckload trucks and goods,and can plan a reasonable truck.Cargo matching and vehicle scheduling scheme.The performance of the improved artificial hummingbird algorithm and the effectiveness of RM-LTLM were verified through simulation and comparative experiments.Finally,according to the system architecture of the less-than-truckload vehicle-cargo matching system and the less-than-truckload vehicle-cargo matching model designed in this paper,a multiobjective optimization system for less-than-truckload vehicle-cargo matching is developed.After testing,the system has perfect functions and excellent matching effect,which provides technical support for the matching of less-than-truckload trucks and goods in the distribution center.