Intelligent logistics distribution and delivery based on discrete particle swarm optimization algorithm

Abstract

The invention discloses intelligent logistics distribution and delivery based on a discrete particle swarm optimization algorithm and aims at scheduling the path of a transportation vehicle so as to save the transportation cost. A coding mode and an operator based on the set and the probability are introduced on the basis of a framework of a standard particle swarm optimization algorithm, the particle swarm optimization algorithm which is originally suitable for a continuous space can be introduced into a discrete combined optimized space, so that the problem that the path of the vehicle is scheduled is solved and the advantages of high operation efficiency, strong optimization capacity, strong robustness, and the like endowed by the traditional particle swarm algorithm can be maintained. In addition, by using heuristic information to construct the positions of particles and introduce a local operator search, features of the problem and information contained in the data are utilized, and thereby the solving result of the algorithm is further enhanced. By adopting normalization weighting and decision idea to deal with the target, the transportation path is strived to be shortest while the number of transportation vehicles are required to be minimum, therefore, the transportation cost of logistics distribution and delivery businessmen can be reduced to the maximum extent.

Description

Technical field: [0001] The invention relates to two major fields of intelligent computing and logistics distribution, and mainly uses a discretized particle swarm optimization algorithm based on set and probability to schedule and optimize the path of transport vehicles in logistics distribution. Background technique: [0002] Vehicle route scheduling is an important content in the research of logistics distribution. The research goal of this problem is to design an appropriate route for a series of customer demand outlets, so that vehicles can pass through in an orderly manner. optimization goal. The constraints are generally: cargo demand, delivery volume, delivery time, vehicle capacity limit, mileage limit, time limit, etc. The optimization goals are generally: the shortest mileage, the least cost, as little time as possible, the fleet size as small as possible, High vehicle utilization. Vehicle routing scheduling contains the classic NP-hard combinatorial optimizatio...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the problems of the existing calculation methods in terms of insufficient calculation rate, poor scheduling quality, and unsuitability for large-scale logistics distribution, the present invention proposes a discrete particle swarm optimization that can efficiently schedule and route transport vehicles Algorithms, using intelligent calculation methods, minimize the number of vehicles required for transportation, and at the same time strive for the shortest transportation path, so as to minimize the transportation cost of logistics distributors

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