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Research On Logistics Path Optimization Based On Improved Discrete Particle Swarm Optimization Algorithm

Posted on:2019-07-28Degree:MasterType:Thesis
Country:ChinaCandidate:G L ShenFull Text:PDF
GTID:2428330563495866Subject:Control engineering
Abstract/Summary:PDF Full Text Request
With the rapid development of China's economy,the logistics industry has an important position in society and life.The logistics cost is an important factor affecting the development of the logistics industry.The simplest and most effective way to reduce costs without high investment is to optimize the logistics transportation path.Optimizing the distance of the logistics transportation path to the shortest can not only reduce the logistics cost,but also improve the transportation efficiency.Therefore,this paper proposes an improved discrete particle swarm algorithm(2SGPSO)to solve the logistics transportation path problem.In this paper,the logistics transportation path problem and the 2SGPSO algorithm are studied:(1)This paper proposes the idealized traditional logistics transportation path problem and combines the actual modern logistics transportation path problem,and establishes the corresponding mathematical model respectively.In the process of establishing a mathematical model for the modern logistics transportation path problem,considering whether the roads between the distribution sites are directly connected and whether the traffic is congested,the road connection situation ensures that the logistics can be smoothly distributed by establishing a road turning point between the two sites,and the traffic congestion situation is expressed using the congestion coefficient,and the actual delivery time is obtained based on the congestion coefficient,the path distance and the traveling speed.Based on this,a mathematical model of modern logistics transportation path problem is established by combining time factor and path distance factor.(2)The 2SGPSO algorithm firstly adds genetic algorithm(GA)selection crossover and mutation operation methods to the discrete particle swarm algorithm(DPSO)to obtain the GPSO algorithm.In the iterative search process,the logistics distribution site is coded and the site sequence is obtained,then the site sequence is selected and crossed with the current optimal site sequence and the group optimal site sequence respectively,and the convergence speed is accelerated.The mutation operation is carried out after the selection of cross operation,and two locations are randomly generated in each distribution site sequence to mutate,which improves the global search capability of the algorithm.Because the GPSO algorithm is easy to fall into the local optimal,in order to avoid the occurrence of this situation,the 2SGPSO algorithm is proposed,that is,the 2-opt search strategy is added to the simulated annealing algorithm(SA)to further optimize the path solution.The distribution site sequence and path solution generated by the GPSO algorithm are the initial values of the 2SA algorithm,and the initial values are calculated by using the 2-opt search strategy and metropolis criterion in the 2SA algorithm,which improves the local optimal condition of the GSPO algorithm and improves the quality of the solution.In this paper,2SGPSO algorithm is used to solve the path optimization problem in logistics transportation,the eil51 city data set,pr136 urban data set and Shenzhen Songyuan Road regional data set are compared and tested respectively.And the test results show that the2 SGPSO algorithm has the shortest path distance compared with other algorithms and has the best effect,which reduces the logistics cost and promotes the development of the logistics industry.
Keywords/Search Tags:discrete particle swarm optimization, the problem of logistics transportation path, genetic algorithm, simulated annealing algorithm, 2-optimization algorithm
PDF Full Text Request
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