The Research And Implementation Of Logistics Address And Other Problems In Green Agricultural Trading And Services System

This paper describes the research projects undertaken by the author of"green agricultural trading system" site at logistic algorithm implementationand transport management module and sales distribution module implementation.With the rapid economic development, transportation management andservice industries continue to improve logistics management is reflected moreand more scientific management and decision-making, safety operations,organization and coordination and harmony, are constant in real terms to meetthe needs of the community management and services industry Innovate. Makefull use of modern logistics in the means of high-tech information technologyservices and management, but also have the necessary support to the corealgorithm with intelligent features, to ensure that their scientific andefficient, it is required by modern logistics of core technology, but alsothe logistics business institutions to enhance their competitiveness,scientific decision-making, resource efficient integration, rapid reactionand open up new space in the value-added services to support essential. Forenhancing the scientific regulation of industry management control, improvereaction speed and management efficiency, ability to deal with an emergencycase, to open up new service areas of great significance. The author assumedstation location problem is to examine the distribution to find the mosteffective path to the problem of goods, name, logistic parks, distributionnetwork rationalization and optimization problem. By location, cargo flowplanning optimization, it is reasonable to determine node distribution networklayout, allocation of supply and logistics resources, science and the deliverypoint into the coverage area and to identify the types of direct and appropriatedistribution of transport way. Authors refer relevant information on domesticand foreign research and experiments, application of genetic algorithms tosolve the problem of the station site. Analog Darwin are the genetic selectionand natural selection processes based on biological evolution model is anatural evolutionary process by simulating the optimal solution search methods, genetic algorithms are possible questions from the representative of thepotential solution set of a species (population) begin , and from a speciesgenetically (gene) encoding a certain number of individuals (individual)components. In fact each individual (chromosome) with the characteristics ofthe entity are chromosome as the main carrier of genetic material, that is,a collection of genes, the performance of its internal (eg., genotype) is acombination of genes, which determines the external shape of individualperformance, such as the characteristics of black hair are controlled by thischromosome One characteristic of a certain combination of genes decide.Therefore, the required implementation from the start one phenotype togenotype mapping work is encoded. Gene encoding modeled because of thecomplexity of the job, we tend to be simplified, such as binary code, the earlygeneration of species, after being elected, in accordance with the principleof survival of the fittest and survival of the fittest, the evolution of eachgeneration to produce more and more good approximate solution at eachgeneration, according to problem domain the size of the individual fitnessof individual selection and genetics through the use of natural geneticoperators of crossover and mutation combination to produce a new solution setof the representative of the species. This process will lead to the same speciesas the natural evolution of the younger generation than the previous generationof more species adapted to the environment, the last of the best individualspecies, after decoding, can be used as the approximate optimal solution ofproblem. The use of genetic algorithms to solve the location problem oflogistics, the use of binary encode, in accordance with the objective functionand constraints set punishment factor, the fitness function reached; thengenetic algorithm based on a sample of the selected cross-operation. First,randomly generated cross-location; then, the use of single-point cross to forma new gene of the way; then carried out in accordance with a certain mutationprobability; again the survival of the fittest genetic algorithm based on therules out, in accordance with the mutation probability must be the worst outof the sample, sample copy will be optimal. Assessed based on the merits ofthe individual fitness function. Finally adopted must be the number ofiterations required is relatively optimal solution. Genetic AlgorithmSolution of the problem from the beginning three sets of cable, which is thegenetic algorithm optimization algorithm with the traditional distinction between great. Traditional optimization algorithms are from a single iterationfor the initial value of the optimal solution; easily into local optimalsolution. Genetic algorithms start the search from the string collection,coverage, and conducive to the overall merit; At the same time, there is astrong genetic algorithm for fault tolerance, genetic algorithm sets theinitial string itself and the optimal solution with a large number ofinformation far; through select, crossover and mutation operation can bequickly ruled out the difference between the optimal solution with a greatseries; And genetic algorithm in the selection, crossover and mutationoperations are random, rather than precise rules to determine. Thereforegenetic algorithms to solve optimization problems and then has its ownadvantages unmatched. Algorithm used in the science of genetic algorithm tosolve optimization problems at the same time the advantages, to learn of theproject site at the demand side aspects of the presence of accumulatedexperience, will learn from the experience of the algorithm. So now to relyon the experience of demand-side compared to the solution of practical problems,the algorithm can be great in theory, improve the logistics efficiency of thestation site.Authors proved the algorithm to improve the market site at the accuracyand computing efficiency, the algorithm after the project is currently inreview and rigorous testing .It will go into practical use soon. Greenagricultural trading system capacity management module refers to the processof the logistics management and transportation system, that is, means oftransport and transport management of human resources. Author combine projectsthe actual situation of the demand side of capacity management module to theneeds analysis, detailed design, coding and testing. According to the currentlogistics information management needs and the actual characteristics ofreal-time vehicle management and logistics and transportation managementfoundation for the two applications, the convergence of advanced logisticsinformation management, process management logistics vehicles based onreal-time dynamic information management, combined with the logistics andtransportation management and distribution management applications, toprovide users with a database of vehicles, the driver database, the vehiclestatus information, such as Management and General Services, in order toachieve online monitoring of vehicle logistics enterprises, statistics, and analysis for management decision-making, improve scheduling and monitoringvehicular traffic management, reduce operating costs, and enhance thecomprehensive competitiveness of enterprises. Green agricultural trading andservice system, distribution of sales of the project modules are demand-sidedistribution system throughout the sales management module. The actualsituation the authors carried out a needs analysis, detailed design, codingand testing. Distribution module sales distribution channels for enterprisesto sell plans to the source of information based on aggregate data to thecentralized control of resources as a means, through the procurement of networkmanagement, sales management, merchandise management, efficient operation,helping enterprises set up distribution networks based on sales of informationsystems, to build the core competitiveness of the distribution.