基于CGACO的多重分类准则模型优化研究

摘要

To improve the service level in logistics supply chain management, a multiple classification criteria model is improved based on the Pareto's law, standard column average method and optimization theory.Through merging the advantage of Chaos Genetic Optimization(CGO) and Chaos Ant Colony Optimization(CACO) algorithm,a Chaos Genetic and Ant Colony Optimization(CGACO) algorithm is designed in solving combinatorial optimization problems.The initial colony is produced, mutation operator is modified, the ant colony algorithm is optimized and related parameters in the algorithm are improved by chaos search optimization to achieve arithmetic's organic integration.A logistics simulation example shows that the CGACO algorithm is valid in solving multiple classification criteria model problems.%为提高供应链物流管理服务水平,基于帕累托定律,运用规范列平均法和优化理论建立了基于多重分类准则模型.通过有效利用混沌遗传和蚁群优化算法在组合优化中的优势,给出了混沌遗传蚁群优化算法,采用混沌搜索优化初始群体、修正变异算子、蚁群算法寻优优化、改进相关参数等实现了两种算法的有机集成.物流案例实证表明了混沌遗传蚁群算法在解决多重分类准则优化模型方面的有效性.