交通运输系统管理02.pptxVIP

2019/11/19;规范分析;系统模型化;模型定义;模型的特征;建立模型的一般原则;模型化的优势;模型的局限性;构造模型的步骤;形象模型;形象模型;概念模型;数学模型;数学模型;优化;Heuristics and the Need for Exact Algorithms;图示化模型;求解技术;Heuristics Algorithms;启发式方法 1:将每个市场区分配给最近的仓库。根据运输成本分配每个生产厂的供应。;启发式方法 2:根据到每个生产厂到各市场的总运输成本分配;启发式方法 #2:根据到每个市场的总运输成本分配;运筹学算法;最小成本的目标函数为: min 0x(p1,w1) + 5x(p1,w2) + 4x(p2,w1) + 2x(p2,w2) + 3x(w1,c1) + 4x(w1,c2) + 5x(w1,c3) + 2x(w2,c1) + 1x(w2,c2)+ 2x(w2,c3) 满足以下约束: x(p2,w1) + x(p2,w2) ? 60000 x(p1,w1) + x(p2,w1) = x(w1,c1) + x(w1,c2) + x(w1,c3) x(p1,w2) + x(p2,w2) = x(w2,c1) + x(w2,c2) + x(w2,c3) x(w1,c1) + x(w2,c1) = 50000 x(w1,c2) + x(w2,c2) = 100000 x(w1,c3) + x(w2,c3) = 50000 所有的流量变量大于或等于零.;线性规划;最优模型线性规划;EXCEL Solver 方法;28;EXCEL Solver Approach;EXCEL Solver Approach;;EXCEL Solver Approach;EXCEL Solver Approach;EXCEL Solver Approach;群智能理论;Swarm Intelligence;Swarm Intelligence(续);Swarm Intelligence(续);Swarm Intelligence(续);Swarm Intelligence(续);Swarm Intelligence(续);Swarm Intelligence(续);Swarm Intelligence(续);Swarm Intelligence(续);Swarm Intelligence(续); 粒子群算法(particle swarm optimization，PSO)由Kennedy和Eberhart在1995年提出，该算法模拟鸟集群飞行觅食的行为，鸟之间通过集体的协作使群体达到最优目的，是一种基于Swarm Intelligence的优化方法。同遗传算法类似，也是一种基于群体叠代的，但并没有遗传算法用的交叉以及变异，而是粒子在解空间追随最优的粒子进行有哪些信誉好的足球投注网站。PSO的优势在于简单容易实现同时又有深刻的智能背景，既适合科学研究，又特别适合工程应用，并且没有许多参数需要调整。 ;James Kennedy received the Ph.D. degree from theUniversity of North Carolina, Chapel Hill, in 1992.He is with the U.S. Department of Labor, Washington,DC. He is a Social Psychologist who has been working with the particle swarm algorithm since 1994. He has published dozens of articles and chapters on particle swarms and related topics, in computer science and social science journals and proceedings. He is a coauthor of Swarm Intelligence (San Mateo, CA: Morgan Kaufmann, 2001), with R.C. Eberhart and Y. Shi, now in its third printing.;Russell C. Eberhart (M’88–SM’89–F’01) received the Ph.D. degree in