基于遗传模拟退火算法的大件公路运输路径选择优化  

Highway transportation route selection optimization based on improved genetic annealing algorithm

作　　者：程博[1] 杨育[1] 刘爱军[2] 陈伟[1] 邢青松[1] 

机构地区：[1]重庆大学机械传动国家重点实验室,重庆400030 [2]西南交通大学机械工程学院,四川成都610031

出　　处：《计算机集成制造系统》2013年第4期879-887,共9页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(71071173);重庆市科技攻关重点资助项目(2010GGB108);教育部高等学校博士学科点科研基金资助项目(20090191110004);中央高校基本科研业务费科研专项自然科学类面上项目(CDJZR-10-110012)~~

摘　　要：针对大件公路运输路径选择优化难题,以最小化运输成本为目标,在考虑带配送时间窗、客户服务时限、车辆超载惩罚、车辆载重限制、车辆容积限制的基础上,构建了大件公路运输路径选择优化模型,并提出改进遗传模拟退火算法对此类问题进行优化。该算法首先基于满足车辆容积和承载量的两层编码方式产生多个初始种群,然后各种群之间通过相互竞争实现优秀个体的迁移共享,最终搜索到最优解。最后,通过实例仿真验证了该算法解决此类特殊运输问题的有效性,并通过与其他算法进行比较,证明了该算法的先进性,为大件公路运输路径选择问题提供了新的解决思路。Aiming at the highway transportation route selection optimization problem, the minimum transportation costs was taken as the goal to construct the highway transportation route selection optimization model based on considerations of delivery time window, customer service time, overload punishment, vehicle load and traffic volume limits. The improved genetic simulated annealing algorithm was also proposed to optimize the problem. Based on the two-layer coding method which satisfied vehicle volume and carrying capacity, multiple initial populations were generated. The migration and sharing of outstanding individual were achieved by competing with each other among all populations, and eventually the optimal solution was found. The effectiveness of the improved algorithm to solve these kind of special transportation problems was verified through the simulation experiment, and compared with other algorithms, the method provided a new solution to highway transportation routing problem.

关 键 词：路径选择 运输 遗传模拟退火算法 多种群竞争 

分 类 号：TP278[自动化与计算机技术—检测技术与自动化装置]