具有模糊时间约束的城市配送多车型车辆调度问题  

Multi-type Vehicle Scheduling Problem in City Distribution with Fuzzy Time Restraints

作　　者：卢冰原[1] 吴义生[1] 程八一[2] 

机构地区：[1]南京工程学院经济管理学院,江苏南京211167 [2]合肥工业大学管理学院,安徽合肥230009

出　　处：《公路交通科技》2011年第11期152-158,共7页Journal of Highway and Transportation Research and Development

基　　金：教育部人文社会科学研究青年基金项目(10YJC630165);江苏省教育厅高校哲学社会科学基金项目(09SJD630036)

摘　　要：针对城市物流配送中广泛存在的有时间窗多车型问题,以及由于交通路况与人力因素导致的相关时间参数模糊化现象,以梯形模糊数表征时间参数,利用梯形模糊代数、有符号距离和区间数距离公式,构造出一种具有较高精度的提前/滞后惩罚函数,继而在此基础上给出了一种以最小化配送费用和客户时间窗提前/滞后惩罚为目标的具有模糊时间约束的多车型车辆调度问题模型。在车辆调度问题求解方面,针对经典粒子群算法容易陷入局部最优的问题,给出了一种具有量子行为的改进粒子群算法来改善粒子群算法的性能。最后通过仿真试验表明,该算法不仅具有较高的搜索效率与搜索质量,而且具有较快的收敛速度,验证了其可行性与有效性。For the widespread multi-type vehicle scheduling problem with time window in city distribution and fuzzy time parameters problem caused by traffic and human factors,using trapezoidal fuzzy number to denote time parameter,a earliness/tardiness penalty function based on trapezoidal fuzzy number algebra,singed distance and interval number distance which has higher accuracy was illustrated.On this basis,a multi-type vehicle scheduling problem（MVSP） model with fuzzy time restraints for minimized delivery cost and earliness/tardiness penalty according to fuzzy time window of customer was introduced.After that,aimed at the problem of classic particle swarm optimization（PSO） algorithm easily getting into the local optimum in solving process,a quantum-behaved particle swarm optimization（QPSO） algorithm was proposed for the MVSP problem,which help the algorithm to improve its performance.At last,the result of simulating experiment indicates that this algorithm not only has good search efficiency and quality,but also has the quick convergence rate,the feasibility and efficiency of the QPSO algorithm were also verified.

关 键 词：运输经济 调度优化 粒子群算法 多车型车辆调度问题 模糊环境 量子行为 

分 类 号：TP278[自动化与计算机技术—检测技术与自动化装置]