公交车调度

本文利用多目标优化方法建立了公交车调度的数学模型.首先通过数据分析,并考虑到方案的可操作性,将一天划分为早高峰前,早高峰,早高峰和晚高峰之间,晚高峰及晚高峰后5个时段;引入车辆的平均满载率,乘客的等待抱怨程度及拥挤抱怨程度作为三个目标函数,建立了三目标优化模型;通过加权,将三个目标函数合并为一个目标函数.运用MATLAB数学软件计算出了上行、下行各个时段发车的时间间隔:上行各时段时间间隔分别为5、2、4、3、15,下行各时段时间间隔分别为10、2、5、3、8(单位:分钟);所需总车辆数为52辆,共发车534次,公交公司的平均满载率为82.094%,抱怨顾客的百分比为0.91%.通过模型检验得出所求模型较为稳定.最后,通过对原始数据的分析和处理,得出在进入和离开乘客高峰时期,局部缩短采集数据时间间隔是改善调度方案的有效方法.

Buses Dispatching

In this paper, a mathematical model on buses dispatching is presented with the method of perfecting on multi objects. Firstly, with the data analyses and consideration on feasibility, a typical workday can be divided into five spans: before morning rush hour, morning rush hour, the span between morning rush hour and evening rush hour, evening rush hour and its beyond. Three objective functions are introduced: the average capacity rate, the degree of the passengers' waiting, and the degree of passengers' complain on waiting and crowded, to set up a model of perfecting on three objectives. Weighted average method is used to combine the three functions as one. MATLAB mathematical software is employed to work out the interval of sending buses. Up going buses are sent at respectively 5,2,4,3,15(minutes) and down going buses are 10,2,5,3,8(minutes). Altogether, 52 buses are needed and 534 buses one a workday. The percent of average capacity is 82.094%, and the percent of the passengers' complain is 0.91%. The model is proved to be steady through the test. Lastly shortening the statistical time on partial area is concluded to be an effective way to improve the dispatching plan through the analyses on original data.