基于可预知事件的城市轨道交通乘务轮转计划优化调整

城市轨道交通乘务轮转计划是在充分考虑乘务员正常的夜间休息、休息日休息、休假等因素的基础上,合理安排乘务员一定阶段内的每日值乘任务.但在某日乘务轮转计划执行前,仍然会发生乘务员临时请假、临时调动等可预知事件,此时就需要对现有乘务轮转计划进行优化调整.调整以在满足任务调整需求条件下乘务员阶段任务最均衡为目标,且调整的幅度越少越好.考虑休息日、工作量、任务连乘等约束条件,以调整后所有修改乘务任务序列的总费用最小为目标函数,建立乘务轮转计划优化调整模型,并设计基于贪婪算法和禁忌搜索算法的启发式算法进行求解.算例表明,求解算法在一定程度上实现了调整后乘务轮转计划的均衡性,达到了优化的目的.

Optimizing Adjustment of Urban Rail Transit Crew Rostering Plan Based on Predictable Events

Crew rostering plan of urban rail transit is the daily crew task which each crew needs to complete within a certain stage. It is requested to fully consider the crew’s normal day off, rest at night and other factors. But there will still be temporary leave, temporary mobilization and other predictable events before the execution of one day’s crew rostering plan. Optimizing adjustment of the existing rostering plan is necessary at this moment. The main goal of adjustment under the condition of predictable events is the equilibrium of every crew’s tasks. And the less adjustment is the better. This paper establishes an optimizing adjustment model for urban rail transit crew rostering plan, with consideration of factors such as day off, workload, tasks connection. The objective function of this model is the minimum total cost of modified crew task sequences. And the heuristic algorithm based on greedy algorithm and tabu search algorithm is used to solve this problem. The numerical example shows that the algorithm achieves the equilibrium of the adjusted crew rostering plan to a certain extent, and achieves the aim of optimization.