城市轨道交通大小交路列车开行方案优化研究

根据城市轨道交通大小交路运营模式的特点,以最小化乘客等待时间、车辆走行公里和列车运行时间为目标,构建大小交路列车开行方案多目标优化模型.其中决策变量为发车频率、列车编组和小交路折返站位置.采用线性加权法将原模型转化为单目标优化模型,并设计受控随机搜索算法求解.通过案例验证了模型的有效性,对小交路发车频率、折返站位置进行了灵敏度分析.结果表明,开行大小交路可以有效降低大交路列车满载率的非均衡性,减少车辆运用数;小交路长度越短对乘客越不利,且不能使企业成本节省越多.     

Route design for last-in,first-out deliveries with backhauling

The retail route design problem extends the capacitated vehicle routing problem with time windows by introducing several operational constraints, including order loading and delivery restrictions (last-in, first-out), order-dependent vehicle capacity, material handling limits at the warehouse, backhauling, and driving time bounds. In this paper, the problem is modeled on a directed network for an application associated with a major grocery chain. Because the corresponding mixed-integer program proved too difficult to solve with commercial software for real instances, we developed a greedy randomized adaptive search procedure (GRASP) augmented with tabu search to provide solutions. Testing was done using data sets provided Kroger, the largest grocery chain in the US, and benchmarked against a previously developed column generation algorithm. The results showed that cost reductions of $4887 per day or 5.58% per day on average, compared to Kroger’s corresponding solutions.     

The Share-a-Ride problem with stochastic travel times and stochastic delivery locations

We consider two stochastic variants of the Share-a-Ride problem: one with stochastic travel times and one with stochastic delivery locations. Both variants are formulated as a two-stage stochastic programming model with recourse. The objective is to maximize the expected profit of serving a set of passengers and parcels using a set of homogeneous vehicles. Our solution methodology integrates an adaptive large neighborhood search heuristic and three sampling strategies for the scenario generation (fixed sample size sampling, sample average approximation, and sequential sampling procedure). A computational study is carried out to compare the proposed approaches. The results show that the convergence rate depends on the source of stochasticity in the problem: stochastic delivery locations converge faster than stochastic travel times according to the numerical test. The sample average approximation and the sequential sampling procedure show a similar performance. The performance of the fixed sample size sampling is better compared to the other two approaches. The results suggest that the stochastic information is valuable in real-life and can dramatically improve the performance of a taxi sharing system, compared to deterministic solutions.     

客户配送要求变动下的VRPSDP干扰管理优化

为解决客户配送要求变动对VRPSDP方案造成的干扰问题，从成本和服务时间两个方面，就干扰事件对原物流配送车辆路径方案造成的影响进行了干扰辨识分析.构建了VRPSDP干扰管理模型，并设计基于禁忌搜索的调度算法.从Solomon标准测试算例中随机抽取6组算例对模型和算法的有效性进行验证.结果显示，本文的干扰管理方案与传统的增派车辆和全局重调度方案相比较，广义总费用偏离有明显地降低，且本文方法能够在较短的时间内生成满意的物流配送车辆调度调整方案.     

A case study of Beijing bus crew scheduling: a variable neighborhood‐based approach

Crew scheduling for bus drivers in large bus agencies is known to be a time‐consuming and cumbersome problem in transit operations planning. This paper investigates a new meta‐heuristics approach for solving real‐world bus‐driver scheduling problems. The drivers' work is represented as a series of successive pieces of work with time windows, and a variable neighborhood search (VNS) algorithm is employed to solve the problem of driver scheduling. Examination of the modeling procedure developed is performed by a case study of two depots of the Beijing Public Transport Group, one of the largest transit companies in the world. The results show that a VNS‐based algorithm can reduce total driver costs by up to 18.1%, implying that the VNS algorithm may be regarded as a good optimization technique to solve the bus‐driver scheduling problem. Copyright © 2015 John Wiley & Sons, Ltd.     

A parallel algorithm for train rescheduling

One of the crucial factors in achieving a high punctuality in railway traffic systems, is the ability to effectively reschedule the trains when disturbances occur. The railway traffic rescheduling problem is a complex task to solve both from a practical and a computational perspective. Problems of practically relevant sizes have typically a very large search space, making them time-consuming to solve even for state-of-the-art optimization solvers. Though competitive algorithmic approaches are a widespread topic of research, not much research has been done to explore the opportunities and challenges in parallelizing them. This paper presents a parallel algorithm to efficiently solve the real-time railway rescheduling problem on a multi-core parallel architecture. We devised (1) an effective way to represent the solution space as a binary tree and (2) a novel sequential heuristic algorithm based on a depth-first search (DFS) strategy that quickly traverses the tree. Based on that, we designed a parallel algorithm for a multi-core architecture, which proved to be 10.5 times faster than the sequential algorithm even when run on a single processing core. When executed on a parallel machine with 8 cores, the speed further increased by a factor of 4.68 and every disturbance scenario in the considered case study was solved within 6 s. We conclude that for the problem under consideration, though a sequential DFS approach is fast in several disturbance scenarios, it is notably slower in many other disturbance scenarios. The parallel DFS approach that combines a DFS with simultaneous breadth-wise tree exploration, while being much faster on an average, is also consistently fast across all scenarios.     

面向城市轨道交通的启发式疫情密接人群追溯方法

在复杂轨道交通网络中，某些乘客的出行行程是确定的。这些乘客可以作为启发式的“证人”，为其他乘客证明某个行程的可能性，从而在保证查全率的前提下，准确地找出潜在的密接乘客。本文的目的是开发一套适用于城市轨道交通的疫情密接人群追溯方法，利用有限的确定行程乘客作为虚拟的目击者，采用启发式的树搜索生成目标乘客可能的出行链，通过验证目标出行链与感染者行程是否有交集，判断目标乘客是否为密接乘客。以北京城市轨道交通为例，招募志愿者在特定线路乘车，并假定感染者乘车信息，通过提取有关自动售检票(Automatic Fare Collection, AFC)数据识别密接乘客，以验证方法的有效性。在实验场景下，本文提出的方法对密接乘客的识别查全率达到100%，查准率达到92.7%，表明方法具备一定的可行性。识别结果有助于有关部门针对性采取措施，更高效率地防范疫情蔓延和传播。     

基于GA-MADS混合算法的既有铁路平面线形自动重构

研究目的:既有铁路平面线形重构是铁路养护维修与增改建设计的重要基础,重构结果对列车运行安全、养护改建工程量将产生重要影响。既有方法通常先识别确定交点坐标,再逐交点优化半径缓长,属局部重构,难以实现全局优化;同时对约束的处理还不全面,重构的线形需要经过大量人工调整方可应用。对此,本文提出一种遗传算法混合网格自适应直接搜索(GA-MADS)的既有线路平面重构方法,实现线路平面整体自动优化重构。研究结论:(1)在遗传算法中融入MADS,有效克服了遗传算法在优化中随机性大、早熟收敛的缺点,实现平面线形整体重构;(2)各类约束在优化过程中被自动处理,无需人工后续调整;(3)研究成果已在各大铁路设计院6 000余公里的既有线增改建设计中成功应用,结果表明该方法可以自动产生满足工程约束的优化重构线形,大幅提高效率和质量。     

铁路集装箱中心站多轨道吊柔性协同调度优化

针对铁路集装箱中心站轨道吊协同装卸调度(RMGCS)问题，考虑多轨道吊间的作业干涉和安全距离约束，建立目标函数为最小化集装箱完工时间的混合整数规划数学模型。结合 RMGCS问题特性，提出一种考虑多轨道吊柔性协同作业的改进麻雀搜索算法(ISSA)，首先，提出能够解决多轨道吊间作业冲突的任务分配策略和干涉判断方法；其次，设计自适应非线性动态安全值，平衡全局和局部搜索；再次，改进加入者更新移动方式，使其在全维度上靠近最优解；最后， 引入LOV(largest-order-value)规则，将个体从实数向量映射到集装箱整数序列。为了验证问题和算法的有效性，分别利用ISSA与其他算法求解不同样本规模的算例，结果表明：ISSA在不同样本 规模的RMGCS问题求解上，均具有更好的寻优效果。此外，所提出的柔性协同调度模式相比现有的固定范围顺序装卸调度模式，使样本量为45的装卸作业总完工时间缩短了15.5%。     

Heuristic search for the coupled runway sequencing and taxiway routing problem

This paper presents the first local search heuristic for the coupled runway sequencing (arrival & departure) and taxiway routing problems, based on the receding horizon (RH) scheme that takes into account the dynamic nature of the problem. As test case, we use Manchester Airport, the third busiest airport in the UK. From the ground movement perspective, the airport layout requires that departing aircraft taxi across the arrivals runway. This makes it impossible to separate arrival from departure sequencing in practice. Operationally, interactions between aircraft on the taxiways could prevent aircraft from taking off from, or landing on, runways during the slots assigned to them by an algorithm optimizing runway use alone. We thus consider the interactions between arrival and departure aircraft on the airport surface. Compared to sequentially optimized solutions, the results obtained with our approach indicate a significant decrease in the taxiway routing delay, with generally no loss in performance in terms of the sequencing delay for a regular day of operations. Another benefit of such a simultaneous optimization approach is the possibility of holding aircraft at the stands for longer, without the engines running. This significantly reduces the fuel burn, as well as bottlenecks and traffic congestion during peak hours that are often the cause of flight delays due to the limited amount of airport surface space available. Given that the maximum computing time per horizon is around 95 s, real-time operation might be practical with increased computing power.