市域轨道交通线路快慢列车停站方案研究

为解决市域通勤客流欲快速到达目的地的需求问题,以乘客的出行时间节约值最大为优化 目标,建立大小交路条件下的快慢列车停站方案模型。基于乘客出行时间效益优先原则,考虑乘客候车时间、乘客在车区间运行时间、乘客在车等待时间以及越行对慢车乘客的延误和快车乘客 的时间变化影响等因素,构建了目标函数,同时以列车开行密度、满载率等作为约束条件,建立 0-1 整数规划模型。通过在实际算例中运用遗传-禁忌算法对模型进行求解,得到快车的最佳停站方案,并将计算所得方案的乘客出行时间与相同交路条件、发车间隔以及开行比例下的普通站站 停开行模式的时间进行了对比分析。结果表明,该方案可以节省总出行时间约8 062h,节省人均出行时间约8.5min,人均延误时间约0.86min,总体提高了乘客出行效率和服务水平,具有有效性和合理性。     

不规则物流园区功能区块布局模型与算法

物流园区功能区块的合理布局对于降低物流成本、提高物流作业效率有重要意义.传统的研究主要建立在假定物流园区是矩形的理想情况下,本文基于栅格数据结构,构建了以运输成本最小为目标的不规则物流园区功能区块布局的集合划分模型,并引入了栅格图像矢量化技术,用于提取布局形成后的图像拓扑结构,获得路网信息,然后设计了基于割树技术的变邻域搜索算法进行求解.最后以某物流园区为例,通过MATLAB编程计算得出布局方案,验证了本文模型和算法的有效性.     

次任务下的驾驶员视觉搜索能力研究

从视觉搜索的角度研究次任务对驾驶绩效的影响,通过反应时长评价驾驶接管绩效,使用视野测试和眼动数据研究驾驶员视觉搜索能力.自变量为驾驶员所执行的次任务类型和次任务沉浸时长.共采集32名被试在不同沉浸程度下的反应时长和眼动数据,结果表明,经过一段时间的反应后,被试所需要的反应时长缩短且正确率升高.被试所执行的次任务类型对其...     

集装箱码头卸船作业调度方案的两阶段禁忌搜索算法

为提高集装箱码头卸船作业效率,建立了堆存位置-集卡调度一体优化模型,设计了集装箱码头卸船作业调度方案两阶段禁忌搜索算法。在第一阶段,通过禁忌搜索算法决定集装箱的堆存位置;在第二阶段,基于堆存方案执行另一禁忌搜索算法,获得集卡的优化调度方案,然后再计算卸船时间,且将结果反馈到第一阶段的搜索过程,通过两阶段搜索过程的反馈优化卸船调度方案。计算结果表明两阶段禁忌搜索算法可以平均减少卸船作业时间6.78%,结果稳定。     

Joint leisure travel optimization with user-generated data via perceived utility maximization

The lack of personalized solutions for managing the demand of joint leisure trips in cities in real time hinders the optimization of transportation system operations. Joint leisure activities can account for up to 60% of trips in cities and unlike fixed trips (i.e., trips to work where the arrival time and the trip destination are predefined), leisure activities offer more optimization flexibility since the activity destination and the arrival times of individuals can vary.To address this problem, a perceived utility model derived from non-traditional data such as smartphones/social media for representing users’ willingness to travel a certain distance for participating in leisure activities at different times of day is presented. Then, a stochastic annealing search method for addressing the exponential complexity optimization problem is introduced. The stochastic annealing method suggests the preferred location of a joint leisure activity and the arrival times of individuals based on the users’ preferences derived from the perceived utility model. Test-case implementations of the approach used 14-month social media data from London and showcased an increase of up to 3 times at individuals’ satisfaction while the computational complexity is reduced to almost linear time serving the real-time implementation requirements.     

Multi-period planning of closed-loop supply chain with carbon policies under uncertainty

Climate change and greenhouse gases emissions have caused countries to implement various carbon regulatory mechanisms in some industrial sectors around the globe to curb carbon emissions. One effective method to reduce industry environmental footprint is the use of a closed-loop supply chain (CLSC). The decision concerning the design and planning of an optimal network of the CLSC plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. In this context, this research proposes an optimization model for design and planning a multi-period, multi-product CLSC with carbon footprint consideration under two different uncertainties. The demand and returns uncertainties are considered by means of multiple scenarios and uncertainty of carbon emissions due to supply chain related activities are considered by means of bounded box set and solve using robust optimization approach. The model extends further to investigate the impact of different carbon policies such as including strict carbon cap, carbon tax, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. The model captures trade-offs that exist among supply chain total cost and carbon emissions. Also, the proposed model optimizes both supply chain total cost and carbon emissions across the supply chain activities. The numerical results reveal some insightful observations with respect to CLSC strategic design decisions and carbon emissions under various carbon policies and at the end we highlighted some managerial insights.     

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.     

Short-term traffic flow prediction using time-varying Vasicek model

This paper provides a two-step approach based on the stochastic differential equations (SDEs) to improve short-term prediction. In the first step of this framework, a Hull-White (HW) model is applied to obtain a baseline prediction model from previous days. Then, the extended Vasicek model (EV) is employed for modeling the difference between observations and baseline predictions (residuals) during an individual day. The parameters of this time-varying model are estimated at each sample using the residuals in a short duration of time before the time point of prediction; so it provides a real time prediction. The extracted model recovers the valuable local variation information during each day. The performance of our method in comparison with other methods improves significantly in terms of root mean squared error (RMSE), mean absolute error (MAE) and mean relative error (MRE) for real data from Tehran’s highways and the open-access PeMS database. We also demonstrate that the proposed model is appropriate for imputing the missing data in traffic dataset and it is more efficient than the probabilistic principal component analysis (PPCA) and k-Nearest neighbors (k-NN) methods.     

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.