基于启发式函数蚁群算法的VRP优化研究

车辆路径问题是一个NP难题,蚁群算法是求解诸如车辆路径安排等组合优化问题的有效工具,为此利用启发式函数对传统的蚁群算法进行改进和优化.并通过实例对该方法进行检验,其结果显示,启发式函数蚁群算法的性能,优于传统的蚁群算法.     

铁路空车调整蚁群算法的研究

依据数量调配和网络配流两个空车调整的核心问题,建立了空车调整协同优化模型,并设计了相应的蚁群算法。由于共同径路约束与模型是不协调的,且对流约束不会对模型的最优解产生影响,故而模型可以描述为线性整数规划模型。同时,研究表明蚁群算法对求解空车调整问题具有一定优势。     

基于连通可靠性的车辆路径问题

物流配送车辆路径问题(VRP)是一个NP-hard问题,很多求解方法仅考虑路网连通无穷大的情况,将其看成平均旅行时间问题,对于突发事件下造成路网拥堵情况下的车辆路径问题很少涉及,对此结合蚁群算法,对突发事件下各路段的连通可靠性进行比较后选择合适路段通过,用以提高路网的通行能力。     

基于蚁群和模拟退火算法的专用线动态取送车研究

针对树枝形专用线直达货物列车取送车作业的特点,在考虑机车走行模式和牵引质量条件下,以取送车走行时间和等待时间之和最小为优化目标,建立树枝形专用线取送车数学模型,并且提出一种融合蚁群和模拟退火算法的混合算法对问题进行求解。最后通过算例分析,得出机车牵引质量的改变会影响取送车作业最优解的结论。     

改进蚁群算法在旅行商问题中的应用

介绍蚁群算法及其原理,算法模型和实现过程,分析基本蚁群算法易出现早熟停滞现象的原因.在原有算法基础上引入最优、最差信息素更新策略和局部最优搜索策略,从而扩大可行解的范围,避免算法过早停滞,同时加快算法的收敛速度.以旅行商问题为例进行仿真计算,说明改进蚁群算法的性能.     

用蚁群算法求解类TSP问题的研究

铁路运输调度问题能否很好解决对于铁路运输公司至关重要,旅行商问题(简称TSP)经常被用来研究运输调度问题。根据某化工集团铁路运输公司的生产实际,提出了“类TSP”问题,但由于“类TSP”和TSP有很大区别,以前求解TSP的优化算法不能直接用于“类TSP”的求解。利用蚁群算法是可以较好解决TSP的一类新型模拟进化算法,适应“类TSP”的要求,并通过“蚁后规则”和变异机制的引入,提出了一种改进的人工蚁群算法。计算机仿真结果表明该算法求解“类TSP”是行之有效的。     

基于改进型蚁群算法的货物作业车取送模型优化

介绍了蚁群算法及其改进型的原理、模型和算法实现过程,并采用改进型算法对货物作业车取送数学模型进行了优化计算。优化计算结果表明,改进型蚁群算法的优化计算切实可行,为类似其他的铁路运输优化计算提供了新的思路和方法。     

基于蚁群算法和人工神经网络的物流配送中心选址决策

将蚁群聚类算法和人工神经网络进行融合,提出ACC—BP算法,并将其用于物流配送中心的选址问题,利用实验数据进行训练得出最终评价模型。     

运输路径优化蚁群算法问题研究

对于某一特定源点和目的地之间的车辆运输调度问题,建立基于风险、考虑成本和时变条件下的路径优化模型,采用蚁群算法的信息素更新策略,使边上残留信息素能够正确反映时变网络中边上权值的变化,并结合遗传算法,采取最优个体交叉策略将蚁群每次遍历后形成的解作为初始群种进行单点交叉计算,以避免陷入局部最优解,提高算法的收敛性。通过算例分析验证算法的有效性。     

应急物流配送问题的蚁群聚类算法研究

提出了一种用于解决突发事件下,物流配送多目标优化问题的蚁群聚类优化算法。突发事件下的物流配送规划一般包含两方面内容,将救灾物资运往受灾地区和将灾区的伤员及时送至各医疗点。将多目标问题转化为单目标问题,结合蚁群的墓地构造行为特点,利用改进LF蚁群聚类模型,以节点需求未得到满足的不满意度最小和路由时间最短为优化目标,用LF蚁群聚类方法按约束条件进行聚类,最终确定车辆路由线路。     

A comparative automated content analysis approach on the review of the sharing economy discourse in tourism and hospitality

Using the sharing economy (SE) as the context, this article provides a coherent and nuanced methodological understanding of automated content analysis (ACA) in tourism and hospitality (TH) field. By adopting a comparative ACA approach, the paper compares the current TH Western academic literature of the SE with news media discourse in TH from the period 2011–2016 (August) (inclusive). The emerging issues from the news media discourse, such as mobility, SE companies and the role of government, are absent in current tourism academic research. Findings reveal that ACA can facilitate a more systematic comparison between different sources of data. This paper offers a starting point for tourism scholars to methodologically engage with ACA that can draw useful insights on a particular context.     

A column generation based approach for the Train Network Design Optimization problem

This paper investigates the Train Network Design Optimization problem arising from railroad industry which involves the integration of three inter-related decision sub-problems: train routing which is to identify origin, destination and itinerary for individual trains; block-to-train assignment detailing the block movements with trains and swaps between trains; and crew-to-train assignment specifying the crew services for train routes. A column generation based hierarchical approach with two stages is designed: the first stage generates a pool of promising train routes iteratively based on the crew segments by the column generation technique; and the second stage develops an integer linear programming model for the subsequent decisions including train route selection and block-to-train assignment. Numerical experiments with realistic test instances are conducted and the outcome demonstrates the capability of the proposed approach in solving the Train Network Design Optimization problem competently.     

An inventory-based simulation model for annual-to-daily temporal freight assignment

In the aggregate freight demand modeling literature, temporal assignment (annual to daily flows) is often oversimplified or neglected altogether. Unlike passenger flows, freight flows over the course of a year are not uniform and can vary significantly as the result of trade-offs between inventory and transportation cost management. We introduce the first temporal assignment model that explicitly considers these trade-offs for aggregate freight forecasting. A two-stage model is proposed that first decomposes aggregate annual zonal flows to firm group annual flows using a supply chain network model, which are then temporally assigned by simulating purchase order transactions throughout supply chains. Lot sizes are estimated with an Economic Order Quantity (EOQ) model and calibrated with monthly inventory data. The result is an aggregate-disaggregate-aggregate model that fits into aggregate freight forecasting models but makes use of more disaggregate logistical data. The model is illustrated with a simple replicable example, followed by a case study conducted with California statewide data to break out the distributed zonal flows into average daily volumes for network assignment. Calibration results using 2007 IMPLAN data showed a median percentage difference of simulated annual flows from FAF³ data of 2.38%, and a median percentage difference of simulated inventories from IMPLAN data of 4.85%, which suggests an excellent fit. Empirical validation results showed the model outperforms fixed factor approaches in mean value accuracy by 15–31%.     

Estimating the mean waiting time in airports through cooperative disaster response operations

Airport use rises in response to the need to transport people and deliver aid following a disaster episode. However, owing to unexpected demand and the availability of multiple aircraft operators and organizations, the fluctuating waiting time in airports has become an operational bottleneck. This requires airports to carry out cooperative disaster response operations in the region to relieve congestion and reduce response time. This study develops a model to estimate the mean waiting time in airports through cooperative disaster response operations, using an open Jackson network model. It presents a numerical example to understand the different operational schemes. The result shows that assignment by adjusting the transition probability to meet the airport's service rate is the optimal case through cooperative operations. The policy implication for airport operators is that airports must be prepared to handle a balanced role assignment during disaster responses.     

A random utility maximization (RUM) based measure of accessibility to transit: Accurate capturing of the first-mile issue in urban transit

The paper presents a random utility-based measure of accessibility to explain the first-mile issue in urban transit. A discrete access stop/station location choice model is used to calculate the expected maximum utility of transit access choices as the measure of the proposed access to transit measurement approach. It captures the effects of changes in various personal, sociodemographic, transportation and land-use variables on access to urban transit that are overlooked by conventional approaches of accessibility measurements (count-based cumulative opportunities measures and gravity-based measures). The proposed accessibility to transit measurement approach is empirically measured for the Greater Toronto Area and is integrated into an operational tool programmed in a GIS-based traffic assignment software, TransCAD 7.0. This allows comparing it to the conventional measures, and the results reveal that the conventional measures tend to over-estimate access to transit.     

拥挤条件下的铁路随机用户平衡配流模型

在对铁路客流分配问题进行概述的基础上,结合铁路旅客运输组织的特点,采用平衡分配法对铁路客流分配问题进行研究,建立了拥挤条件下的铁路随机用户平衡配流模型,并给出了求解算法和算例。     

Ant colony optimization for disaster relief operations

This paper presents a meta-heuristic of ant colony optimization (ACO) for solving the logistics problem arising in disaster relief activities. The logistics planning involves dispatching commodities to distribution centers in the affected areas and evacuating the wounded people to medical centers. The proposed method decomposes the original emergency logistics problem into two phases of decision making, i.e., the vehicle route construction, and the multi-commodity dispatch. The sub-problems are solved in an iterative manner. The first phase builds stochastic vehicle paths under the guidance of pheromone trails while a network flow based solver is developed in the second phase for the assignment between different types of vehicle flows and commodities. The performance of the algorithm is tested on a number of randomly generated networks and the results indicate that this algorithm performs well in terms of solution quality and run time.     

铁路旅客列车车底套用优化模型的研究

在列车车底数量有限的条件下,利用多商品网络流模型对列车车底的套用问题建立数学优化模型,对车底套用方案进行优化,即把列车运行图转化为网络图,而后根据流守恒、容量限制和维修等约束条件,建立以列车运输能力最大化为目标的整数规划模型。     

Integrated inventory-transportation model by synchronizing delivery and production cycles

This paper develops an integrated single-vendor multi-buyer inventory-transportation synchronized supply chain model. In this paper, the decisions of truck assignment and routing are also considered and as a result, a series of vehicle routing problems (VRP) are required to be solved. Due to the highly complicated objective function and the NP-hard VRP problems, the problem cannot be solved analytically. Hence, meta-heuristics are proposed. By means of the numerical examples and a case study, the meta-heuristics developed are shown to be very effective in solving such comprehensive supply chain models, and the results so obtained are promising.