带时间窗约束的集装箱接驳运输问题模型研究

集装箱接驳运输是集装箱运输过程中的重要环节。针对带有时间窗约束的集装箱接驳运输问题,本文首先考虑四种集装箱接驳运输任务(进口重箱,出口重箱,进口空箱,出口空箱)的实际运输要求,确定其时间窗约束,其次,基于图论知识,采用一种确定的活动在顶点上的图的描述方法,将集装箱接驳运输问题转化为一个混合0-1线性规划模型,考虑其计算复杂度,设计了一种改进的遗传算法对该问题进行求解。最后给出算例,运用本文的求解方法进行计算,结果与其他方法相比较,本文所提出的算法比目前已知的最优解差了2.1%,但是计算速度提高了51.4%。总体上在目标函数值和计算时间上都较优。     

基于混合遗传算法的供水管道漏损安全预测

为解决供水行业中供水管道漏损安全性问题,建立了供水管道漏损安全预测模型,并引入遗传算法对其优化进行漏损预测,同时,针对传统的遗传算法优化该模型的过程中出现的缺陷,提出了混合遗传算法对其求解.模拟实例的预测结果表明：该模型预测方法准确可靠,具有较高的实用性。     

城市中心区轨交站域慢行接驳空间优化设计——以天津市营口道站为例

基于OSM路网、百度地图实景、POI、百度热力等多源时空数据，运用绕行系数PRD值分析法、空间分析法、对比分析法，构建城市中心区轨交站域慢行接驳空间特征分析方法，确定轨交站域影响范围，分析站域慢行接驳空间便捷性，剖析时空特征视角下的轨交站点出入口活力特征，挖掘轨交站域慢行接驳空间品质特征。在明确慢行接驳空间优化原则的基础上，从居住区出入口、路权、静态交通、导视系统、辅助设施等方面优化设计，通过空间模型法可视化表达，营造畅行连续、空间宜人、绿色友好的轨交站域慢行接驳环境。     

遗传算法在精益物流配送线路优化中的应用

精益物流配送活动中出现的一类散户配送问题是典型的组合优化问题,利用传统的优化方法计算复杂、难以求得全局最优解,遗传算法在求解这类问题中表现出了强劲的优势.利用遗传算法对这类特殊问题进行了研究分析,针对具体的配送问题进行了仿真试验,在实验的基础上探索了交叉算子、变异算子的优化设计,实验结果证明了遗传算法在解决这类问题上的可行性和有效性.     

基于Kirchhoff分布方程的轨道交通多方式接驳选择解析

轨道交通出行与多种出行方式有效衔接，不仅能充分发挥轨道交通的运输能力，且可以有效利用低碳、环保的出行工具。通过建立接驳方式选择模型，可以研究接驳方式选择概率与多要素之间的关系。本文以厦门市为例，研究使用Kirchhoff分布方程建立接驳方式选择模型，并考虑综合出行成本对选择概率的影响，该方法具有考虑各接驳方式综合成本相对差异、计算简单的优势。结果表明：该方法能够较好地分析轨道交通站点的接驳方式选择，合理推导出站点周边接驳出行选择概率与出行距离的关系，能够一定程度反映接驳需求的空间分布。     

“高速公路客运接驳站”服务模式

山东省交通运输集团结合当地的实际情况,建立了高速公路客运接驳站——济青高速刁镇客运站,解决了当地群众上高速路、乘车难的问题。文章论述了建设高速公路客运接驳站的必要性,介绍了高速客运接驳站的选址与服务形式、安全与服务管理情况、以及使用效果与发展前景。     

中型城市居民大站快车与常规公交出行差异性分析

文章结合潍坊市城市布局及公共交通发展特点,通过对潍坊市金马街沿线居民进行公交出行意愿调查,建立居民大站快车与常规公交出行选择意愿的BL模型。对模型进行标定,分析影响居民公交出行行为的显著性因素,并对大站快车与常规公交出行分担率进行预测,对接驳距离进行弹性分析,结果表明接驳距离在一定范围内对大站快车出行分担率影响较大。     

班线客运

部要求确保接驳车夜间畅行 日前，交通运输部发出通知，针对长途客运接驳运输车辆夜间通行难等问题，要求各试点省份交通运输系统联合公安交警部门强化动态监控，对于实施接驳运输的车辆，确保其夜间通行顺畅，高速公路收费站不得阻拦正常运营的接驳运输车辆驶入高速公路。     

路段通行时间不确定下取送货车辆路径优化研究

城市高峰期交通拥堵呈常态化趋势,各运输路段的通行时间很难用精确值表达,而配送车辆的路径仍需具体指定,亟需研究路段通行时间不确定情况下的物流配送车辆的具体运输路径。文章统筹考虑配送时间与环境成本,结合Bertsimas鲁棒离散优化理论,以总行程时间和碳排放量最小为优化目标,构建了路段时间阻抗不确定情况下的同时取送货多配送中心多车路径鲁棒优化模型,采用遗传算法进行求解,并以呼和浩特市部分路网为例进行实证研究。研究结果表明:车辆路径鲁棒优化模型及其改进的遗传算法能快速生成鲁棒性能较好的车辆行驶路径,而且得到的是一条条具体的运输路径,而非配送顺序。该模型与算法能为物流配送车辆路径导航系统提供技术支撑。     

层状地层横观各向同性粘弹性优化反分析与优化方法比较

针对基坑开挖过程中所表现出来的时间效应以及地层横向与竖向变形的差异,采用Voigt模型进行横观各向同性粘弹性模拟,并结合具体的施工过程,进行横观各向同性粘弹性位移优化反分析。结合具体的工程实例,分析了阻尼最小二乘法、遗传算法以及混合遗传算法对横观各向同性粘弹性位移反分析的适应性,结果发现,阻尼最小二乘法对流变参数不敏感,而遗传算法则不同,可以对参数进行全面优化,而混合遗传算法集合了两者的优点,克服了阻尼最小二乘法的不足。     

Multi-objective bilevel construction material transportation scheduling in large-scale construction projects under a fuzzy random environment

Abstract

This paper investigates a transportation scheduling problem in large-scale construction projects under a fuzzy random environment. The problem is formulated as a fuzzy, random multi-objective bilevel optimization model where the construction company decides the transportation quantities from every source to every destination according to the criterion of minimizing total transportation cost and transportation time on the upper level, while the transportation agencies choose their transportation routes such that the total travel cost is minimized on the lower level. Specifically, we model both travel time and travel cost as triangular fuzzy random variables. Then the multi-objective bilevel adaptive particle swarm optimization algorithm is proposed to solve the model. Finally, a case study of transportation scheduling for the Shuibuya Hydropower Project in China is used as a real world example to demonstrate the practicality and efficiency of the optimization model and algorithm.     

Optimal speed detector density for the network with travel time information

In the expressway network, detectors are installed on the links for detecting the travel time information while the predicted travel time can be provided by the route guidance system (RGS). The speed detector density can be determined to influence flow distributions in such a way that the precision of the travel time information and the social cost of the speed detectors are optimized, provided that each driver chooses the minimum perceived travel time path in response to the predicted travel time information. In this paper, a bilevel programming model is proposed for the network with travel time information provided by the RGS. The lower-level problem is a probit-based traffic assignment model, while the upper-level problem is to determine the speed detector density that minimizes the measured travel time error variance as well as the social cost of the speed detectors. The sensitivity analysis based algorithm is proposed for the bilevel programming problem. Numerical examples are provided to illustrate the applications of the proposed model and of the solution algorithm.     

Reducing average comprehensive travel cost by rationally allocating trips to different travel modes

To study the effect of different transport policies on reducing the average comprehensive travel cost (CTC) of all travel modes, by increasing public transport modal share and decreasing car trips, an optimization model is developed based on travel cost utility. A nested logit model is applied to analyze trip modal split. A Genetic Algorithm is then used to determine the implementation of optimal solutions in which various transport policies are applied in order to reduce average CTC. The central urban region of Beijing is selected as the study area in this research. Different policies are analyzed for comparison, focusing on their optimal impacts on minimizing the average CTC utility of all travel modes by rationally allocating trips to different travel modes in the study area. It is found that the proposed optimization model provides a reasonable indication of the effect of policies applied.     

A schedule‐based dynamic transit network model — Recent advances and prospective future research

Using the schedule‐based approach, in which scheduled timetables are used to describe the movement of vehicles, a dynamic transit assignment model is formulated. Passengers are assumed to travel on a path with minimum generalized cost that consists of four components: in‐vehicle time; waiting time; walking time; and a time penalty for each line change. A specially developed branch and bound algorithm is used to generate the time‐dependent minimum path. The assignment procedure is conducted over a period in which both passenger demand and train headway are varying. This paper presents an overview of the research that has been carried out by the authors to develop the schedule‐based transit assignment model, and offers perspectives for future research.     

Travel itinerary problem

In this study, we propose a travel itinerary problem (TIP) which aims to find itineraries with the lowest cost for travelers visiting multiple cities, under the constraints of time horizon, stop times at cities and transport alternatives with fixed departure times, arrival times, and ticket prices. First, we formulate the TIP into a 0–1 integer programming model. Then, we decompose the itinerary optimization into a macroscopic tour (i.e., visiting sequence between cities) selection process and a microscopic number (i.e., flight number, train number for each piece of movement) selection process, and use an implicit enumeration algorithm to solve the optimal combination of tour and numbers. By integrating the itinerary optimization approach and Web crawler technology, we develop a smart travel system that is able to capture online transport data and recommend the optimal itinerary that satisfies travelers’ preferences in departure time, arrival time, cabin class, and transport mode. Finally, we present case studies based on real-life transport data to illustrate the usefulness of itinerary optimization for minimizing travel cost, the computational efficiency of the implicit enumeration algorithm, and the feasibility of the smart travel system.     

Transit network design based on travel time reliability

This paper presents a transit network optimization method, in which travel time reliability on road is considered. A robust optimization model, taking into account the stochastic travel time, is formulated to satisfy the demand of passengers and provide reliable transit service. The optimization model aims to maximize the efficiency of passenger trips in the optimized transit network. Tabu search algorithm is defined and implemented to solve the problem. Then, transit network optimization method proposed in this paper is tested with two numerical examples: a simple route and a medium-size network. The results show the proposed method can effectively improve the reliability of a transit network and reduce the travel time of passengers in general.     

Optimizing train operational plan in an urban rail corridor based on the maximum headway function

The train operational plan (TOP) plays a crucial role in the efficient and effective operation of an urban rail system. We optimize the train operational plan in a special network layout, an urban rail corridor with one terminal yard, by decomposing it into two sub-problems, i.e., the train departure profile optimization and the rolling stock circulation optimization. The first sub-problem synthetically optimizes frequency setting, timetabling and the rolling stock circulation at the terminal without a yard. The maximum headway function is generated to ensure the service of the train operational plan without considering travel demand, then we present a model to minimize the number of train trips, and design a heuristic algorithm to maximize the train headway. On the basis of a given timetable, the rolling stock circulation optimization only involves the terminal with a yard. We propose a model to minimize the number of trains and yard–station runs, and an algorithm to find the optimal assignment of train-trip pair connections is designed. The computational complexities of the two algorithms are both linear. Finally, a real case study shows that the train operational plan developed by our approach enables a better match of train headway and travel demand, and reduces the operational cost while satisfying the requirement of the level of service.     

Train rescheduling model with train delay and passenger impatience time in urban subway network

This paper considers the train rescheduling problem with train delay in urban subway network. With the objective of minimizing the negative effect of train delay to passengers, which is quantified with a weighted combination of travel time cost and the cost of giving up the planned trips, train rescheduling model is proposed to jointly synchronize both train delay operation constraints and passenger behavior choices. Space–time network is proposed to describe passenger schedule‐based path choices and obtain the shortest travel times. Impatience time is defined to describe the intolerance of passengers to train delay. By comparing the increased travel time due to train delay with the passenger impatience time, a binary variable is defined to represent whether the passenger will give up their planned trips or not. The proposed train rescheduling model is implemented using genetic algorithm, and the model effectiveness is further examined through numerical experiments of real‐world urban subway train timetabling test. Duration effects of the train delay to the optimization results are analyzed. Copyright © 2017 John Wiley & Sons, Ltd.     

The existence, uniqueness and computation of an arc-based dynamic network user equilibrium formulation

In this paper, a dynamic user equilibrium traffic assignment model with simultaneous departure time/route choices and elastic demands is formulated as an arc-based nonlinear complementarity problem on congested traffic networks. The four objectives of this paper are (1) to develop an arc-based formulation which obviates the use of path-specific variables, (2) to establish existence of a dynamic user equilibrium solution to the model using Brouwer's fixed-point theorem, (3) to show that the vectors of total arc inflows and associated minimum unit travel costs are unique by imposing strict monotonicity conditions on the arc travel cost and demand functions along with a smoothness condition on the equilibria, and (4) to develop a heuristic algorithm that requires neither a path enumeration nor a storage of path-specific flow and cost information. Computational results are presented for a simple test network with 4 arcs, 3 nodes, and 2 origin–destination pairs over the time interval of 120 periods.     

Optimal feeder bus routes on irregular street networks

The methodology presented here seeks to optimize bus routes feeding a major intermodal transit transfer station while considering intersection delays and realistic street networks. A model is developed for finding the optimal bus route location and its operating headway in a heterogeneous service area. The criterion for optimality is the minimum total cost, including supplier and user costs. Irregular and discrete demand distributions, which realistically represent geographic variations in demand, are considered in the proposed model. The optimal headway is derived analytically for an irregularly shaped service area without demand elasticity, with non‐uniformly distributed demand density, and with a many‐to‐one travel pattern. Computer programs are designed to analyze numerical examples, which show that the combinatory type routing problem can be globally optimized. The improved computational efficiency of the near‐optimal algorithm is demonstrated through numerical comparisons to an optimal solution obtained by the exhaustive search (ES) algorithm. The CPU time spent by each algorithm is also compared to demonstrate that the near‐optimal algorithm converges to an acceptable solution significantly faster than the ES algorithm.