The design of bus route systems — An interactive location-allocation approach

An interactive modelling approach is developed to solve the practical problem of bus route network design. Possible bus routes are identified with facilities which can be located. Zones or pairs of zones in the urban area are identified with customers who will be allocated to the established facilities. It is shown that the classical Set Covering Problem is useful under the assumption of fixed demand; the Simple Plant Location Problem is effective under the assumption of demand which is sensitive to the level of bus service provided.     

试论铁路职业卫生管理与技术服务支持

广州铁路集团公司是我国第一家铁路运输企业集团,职业卫生工作一直是企业卫生工作的重点,职业卫生管理和职业卫生技术服务也是企业履行社会责任、建立＂以人为本＂的现代企业制度的重要组成。通过分析铁路行业职业危害特点和职业卫生管理方面存在的问题,结合长期从事职业卫生技术服务工作的实践,提出加强与改进铁路企业职业卫生工作的对策,旨在助于促进《职业病防治法》在企业的全面贯彻落实。     

既有铁路扩能改造若干问题探讨

研究目的:根据新的技术政策、新的标准规范等要求,通过对既有线扩能改造中出现的与新的标准规范不相适应的若干问题的分析研究、对既有线通行双层集装箱的合理性及增建二线后改变维修体制的探讨,提出了觖决既有线扩能改造工程若干问题的基本思路。研究结论:对于既有线扩能改造工程的一些问题,应根据项目的特点,在本着避免既有线大拆大改、节省工程投资的原则下,结合铁路新的技术发展方向、新的标准规范要求,采取具体问题具体分析,对于新建线路应不折不扣地执行新的标准规范,对既有线改建则应充分考虑到既有线的现状特点,在满足项目功能需求的情况下,应尽可能地与新的标准、规范相适应,并应考虑未来的发展需要。     

多货物品种集装化运输的优化调度问题

用数学模型描述了供应商根据某一用户在各个不同时间段内对不同货物的需求量情况,利用各种不同型号的箱体进行分时段货物发送的问题。其最终目的是为了求得能够使运输成本和存储成本达到最小的货物发送方案,该问题是一个由批量问题和拼箱问题所组成的复合问题。就此问题,提出了一种三阶段算法。通过理论分析,该算法可以有效地解决此类问题。     

自动化散货码头装卸作业存在的问题与对策

针对目前国内已投入运营的自动化散货码头在装卸机械设备配置、功能区域划分、安全管理等方面存在的问题进行分析。提出针对性的解决对策,以进一步提高自动化散货码头的装卸作业效率并确保作业安全。对自动化散货码头的技术发展进行展望,供自动化散货码头建设或升级改造时参考和借鉴。     

Travel time estimation from sparse floating car data with consistent path inference: A fixed point approach

Estimation of urban network link travel times from sparse floating car data (FCD) usually needs pre-processing, mainly map-matching and path inference for finding the most likely vehicle paths that are consistent with reported locations. Path inference requires a priori assumptions about link travel times; using unrealistic initial link travel times can bias the travel time estimation and subsequent identification of shortest paths. Thus, the combination of path inference and travel time estimation is a joint problem. This paper investigates the sensitivity of estimated travel times, and proposes a fixed point formulation of the simultaneous path inference and travel time estimation problem. The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times assumptions and data sizes. Validation against actual path travel time measurements from the Google API and an instrumented vehicle deployed for this purpose shows that the fixed point algorithm improves shortest path finding. The results highlight the importance of the joint solution of the path inference and travel time estimation problem, in particular for accurate path finding and route optimization.     

Estimation of origin-destination matrices from link counts and sporadic routing data

Estimation of origin-destination (OD) matrices from link count data is a challenging problem because of the highly indeterminate relationship between the observations and the latent route flows. Conversely, estimation is straightforward if we observe the path taken by each vehicle. We consider an intermediate problem of increasing practical importance, in which link count data is supplemented by routing information for a fraction of vehicles on the network. We develop a statistical model for these combined data sources and derive some tractable normal approximations thereof. We examine likelihood-based inference for these normal models under the assumption that the probability of vehicle tracking is known. We show that the likelihood theory can be non-standard because of boundary effects, and provide conditions under which such irregular behaviour will be observed in practice. For regular cases we outline connections with existing generalised least squares methods. We then consider estimation of OD matrices under estimated and/or misspecified models for the probability of vehicle tracking. Theoretical developments are complemented by simulation experiments and an illustrative example using a section of road network from the English city of Leicester.     

基于多旅行商问题的应急设施服务区划分模型

基于多旅行商问题，增设集散中心需求及应急服务设施资源容量约束条件，以最小化遍历区域内全部集散中心的综合旅行时间成本为优化目标，构建一种应急设施服务区划分模型，确定各应急设施的服务区范围.设计一种复合算法求解模型，首先基于P-中值选址模型的优化理念，形成初始方案；继而加入禁忌搜索算法，结合LKH求解器对模型进行迭代优化求得最优解.基于宁波市北仑区实际拓扑网络进行案例分析，验证了模型和求解方法的有效性.     

Solving the gate assignment problem through the Fuzzy Bee Colony Optimization

In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers’ total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.     

An optimal data-splitting algorithm for aircraft scheduling on a single runway to maximize throughput

In this research, we present a data-splitting algorithm to optimally solve the aircraft sequencing problem (ASP) on a single runway under both segregated and mixed-mode of operation. This problem is formulated as a 0–1 mixed-integer program (MIP), taking into account several realistic constraints, including safety separation standards, wide time-windows, and constrained position shifting, with the objective of maximizing the total throughput. Varied scenarios of large scale realistic instances of this problem, which is NP-hard in general, are computationally difficult to solve with the direct use of commercial solver as well as existing state-of-the-art dynamic programming method. The design of the algorithm is based on a recently introduced data-splitting algorithm which uses the divide-and-conquer paradigm, wherein the given set of flights is divided into several disjoint subsets, each of which is optimized using 0–1 MIP while ensuring the optimality of the entire set. Computational results show that the difficult instances can be solved in real-time and the solution is efficient in comparison to the commercial solver and dynamic programming, using both sequential, as well as parallel, implementation of this pleasingly parallel algorithm.