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.     

Locating emergency vehicles with an approximate queuing model and a meta-heuristic solution approach

In this paper, the location of emergency service (ES) vehicles is studied on fully connected networks. Queuing theory is utilized to obtain the performance metrics of the system. An approximate queuing model the (AQM) is proposed. For the AQM, different service rate formulations are constructed. These formulations are tested with a simulation study for different approximation levels. A mathematical model is proposed to minimize the mean response time of ES systems based on AQM. In the model, multiple vehicles are allowed at a single location. The objective function of the model has no closed form expression. A genetic algorithm is constructed to solve the model. With the help of the genetic algorithm, the effect of assigning multiple vehicles on the mean response time is reported.     

放宽条件定点集结模式下编组站车辆 集结排队系统分析

集结模式决定了货车集结过程的结束条件，定点集结是一种高效率的集结方式，有利于提高运输质量.针对放宽条件定点集结模式下编组站车辆集结过程，建立离散时间批到达批服务排队模型，利用嵌入式马尔可夫链方法求得离去时刻瞬时系统集结车辆队长分布，并求得任意时刻车辆集结队长分布，在此基础上分别分析了最小编成辆数，车组大小分布，车流到达强度，服务时间间隔分布对车辆平均集结队长，集结延误时间，效率，一昼夜发送车流量等系统指标的影响.分析结果表明，各因素对车辆集结排队系统影响明显.因此，利用本文提出的模型能为编组站的精细化管理和车流组织优化提供决策参考.     

信息技术市场拓展方法研究

科学技术的发展遵循一定的轨迹,也会为信息技术市场带来必然的改变.面对众多的市场机会和激烈的竞争环境,需要通过科学有效的方法找准市场规律,以新的视角开拓差异化市场,使企业进入可持续发展的良性循环中.本文通过分析3种技术发展趋势,展开讨论了新市场的切入方法,并分享3个典型技术市场案例供读者思考.     

基于多智能体仿真的自动化集装箱码头合理通过能力研究

针对自动化集装箱码头泊位合理通过能力评估问题,基于多智能体仿真建立采用"双小车岸桥(DTQC)+自动导引车(AGV)+自动化轨道吊(ARMG)"工艺的自动化集装箱码头装卸作业模型,对顺岸布置的自动化码头通过能力与码头服务水平、泊位数量之间的关系进行研究。仿真结果表明:码头服务水平(AWTAST)与泊位通过能力呈明显负相关关系,且在一定码头服务水平下,泊位合理通过能力随顺岸式码头泊位数量的增加显著提高。基于仿真结果进行公式拟合,提出了自动化集装箱码头的泊位合理通过能力估算方法,可以为自动化集装箱码头规划与运营提供决策支持。     

Congestion and pollution consequences of driving-to-school trips: A case study in Beijing

Parents compete for high-quality education for their children by enrolling them in good schools. However, in a Chinese mega-city like Beijing, three factors jointly lead to the spatial separation between schools and homes: the centralized public goods provision mechanism, the historical dependency in school location, and the constrained supply of housing in downtown. Without an adequate number of school buses, this spatial separation of schools and homes triggers the numerous long-distance driving-to-school trips by private vehicle during workday morning rush hours in Beijing. We use the start and end dates of “school holiday” as exogenous repeated shocks to the aggregate traffic congestion, and employ the two-stage least squares (2SLS) regression approach to examine the congestion and pollution consequences of such driving-to-school trips in Beijing. We find that, all else being equal, workdays during school holidays have a traffic congestion index 20% lower than that of non-school-holiday workdays. Such a sharp reduction in congestion leads to a significant decrease in PM₁₀ concentration. Policymakers should lower such “extra” congestion and environmental costs via optimizing the spatial balance between school supply and demand.     

Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.     

Liner shipping network design with emission control areas: A genetic algorithm-based approach

To curb emissions, containerized shipping lines face the traditional trade-off between cost and emissions (CO₂ and SO_x) reduction. This paper considers this element in the context of liner service design and proposes a mixed integer linear programming (MILP) model based on a multi-commodity pickup and delivery arc-flow formulation. The objective is to maximize the profit by selecting the ports to be visited, the sequence of port visit, the cargo flows between ports, as well as the number/operating speeds of vessels on each arc of the selected route. The problem also considers that Emission Control Areas (ECAs) exist in the liner network and accounts for the vessel carrying capacity. In addition to using the MILP solver of CPLEX, we develop in the paper a specific genetic algorithm (GA) based heuristic and show that it gives the possibility to reach an optimal solution when solving large size instances.     

Conflict-free arrival and departure trajectory planning for parallel runway with advanced point-merge system

In this paper, an efficient trajectory planning system is proposed to solve the integration of arrivals and departures on parallel runways with a novel route network system. Our first effort is made in designing an advanced Point Merge (PM) route network named Multi-Level Point Merge (ML-PM) to meet the requirements of parallel runway operations. Then, more efforts are paid on finding a complete and efficient framework capable of dynamically modelling the integration of arrival and departure trajectories on parallel runways, modelling the conflict detection and resolution in presence of curved trajectory and radius-to-fix merging process. After that, a suitable mathematical optimization formulation is built up. Receding Horizon Control (RHC) and Simulated Annealing (SA) algorithms are proposed to search the near-optimal solution for the large scale trajectories in routine dense operations. Taking Beijing Capital International Airport (BCIA) as a study case, the experimental results show that our system shows good performances on the management of arrivals and departures. It can automatically solve all the potential conflicts in presence of dense traffic flows. With its unique ML-PM route network, it can realize a shorter flying time and a near-Continuous Descent Approach (CDA) descent for arrival aircraft, an economical climbing for departure aircraft, an easier runway allocation together with trajectory control solutions. It shows a good and dynamic sequencing efficiency in Terminal Manoeuvring Area (TMA). In mixed ML-PM mode, under tested conditions, our proposed system can increase throughput at BCIA around 26%, compared with baseline. The methodology defined here could be easily applied to airports worldwide.