Subjective-utility travel time budget modeling in the stochastic traffic network assignment

ABSTRACT

In this article, we propose a new model called subjective-utility travel time budget (SU-TTB) model to capture travelers' risk-averse route choices. In the travel time budget (TTB) and mean-excess travel time (METT) model, a predefined confidence level is needed to capture the risk-aversion in route choice. Due to the day-to-day route travel time variations, the exact confidence level is hard to be predicted. With the SU-TTB model, we assume travelers' confidence level belongs to an interval that they may comply with in the route choice. The two main components of SU-TTB are the utility function and the TTB model. We can show that the SU-TTB can be reduced to the TTB and METT model with proper utility function for the confidence levels. We can also prove its equivalence with our recently proposed nonlinear-expectation route travel time (NERTT) model in some cases and give some new interpretation on the NERTT with this equivalence. Finally, we formulate the SU-TTB model as a variational inequality (VI) problem to model the risk-averse user equilibrium (RAUE), termed as generalized RAUE (GRAUE). The GRAUE is solved via a heuristic gradient projection algorithm, and the model and solution algorithm are demonstrated with the Braess's traffic network and the Nguyen and Dupuis's traffic network.     

“船舶—堆场—列车”混堆堆场箱位分配模型

集装箱海铁联运港口堆场作业箱型复杂,为了提高港口作业能力和效率,基于进口、出口箱的混合堆存方式,对"船舶-堆场-列车"作业堆场箱位分配问题进行研究."船舶-堆场-列车"作业堆场负责堆存通过船舶、列车及外集卡进出港口的集装箱.本文在待分配箱的作业时间及箱区已知的前提下,以集装箱堆存产生的压箱数最小为目标,建立"船舶-堆场-列车"作业混堆堆场箱位分配模型,根据模型特点设计了启发式算法进行求解,并进行算例分析.结果表明,所建模型和求解算法能够有效减少堆场的压箱数,满足箱型堆存要求,提高港口作业效率.     

序决策逻辑及基于启发式遗传算法的规则挖掘

很多真实世界处理排序问题代替分类问题,例如由不同生产厂商生产的消费品,大学之间的排序等等.一般地,一个全局的信息表需要给定.本文采用了基于Rough集理论的方法来阐述排序规则的挖掘问题,且利用了基于遗传算法的启发式算法来达到算法的实现.     

The Storage Space Allocation Problem at a Container Terminal

Due to the asymmetry between the time required to retrieve a container to vessel and the time required to register a container at the gate, the storage space allocation problem solves the best allocation policy of these containers that minimizes the potential number of gantry movements during the assigning process and the future retrieval process. A decision rule-based heuristic is proposed, and three properties are discovered in this research. Comprehensive numerical experiments show that the proposed heuristic can solve the real-sized instances within a second, making it possible to account for uncertainty because the re-optimization effort is negligible. Moreover, when applying the proposed heuristic to a container yard at the Port of Kaohsiung in Taiwan and considering the extreme cases, the heuristic performs even better when the number of containers increases and still requires less than 1 s to solve these extreme cases. Finally, the proposed heuristic can be applied to different layouts of storage space and various material handling machines.     

Order-first split-second methods for vehicle routing problems: A review

Cluster-first route-second methods like the sweep heuristic (Gillett and Miller, 1974) are well known in vehicle routing. They determine clusters of customers compatible with vehicle capacity and solve a traveling salesman problem for each cluster. The opposite approach, called route-first cluster-second, builds a giant tour covering all customers and splits it into feasible trips. Cited as a curiosity for a long time but lacking numerical evaluation, this technique has nevertheless led to successful metaheuristics for various vehicle routing problems in the last decade. As many implementations consider an ordering of customers instead of building a giant tour, we propose in this paper the more general name of ordering-first split-second methods. This article shows how this approach can be declined for different vehicle routing problems and reviews the associated literature, with more than 70 references.     

求解TSP问题的一种启发式遗传算法

TSP问题是著名的NPC问题，在组合优化中有许多应用。讨论如何应用启发式遗传算法求解此问题，并设计一种启发式交叉算子和换位变异算子，主要特点是给出算子在程序中的实现技巧，提高搜索的速度。经实例分析，算法性能较好，能较快得到问题的满意解。     

Reliable sensor deployment for network traffic surveillance

New sensor technologies enable synthesis of disaggregated vehicle information from multiple locations. This paper proposes a reliable facility location model to optimize traffic surveillance benefit from synthesized sensor pairs (e.g., for travel time estimation) in addition to individual sensor flow coverage (e.g., for traffic volume statistics), while considering probabilistic sensor failures. Customized greedy and Lagrangian relaxation algorithms are proposed to solve this problem, and their performance is discussed. Numerical results show that the proposed algorithms solve the problem efficiently. We also discuss managerial insights on how optimal sensor deployment and surveillance benefits vary with surveillance objective and system parameters (such as sensor failure probabilities).     

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随着中国民航事业的快速发展,航班不正常变得越来越严重.虽然航空运输系统涉及空管、航空公司与机场三方面,但是目前解决不正常航班却单纯依靠空管.而这种方式无法达到减少不正常航班的目的.本文研究在协同决策机制下,航空公司自由指派航班时隙的方法问题.文中建立航班时隙指派模型,针对模型的特点设计了一种参数启发式算法。最后通过实例证明了采用本文的方法可以减少航班总延误时间并且提高航班正点率.通过该实例说明航空公司参与时隙分配决策的重要性.     

一类运输工具带双重能力约束的LRP问题

分析了一类运输工具受双重能力约束的LRP问题，即在物流网络节点最大单批处理能力及运输工具总运输能力双重约束的情况下,如何进行物流中心选址和运输路线安排，使总的费用最小,模型假设网络系统满足如下条件：①节点的最大单批处理能力是逐层变化的，②货物只能用不同运输工具经过某一物流中心进行中转运输．文中建立了混合整数规划模型，提出了一种优化算法，并用优化算法对实例进行求解．     

协同决策机制下航空公司时隙分配优化决策

航空运输系统涉及空管、航空公司与机场三方面,但是目前解决不正常航班却单纯依靠空管。而这种方式无法达到减少旅客延误时间的目标。文中研究在协同决策机制下,航空公司根据航班旅客特性自由指派航班时隙的方法问题。建立了一种航班时隙指派模型,针对模型的特点设计了一种启发式算法。通过实例证明了采用该方法可以减少旅客总延误时间,同时还说明了航空公司参与时隙分配决策的重要性。