Analysis of an integrated maximum covering and patrol routing problem

In this paper, we address the problem of determining the patrol routes of state troopers for maximum coverage of highway spots with high frequencies of crashes (hot spots). We develop a specific mixed integer linear programming model for this problem under time feasibility and budget limitation. We solve this model using local and tabu-search based heuristics. Via extensive computational experiments using randomly generated data, we test the validity of our solution approaches. Furthermore, using real data from the state of Alabama, we provide recommendations for (i) critical levels of coverage; (ii) factors influencing the service measures; and (iii) dynamic changes in routes.     

Stochastic optimization models for the airport gate assignment problem

Uncertainties inherent in the airport traffic may lead to the unavailability of gates for accommodating scheduled flights. Incorporating random disruptions is crucial in constructing effective flight-gate assignments. We consider the gate assignment problem under uncertainty in flight arrival and departure times and develop stochastic programming models incorporating robustness measures based on the number of conflicting flights, idle and buffer times. The proposed models are formulated as large-scale mixed-integer programming problems and tabu search algorithms are implemented to obtain assignments of reasonable quality. We conduct a computational study to analyze the proposed alternate models and show the effectiveness of the solution methods.     

Global intermodal tank container management for the chemical industry

The scale of the global chemical industry is enormous: in 2003, the total value of global production exceeded US$1.7 trillion. International logistics is especially crucial to the high-value chemicals industry, since raw materials sources, production facilities, and consumer markets are distributed globally. Fluctuating demand, imbalanced trade flows, and expensive transportation equipment necessitate dynamic asset management. This paper focuses on asset management problems faced by tank container operators, and formulates an operational tank container management problem as a large-scale multi-commodity flow problem on a time-discretized network. By integrating container routing and repositioning decisions in a single model, total operating costs and fleet sizes can be reduced. A computational study verifies this hypothesis.     

单车场大规模车辆路径优化问题研究

针对传统优化技术在解决大规模车辆路径问题中存在的缺陷,提出了一种解决单车场大规模车辆路径优化问题的综合启发式算法。首先,采用Sweep技术将区域分解成几个子区。其次,设计了分区的禁忌搜索算法,并采用相邻区域综合优化技术,提高了算法的全局搜索能力。仿真试验表明,该算法能够有效解决大规模车辆路径优化问题。     

The effect of multi-scenario policies on empty container repositioning

This paper addresses a container maritime-repositioning problem where several parameters are uncertain and historical data are useless for decision-making processes. To address this problem, we propose a time-extended multi-scenario optimization model in which scenarios can be generated taking into account shipping company opinions. We then show that multi-scenario policies put shipping companies in the position of satisfying empty-container demands for different values that may be taken by uncertain parameters.     

Container fleet sizing and empty repositioning in liner shipping systems

This paper considers the joint container fleet sizing and empty container repositioning problem in multi-vessel, multi-port and multi-voyage shipping systems with dynamic, uncertain and imbalanced customer demands. The objective is to minimize the expected total costs including inventory-holding costs, lifting-on/lifting-off costs, transportation costs, repositioning costs, and lost-sale penalty costs. A simulation-based optimization tool is developed to optimize the container fleet size and the parameterized empty repositioning policy simultaneously. The optimization procedure is based on Genetic Algorithms and Evolutionary Strategy combined with an adjustment mechanism. Case studies are given to demonstrate the results.     

禁忌搜索算法在编组站调机运用计划中的应用

在分析论述调机运用计划编制方法的基础上,提出应用禁忌搜索算法进行编组站调机运用计划的编制。分别以最小化延迟解体列车和编组列车加权数量为目标建立数学模型,以解编顺序作为优化对象,设计禁忌搜索算法对其进行求解,并以解体顺序为例,采用两两交换(2-opt)方式构建邻域,以该操作前后列车解体顺序的变化作为禁忌对象构建禁忌表,利用软件编程实现模型计算,并通过算例验证该算法的可行性和有效性。     

运用学习规则求解编组站静态配流问题的研究

编组站静态配流问题需要制定配流方案,明确出发列车的编组内容和车流来源。算法的思路是通过构建网络模型,将静态配流问题转化为固定费用的产销平衡运输问题,并将目标函数转化为求最小虚拟到达列车车辆数。首先设定虚拟到达列车并对其赋初值,把出发列车分为可欠轴与不可欠轴两类,在计算过程中调用学习规则保证出发列车满轴,最后求出虚拟到达列车的最小值,得到配流方案。通过简单的算例验证表明,该算法能够在有效的时间内求解大规模的静态配流问题,为静态配流问题提供一种新的方法。