多目标进化算法在船舶设计中的应用

针对船舶概念设计阶段船型参数优化问题,根据快速性、操纵性、舱容衡准及基于经验公式建立船舶优化数学模型,采用改进的非支配解排序的多目标进化算法（NSGA-Ⅱ）,以推进系统总效率、舰船经验系数、相对回转直径及船舶造价4个参数为优化目标,选取若干设计变量,对50 000 DWT油船进行多目标优化.数值算例表明,改进的非支配解排序的多目标进化算法能够有效、客观地选择合理的船舶主尺度参数.     

自升式平台桩靴结构优化设计

结合有限单元法和尺寸优化理论,对某自升式海洋平台的桩靴结构进行了设计与优化。根据桩靴设计要求确定各部件尺寸和相应材料参数,以此建立桩靴的有限元模型。针对CCS对桩靴提出的工况依次进行分析计算,获得各工况下应力与位移的分布特征和土壤作用力在结构中的传递路径。然后进行板厚尺寸的优化。计算结果表明,桩靴在偏心工况且偏心角度为60°时应力达到最大,且土壤流失可能性最高;尺寸优化后,桩靴的质量明显减少,应力分布更加均匀,材料利用率得到有效提高。     

27000 DWT多用途船设计

多用途船以其分舱灵活、装载货物种类多、货物装卸方便等优势,得到国内外以散杂货运输为主的船东青睐.介绍27 000 DWT多用途船的主要参数和总体布置情况,并对其EEDI能效设计指数、主机配置优化、线型优化设计、货舱布置、舷侧设计和防海盗措施进行了探讨.     

典型船舶悬臂基座阻抗特性研究

基于阻抗分法探索悬臂型基座的机械阻抗特性,借助有限元软件数值分析典型船体悬臂型基座的阻抗特性,以及基座各结构参数对其机械阻抗的影响规律;在此基础上,基于阻抗失配原理开展了悬臂型基座结构参数优化研究,最终形成一套较准确的船体基座阻抗分析方法,且具有较好的工程应用价值。     

大跨度隧道施工核心岩柱宽度优化研究

依托重庆轨道交通六号线红土地暗挖车站,利用有限元软件计算不同核心岩柱宽度下,围岩应力,支护结构内力,围岩塑性应变和拱顶沉降随开挖工序的变化。大量计算数据表明,深埋大跨度隧道双侧壁导坑法施工过程中,核心岩柱的宽度存在一个最优值,建议取为隧道跨度的0.3倍,该结论对其他类似的大跨度隧道双侧壁导坑法施工中核心岩柱宽度的留设具有借鉴意义。     

浅谈高速公路排水的优化完善

通过介绍平榆高速公路修建中,排水优化完善的过程和所采用的处理方法,分析了处理路基排水问题和处理已有水利管网与路基相交叉时采用的方法,处理问题时应注意的主要事项和关键时间,浅谈了高速公路排水优化完善的方法,为高速公路的排水设计和施工提出几点建议。     

基于粒子群的城市交通信号两级模糊控制

针对城市交通流的特点,设计一种单交叉口多相位两级模糊控制器,有效地减少控制规则数,实现相序、绿信比、周期随交通状况而自适应变化,并采用粒子群算法对模糊控制器的隶属度函数进行优化。仿真结果表明,该系统能有效地提高交叉口的通行能力,减少车辆平均延误。     

三峡水库航运优化调度蓄水方案研究

文章通过研究分析水库蓄水过程对坝上下游河段演变的影响,并根据汛末长江来水过程,对三峡枢纽汛末蓄水的起蓄时间、起蓄水位及下泄流量进行了研究,提出了合理的优化调度方案。该方案可降低三峡枢纽汛末蓄水对坝上回水变动区河段淤积的不利影响．提高对坝下河段浅滩的冲刷效果,并保证三峡水库按时蓄满。     

Ground delay program planning under uncertainty in airport capacity

Abstract

This paper presents an algorithm for assigning flight departure delays under probabilistic airport capacity. The algorithm dynamically adapts to weather forecasts by revising, if necessary, departure delays. The proposed algorithm leverages state-of-the-art optimization techniques that have appeared in recent literature. As a case study, the algorithm is applied to assigning departure delays to flights scheduled to arrive at San Francisco International Airport in the presence of uncertainty in the fog clearance time. The cumulative distribution function of fog clearance time was estimated from historical data. Using daily weather forecasts to update the probabilities of fog clearance times resulted in improvement of the algorithm's performance. Experimental results also indicate that if the proposed algorithm is applied to assign ground delays to flights inbound at San Francisco International airport, overall delays could be reduced up to 25% compared to current level.     

Disruption Management of an Inequality-Based Multi-Fleet Airline Schedule by a Multi-Objective Genetic Algorithm

Abstract

This paper presents a novel application of a Method of Inequality-based Multi-objective Genetic Algorithm (MMGA) to generate an efficient time-effective multi-fleet aircraft routing algorithm in response to the schedule disruption of short-haul flights. It attempts to optimize objective functions involving ground turn-around times, flight connections, flight swaps, total flight delay time and a 30-minute maximum delay time of original schedules. The MMGA approach, which combines a traditional Genetic Algorithm (GA) with a multi-objective optimization method, can address multiple objectives at the same time, then explore the optimal solution. The airline schedule disruption management problem is traditionally solved by Operations Research (OR) techniques that always require a precise mathematical model. However, airline operations involve too many factors that must be considered dynamically, making a precise mathematical model difficult to define. Experimental results based on a real airline flight schedule demonstrate that the proposed method, Multi-objective Optimization Airline Disruption Management by GA, can recover the perturbation efficiently within a very short time. Our results further demonstrate that the application can yield high quality solutions quickly and, consequently, has potential to be employed as a real-time decision support tool for practical complex airline operations.