Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization

In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm.

Multi-Objective Genetic Algorithm to Design Manufacturing Process Line Including Feasible and Infeasible Solutions in Neighborhood

This paper treats multi-objective problem for manufacturing process design. A purpose of the process design is to decide combinations of work elements assigned to different work centers. Multiple work elements are ordinarily assigned to each center. Here, infeasible solutions are easily generated by precedence relationship of work elements in process design. The number of infeasible solutions generated is ordinarily larger than that of feasible solutions generated in the process. Therefore, feasible and infeasible solutions are located in any neighborhood in solution space. It is difficult to seek high quality Pareto solutions in this problem by using conventional multi-objective evolutional algorithms. We consider that the problem includes difficulty to seek high quality solutions by the following characteristics： （1） Since infeasible solutions are resemble to good feasible solutions, many infeasible solutions which have good values of objective functions are easily sought in the search process, （2） Infeasible solutions are useful to select new variable conditions generating good feasible solutions in search process. In this study, a multi-objective genetic algorithm including local search is proposed using these characteristics. Maximum value of average operation times and maximum value of dispersion of operation time in all work centers are used as objective functions to promote productivity. The optimal weighted coefficient is introduced to control the ratio of feasible solutions to all solutions selected in crossover and selection process in the algorithm. This paper shows the effectiveness of the proposed algorithm on simple model.

A Review of On-Line Machine Scheduling:Algorithms and Competitiveness

在过去的十年里，在线算法的研究吸引了广泛的兴趣．本文对在排序和时间表问题中的各种有效的在线算法以及它们的竞争度作一综述．

Design Optimization of Permanent Magnet Eddy Current Coupler Based on an Intelligence Algorithm

The permanent magnet eddy current coupler(PMEC)solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems.It provides torque to the load and generates heat and losses,reducing its energy transfer efficiency.This issue has become an obstacle for PMEC to develop toward a higher power.This paper aims to improve the overall performance of PMEC through multi-objective optimization methods.Firstly,a PMEC modeling method based on the Levenberg-Marquardt back propagation(LMBP)neural network is proposed,aiming at the characteristics of the complex input-output relationship and the strong nonlinearity of PMEC.Then,a novel competition mechanism-based multi-objective particle swarm optimization algorithm(NCMOPSO)is proposed to find the optimal structural parameters of PMEC.Chaotic search and mutation strategies are used to improve the original algorithm,which improves the shortcomings of multi-objective particle swarm optimization(MOPSO),which is too fast to converge into a global optimum,and balances the convergence and diversity of the algorithm.In order to verify the superiority and applicability of the proposed algorithm,it is compared with several popular multi-objective optimization algorithms.Applying them to the optimization model of PMEC,the results show that the proposed algorithm has better comprehensive performance.Finally,a finite element simulation model is established using the optimal structural parameters obtained by the proposed algorithm to verify the optimization results.Compared with the prototype,the optimized PMEC has reduced eddy current losses by 1.7812 kW,increased output torque by 658.5 N·m,and decreased costs by 13%,improving energy transfer efficiency.

基于可信容量的长期储能充裕性决策分解模型

电力系统发电容量充裕性是表征系统发电能力的一个重要指标,合理调动长期储能充分利用其转移电量的能力,是平衡系统充裕性波动的一个重要方法.针对长期储能年周期内的转移电量分配问题,提出基于可信容量的长期储能充裕性决策分解模型.鉴于新能源机组和常规机组的评估水平不同,利用基于等可靠性指标的新能源可信容量评估将新能源等效为常规机组,以评估新能源机组对电力系统充裕性的贡献.计及强迫停运因素,对机组充裕容量进行调整,并且分析长期储能转移电量对于系统充裕性的影响;根据系统充裕性评估方法,构建以长期储能转移电量为决策变量的长期储能充裕性决策分解模型,并且考虑长期储能的自放电和充放电损耗,建立相关约束条件.考虑到模型非线性分段的特征,采用列队竞争算法对其求解,同时为了克服收缩比选取困难的问题,对列队竞争算法进行改进.设置算例分析电力系统新能源和常规机组对系统发电容量充裕性的贡献程度,验证长期储能充裕性决策分解模型的可行性,对比算法证实了改进列队竞争算法的优越性.

考虑物料配送的混流装配线协同优化

针对混流装配线的平衡排序和物料配送的协同问题,提出了一种装配线同步的协同配送策略,在此基础上以平衡工位负载、最小化最大完工时间和减少物料配送成本为目标,建立了考虑物料配送的协同优化模型。利用改进的非支配排序遗传算法(INSGA)对模型进行求解,基于模型的三层编码设计保证了解集的合法性和完整性。提出的多层编码交叉算子和变异算子,在提高种群多样性的同时也加快了算法的收敛速度。为避免算法陷入局部最优,重新设计了拥挤度距离,并改进了精英选择策略。此外,基于竞争机制,设计了一种舒适度竞争算子,便于在Pareto最优解集中确定具体方案。最后,通过标准算例和具体案例的求解,证明了改进算法的优越性和模型的有效性,案例的优化结果表示,装配线的完工时间缩短了近20%,物料配送成本节省了30%以上,取得了较好的优化效果。

P|rj,on-line|∑C_j的一类在线算法与竞争比分析

本文研究平等机上的在线排序问题,优化目标是使总完工时间最小,算法SSPT是此问题的一类在线算法,论文引入一个拟时间表,此时间表具有SRPT时间表的部分性质,论文通过此辅助时间表证明了SSPT算法是(3-1/m)-competitive的。

两台机器超载实时系统的On-line算法

对超载实时系统的On-line算法中的SR算法作了修改,提出了NSR算法,并证明NSR算法的竞争比至少为2/5,因而它比SR算法更为优异.

基于充裕性的季节性储能合约分解调度模型

为应对电力供需的季节性矛盾,季节性储能(seasonal energy storage, SEE)成为了解决长期新能源消纳问题的关键技术。考虑长期调度的时间尺度要求,以SEE合约电量为衔接点和分割点构建双层模型:上层为电力系统发电容量充裕性裕度模型,利用发电容量充裕性裕度映射电力系统季节性特征,以平衡长周期下充裕性裕度波动为目标构建函数,并采用列队竞争算法进行求解,将季节性储能长期目标分解为若干短期目标;下层为两阶段随机规划模型,采用场景法将风光负荷的不确定性问题转化为确定性问题,利用Cplex进行求解,完成短周期内的精细调度;同时考虑长短期储能协调运行要求,制定基于功率需求和荷电状态的混合储能能量分配方法。构建算例,验证了双层模型应用于含季节性储能的风光火储系统的调度研究的可行性。仿真结果显示,经过双层模型的相互协调,风光火储联合系统的季节性波动显著减小,极差由原始曲线的0.633 9缩减为0.376 0,约缩减了40.68%,整体充裕性水平大幅提高,充裕性裕度的平均值由原始的0.443 4提高为0.637 2,约提高了43.71%。充裕性指标可有效映射中长期系统的供需水平,基于充裕性的季节性储能合约分解调度模型可适用于含季节性储能的风光火储系统,在平抑电力系统季节性波动方面效果显著。

舟山邮轮港国内航线规划研究

邮轮航线网络的规划与布局是优化区域间港口竞合关系、开发差异化旅游产品的关键,对国内邮轮产业的本土化发展至关重要。以舟山国际邮轮港为研究对象,并在全国范围内挑选发展领先的11个沿海邮轮港口作为备选港口,采用因子分析法计算得到港口综合竞争力的定量数值。以邮轮航线竞争力和邮轮公司运营利润为目标构建邮轮航线设计模型,并利用遗传算法求解得到舟山邮轮港国内运营最优航线和配船方案,设计短线、中长线和长线的主题航线产品,以协调优化区域航线网络布局,加强国内沿海港口联动,推进邮轮旅游持续健康发展。

Optimal transmission lines assignment with maximal reliabilities in multi-source multi-sink multi-state computer network

The optimal transmission lines assignment with maximal reliabilities (OTLAMR) in the multi-source multi-sink multi-state computer network (MMMCN) was investigated. The OTLAMR problem contains two sub-problems: the MMMCN reliabilities evaluation and multi-objective transmission lines assignment optimization. First, a reliability evaluation with a transmission line assignment (RETLA) algorithm is proposed to calculate the MMMCN reliabilities under the cost constraint for a certain transmission lines configuration. Second, the non-dominated sorting genetic algorithm II (NSGA-II) is adopted to find the non-dominated set of the transmission lines assignments based on the reliabilities obtained from the RETLA algorithm. By combining the RETLA and the NSGA-II algorithms together, the RETLA-NSGA II algorithm is proposed to solve the OTLAMR problem. The experiments result show that the RETLA-NSGA II algorithm can provide efficient solutions in a reasonable time, from which the decision makers can choose the best solution based on their preferences and experiences.

Multi-Parameter and Multi-Objective Optimization of Occupant Restraint System in Frontal Collision

To solve the constraints of multi-objective optimization of the driver system and high nonlinear problems, according to the relevant dimensions of a car, we build a simulation model with Hybrid Ⅲ 50th dummy driver constraint system. The comparison of the driver mechanics index of the experimental data with the simulation data in the frontal crash shows that the accuracy of simulation model meets the requirements. The optimal Latin test design is adopted, and the global sensitivity analysis of the design parameters is carried out based on the Kriging model. The four most sensitive parameters are selected, and the parameters are solved by a multi-island genetic algorithm.And then the nonlinear programming quadratic line(NLPQL) algorithm is used to search for accurate optimization. The optimal parameters of the occupant restraint system are determined: the limiting force value of force limiter 2 985.603 N, belt extension 12.684%, airbag point explosion time 27.585 ms, and airbag vent diameter 27.338 mm, with the weighted injury criterion(WIC) decreased by 12.97%, the head injury decreased by 22.60%, and the chest compression decreased by 7.29%. The results show that the system integration of passive safety devices such as seat belts and airbags can effectively protect the driver.

具有几何分布统计特征的在线租赁竞争分析

近年来,在线算法的兴起为金融领域的研究提供了新的视角,但传统的竞争分析方法有意规避概率分布假设。在金融领域中,似乎有时忽略这些极有价值的信息而只运用标准的竞争比方法分析显然是一个极大浪费。在本文中,我们首次结合输入结构的分布信息研究了离散型在线租赁问题,建立了最优的离散型在线租赁决策模型,并给出了最优的竞争策略及其竞争比。相比较Karp和El Yaniv的研究结果,由于本文引进了输入的分布信息使得竞争比改善;而相对于Fujiwara的研究结果,由于本文研究了离散型情形,给出了实际问题的精确解。

局内出租车调度与竞争算法

应用复位策略给出了局内ｋ出租车调度问题的竞争算法．

局内车辆选线问题和竞争策略分析

将现实物流配送中所遇到的问题抽象为一个局内车辆选线问题,考虑堵塞点动态产生、一个个遇到的情况下的车辆调度方案.经典的优化理论大多是在已知条件不变的基础上给出最优方案(即最优解),在条件发生变化时就会失去其最优性.而论文所考虑的竞争算法能使得调度方案对于变化因素的每一个特例得到的解离最优方案给出的解总在一定范围之内.不仅设计了解决局内车辆选线问题的竞争算法:贪婪策略和复位策略,分析了不同情况下算法各自的竞争比,而且给出了此问题的竞争比下界.

限制图上的局内出租车调度与竞争算法

经典的优化理论大多是在已知条件不变的基础上给出最优方案（即最优解），其最优性在条件发生变化时就会失去．局内问题与竞争算法则是针对特定的优化问题来研究这样的方法，它在变化因素的每一个特例中都能给出一个方案，使得这一方案所得到的解离最优方案给出的解总在一定的比例之内．本文应用复位策略给出限制图上局内ｋ 出租车调度问题竞争比为１＋ （ｎ－ ｋ）λ的竞争算法．

局内租赁问题的风险补偿模型及其竞争分析

将风险的概念引入局内租赁问题中,建立了该问题的风险补偿模型,并对存在和不存在利率情况下的局内租赁问题作了分析.和局内问题中传统的竞争比分析不同的是,竞争比分析只反映局内策略与基准算法(局外最优算法)的相对绩效,但这往往忽略了很多的有用信息,且分析模型很不灵活.然而在风险补偿模型中,投资者可以控制风险,根据自己不同的风险容忍度和未来预期选择最优的租赁策略.

存在市场利率条件下的占线租赁策略研究

基于占线租赁问题的输入结构简单且具有良好的统计特性,运用竞争分析方法并结合输入结构的分布信息建立最优占线租赁决策模型,突破纯竞争分析有意规避概率分布这一假设条件,分析当存在市场利率因素时的占线租赁策略,并给出最优的竞争策略及其竞争比。相对于Karp及El-Yaniv等人的研究结果,由于引进了输入信息使得竞争比性能分析提高;相对于Fujiwara等人的研究结果,本文研究了存在利率时离散性情形,给出了问题的最优解。

平行机半在线排序问题研究(Ⅰ)

对半在线平行机排序问题的研究进展作了详细综述和进一步探讨.文章给出半在线排序问题的背景、定义、分类和求解.介绍它们定义和在不同机器环境和目标函数下半在线排序问题分类,以及第一类半在线模型的近似算法的设计及其竞争比分析.

基于殖民竞争算法的多约束双边装配线平衡

针对实际生产中双边装配线平衡问题(Two-sided assembly line balancing problem,TALBP)所特有的操作方位约束、优先顺序约束、位置约束、区域约束、同步约束,介绍各个约束的特点及其对任务分配的要求,建立相应的数学模型。根据数学模型,设计相应的殖民竞争算法。在该算法中,对于不符合优先顺序约束的任务系列,提出二叉树调整方法,使任务分配仅在可行解空间内进行,提高了优化效率。将殖民竞争算法(Colonial competitive algorithm,CCA)的全局搜索能力与延迟接受爬山(Late acceptance hill-climbing,LAHC)算法的局部搜索能力有机结合,提出一种新型的混合殖民竞争算法,求解第I类平衡问题。通过多个算例测试验证了算法的有效性。