考虑故障车回收的共享单车静态重平衡问题

共享单车系统在运行中会出现站点供需不平衡以及故障单车回收困难的情况,对人们的出行造成影响。针对该问题,提出了对共享单车系统同时进行重平衡和回收故障车的策略,建立了以最小化行驶成本和未满足好车需求的惩罚成本为目标函数的混合整数规划模型,并设计了相应的改进遗传算法进行求解。数值实验将该改进算法与gurobi在小、中、大三种规模算例下进行比较,验证了该改进算法有较好性能。最后对影响目标函数的因素进行了灵敏度分析,为减少共享单车系统运营成本提供了依据。     

政府监管对共享单车发展的影响——基于出行大数据的挖掘与分析

共享单车作为一种环保便捷的智能交通工具,由于过量投放、无序占道和运维错配等突出问题,严重影响公共秩序,政府监管势在必行。然而,政府严格监管对共享单车发展有何影响,一直在理论研究和社会实践中存在诸多争议。基于此,通过对成都中心区摩拜单车用户出行数据进行挖掘和分析,从政府、出行者和运营商视角,评估现行政府监管实施效果。研究表明,政府监管后,尽管出行意愿受到一定程度抑制,运维难度加大,但共享单车的公共属性得以强化,共享单车行业竞争格局得以调整,将有利于共享单车发展潜力的释放。     

“互联网+”下共享单车的管理方法研究

共享单车作为一种新兴的公共交通方式,在给人们带来许多便利的同时,也给整个社会带来了很多不可忽视的问题。本文描述了目前共享单车所面临的问题,并提出在"互联网+"、大数据以及众多高新科技背景下的改进措施。利用这些技术对共享单车从管理运营模式和技术方法两方面进行改进,以达到解决现阶段共享单车所面临的问题的目的。     

基于GeoHash与聚类的共享单车动态回收点设置方法研究

如何获取损坏车辆的相关信息并建立合理的回收物流系统已经成为共享单车企业良好运营的关键所在。通过对共享单车骑行空间分布和骑行数据的聚类分析建立了包括聚类回收点、初始回收站和回收处理中心的共享单车回收逆向物流网络路径规划及选址规划模型,以北京摩拜单车为例,发现热力图及区域网格化数据分析可以展现共享单车骑行的空间分布特性,通过聚类分析得到聚类回收点位置的方法具有很好的实用性和可操作性,能够为物流回收模型构建提供有效数据支撑,为共享单车企业提供一定的方法参考。     

考虑需求可拆分的共享单车调度优化研究

研究考虑需求可拆分的共享单车调度优化问题为可拆分单商品取送货TSP问题，考虑一辆调度车，允许调度车多次访问各站点，每次满足站点的部分需求，即允许对站点的需求进行拆分。首先，考虑到调度车容量限制，统筹安排调度车行驶路径和调度车在每个站点的取车量、送车量，使得企业的运营成本达到最优。其次，提出了一种改进的变邻域搜索算法求解上述问题，使算法在陷入局部最优解时改变邻域结构，扩大搜索范围，以此提升算法跳出局部最优解的能力，加快收敛速度。最后，用数值实验验证了算法的有效性。研究结论拓展了可拆分单商品取送货问题的相关理论，并为共享单车企业的实际运营提供决策支持。     

共享单车发展中存在的问题及其对策研究

随着互联网的不断发展,共享经济出现在人们的视野中.在共享经济模式下,共享单车为人们的出行带来了极大的便利,同时在社会的发展、环境保护等方面也体现了其重要的作用.但共享经济模式下共享单车的发展存在很多的问题,本文在对共享单车发履历程中进行分析的基础上,找出现存的问题并提出相应的解决对策,以强化共享单车的管理策略,保障共享单车的稳健发展.     

基于深度强化学习的微电网储能调度策略研究

文章研究了基于深度强化学习的微电网储能调度策略,如果场景的电价方式不同,利用强化学习算法,可以将模型的自主性充分发挥出来,结合学习环境信息,确定最优调度策略。     

浅谈共享经济背景下共享单车的发展现状

从去年开始,越来越多的共享单车出现在我们的视野中.共享单车的发展速度超乎我们的想象.但是其在给人们带来方便的同时,也给城市的管理带来了很大的挑战.基于此,笔者对共享单车的现状、单车的利弊和盈利模式进行分析,同时针对问题提出了相应的对策和建议.     

共享单车的运营与管理优化研究

本文介绍了共享单车的研究现状,阐述了共享单车运营与管理存在的问题,乱停乱放,投放数量不合理.并以内蒙古工业大学校园为例,研究了共享单车的选址优化与运营管理.确立并量化了评价指标,通过校园实际数据进行了相应计算,进行了选址优化与方案确立.研究成果对于共享单车的规划与管理有一定的指导意义.     

共享单车使用者满意度调查研究 ——基于摩拜与小黄车对比实证分析

用户满意度是影响客户选择的重要因素,如何稳定客源,增强用户粘性是当今各大单车企业关注的重点问题。本文试图构建共享单车顾客满意度的评价指标体系,并且评价体系包含4个共享单车满意度的影响因素:自身体验性、用户成本、用车便利性、沟通性。在此基础上设计共享单车满意度调查问卷,对比分析小黄车和摩拜的使用情况,分析了共享单车存在的问题并提出相应的建议。     

A location-routing model for free-floating shared bike collection considering manual gathering and truck transportation

With the booming of free-floating bike-sharing systems globally, collecting target bikes effectively becomes increasingly more important in practice. Although many existing studies deal with the transportation of shared bikes with trucks, the process of manual gathering scattered target bikes along truck transportation is seldom explicitly addressed. This study tackles an integrated free-floating shared bike collection problem, which includes manually moving the target bikes to selected gathering sites and transporting the gathered bikes back to the depot with trucks. We propose a location-routing model to optimize the gathering site locations, the bike gathering assignments, and the truck delivering routes integrally under the goal of minimizing a total operation cost, and we develop a heuristic approach to solve the large-size instance of our model. A case study, which is based on the real data of Mobike company in Beijing, China, is conducted to verify the effectiveness of our planning model and solution approach and to generate managerial insights and suggestions for better practices of free-floating shared bike collection.     

What Makes a Good Cargo Bike Route? Perspectives from Users and Planners

Cargo bikes—bicycles made to carry both goods and people—are becoming increasingly common as an alternative to automobiles in urban areas. With a wider and heavier body, cargo bikes often face problems even in the presence of cycling infrastructure, thus limiting their possibilities of route choice. Infrastructure quality and the route choices of cyclists have been well studied, but often solely based on a quantitative approach, leading to tools such as BLOS (bicycle level of service). With various designs of cargo bikes being used for a wide range of purposes, the route choice of cargo bike users is difficult to generalize. This study combines quantitative and qualitative approaches in order to explore what is important for cargo bike users’ route choice, and how this knowledge can be effectively used for planning. Our results suggest that while some general preferences exist, route choice involves complex dynamics that cannot be fully explained by quantitative measures alone: in addition to understanding “what” is important for cargo bike users, we need to understand “why” it is important. Furthermore, route choice is also influenced by the city context, making a study tailored to the local context essential.     

基于动态数据驱动的半导体生产线调度仿真

针对半导体晶圆生产的特点,研究了基于动态数据驱动的半导体生产调度仿真系统。系统通过实时采集半导体生产线上的动态数据监控生产过程,并且根据仿真数据和生产数据的差异,及时识别扰动,驱动系统重调度仿真。系统通过混合知识表示形成基于半导体生产调度因素、调度策略和调度方法的知识网络,运用JESS实现动态调度策略实时推理。并且利用多Agent强大的模型描述能力和分布式问题求解能力提高仿真的实时性。仿真结果表明,系统具有很好的实时性和动态适应性,能够保持生产的稳定运行。     

Evaluation of scheduling techniques for solving flowshop problems with no intermediate storage

The flowshop scheduling problem with no intermediate storage (NIS problem) was studied in this research. This problem, a modification of the classical flowshop scheduling problem, arises when a set of jobs, once started, must be processed with no wait between consecutive machines. By eliminating the need for intermediate storage, reduction of capital investment in work-in-process inventory can be achieved. This approach can be practically applied to a steel mill, in which the metal should be continuously processed in order to maintain high temperature, as well as many other similar processes.To provide insight into selecting an appropriate scheduling technique for solving the NIS problem, six methods were compared in terms of the quality and efficiency of the scheduling solutions they produced. The quality of solution was measured by makespan and the efficiency of solution was measured by the computational time requirements. The six methods examined in this study included: the Gupta algorithm, the Szwarc algorithm, an integer linear programming method, the Campbell et al. algorithm, the Dannenbring rapid access with extensive search algorithm, and a mixed integer linear programming procedure.The problem factors considered in this study were number of jobs, number of machines, and range of processing times. Relatively small-sized problems were tested with up to ten jobs, five machines, and 1–100 processing time units. Six solution techniques were selected and compared, with respect to makespan and computational time requirements, for multiple combinations of the three problem variables.The resulting test data were investigated using graphical procedures and formal statistical analyses. Initially, plots of mean values were used to graphically compare the six solution methods for the two performance criteria. Next, a multivariate analysis of variance study was conducted to investigate the quality and efficiency of the algorithms with respect to the problem factors. Then, a multiple comparison procedure was employed to analyze treatment mean differences among the six solution techniques. Results from the statistical analyses are summarized in this article.It was concluded that the two mathematical programming methods, the integer linear programming procedure and the mixed integer linear programming methods, produced the best performance in terms of makespan. These two methods, however, used a far greater amount of computational time than the other four solution techniques. Producing moderately good results as far as quality of performance, the Gupta and the Szwarc algorithms were comparable with the Campbell et al. and the Dannenbring algorithms in terms of computational efficiency. By comparison, the Campbell et al. and the Dannenbring algorithms produced the poorest performance with respect to the quality of solutions.Certain limitations were imposed for this study. The problem size considered was relatively small and the sample size was also limited to ten problems per cell. In addition, a uniform distribution function was used for generating processing times within certain ranges. These limitations were necessary in order to allow the various scheduling problems to be solved within a reasonable amount of computer time.Finally, some suggestions were provided for future research in the NIS problem area. The integer linear programming method was recommended as a standard of evaluation, owing to its best overall performance. A possible area for future research would involve the improvement of the Gupta and the Szwarc algorithms through the use of backtracking procedures within the branch-and-bound technique, so that they might be competitive with the mathematical programming methods with respect to quality of performance. Other distribution functions could be investigated in terms of the influence of the distribution of processing times on the performance measures.     

一种解决配送规划问题的改进节约算法

车辆优化调度问题（VSP）是物流配送中广泛存在的一类问题，VSP问题属于NP一困难问题。在描述了简单VSP模型的基础上，对启发式算法中的C-W节约算法进行改进，将AK算法的思想运用其中，使计算结果的优化程度明显提高。     

基于协同演化算法的物流配送车辆路径问题求解

针对物流配送车辆调度模型求解难的问题,以差异演化算法为基础,提供一种竞争型的协同演化算法来求解物流配送车辆路径模型．最后通过实例检验模型和算法的有效性,仿真结果表明协同差异演化算法求解物流配迭车辆路径模型具有一定的优越性。     

卷烟厂制丝车间自动排产问题研究

以卷烟厂为研究背景,对制丝车间生产特点和排产需求进行分析,提出了基于规则的启发式算法,以解决卷烟厂制丝车间排产问题。以生产均衡为目标,构建以工段为整体的倒序排产的制丝车间排产算法,并将该算法应用于某卷烟厂。经实际数据验证,该算法可很好地满足制丝排产的需求。     

基于PDDL智能规划的车辆路径问题研究

针对车辆路径问题和多种扩展问题,文章提出一种基于PDDL智能规划的建模和求解方法,给出了多种车辆路径问题的PDDL模型,并利用SGPlan6和DaeYa规划器对PDDL模型进行了求解。实验结果表明,该算法能够在较短时间内给出高质量的车辆路径安排方案,并具备很好的扩展能力。     

基于启发式SVM的入侵检测系统研究

文章针对支持向量机参数一直存在根据经验确定不足的问题,提出将启发式支持向量机快速学习算法应用于入侵检测系统中。为了使支持向量分类机获得更好的分类性能,该算法提出以启发式规则选取对分类器最有利的样本进行训练,以确定支持向量机的参数,并采用内积矩阵分解算法提高分类速度,达到提高学习速度的目的。实验表明该算法在入侵检测系统中的应用优于标准支持向量机算法。     

基于SP的模拟退火算法在装箱问题中的应用

在传统模拟退火算法的基础上,对装箱问题的优化算法进行了研究。结合装箱问题的具体特点,采用SP序列对来描述模块间的拓扑关系,并通过精细的模拟退火策略(精细的降温策略,提前退出策略),以及增量式的新解评估策略对算法进行优化。试验结果表明,相比传统的模拟退火框架,改进的算法能够取得较好的运行时间与面积利用率。