电梯垂直交通系统的配置与优化调度问题研究
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摘要
现代生活快节奏的特点和现代商务活动的高效率要求,及建筑物使用功能的多样化,使电梯垂直交通发生着深刻变化。经典的电梯交通系统的配置、交通分析、交通需求预测与电梯群的调度技术都面临着新的挑战。因此,对电梯垂直交通系统的配置与优化调度问题的研究有重要的理论意义和重大的潜在经济效益。本文以高层建筑电梯垂直交通系统为背景,分别对与此相关的电梯垂直交通系统的配置、电梯交通需求预测与模式识别、电梯群的优化调度问题进行了较为深入和系统的研究,论文完成了如下主要工作:
1.针对上行高峰交通条件,分析了电梯交通系统的性能指标,建立了高层建筑电梯群组分区运行方式的多目标双层规划模型;提出了采用遗传算法求解一类多目标问题的方法,通过决策变量的整体优化,获取目标函数的最优值,克服了传统方法依据偏好确定权值的缺点;通过对最优解进行成本消耗分析,给出了与建筑物特性相关的电梯群优化运行的配置方案,为电梯群系统优化调度目标的实现提供基本条件。
2.针对电梯交通需求的时变性、不确定性的特点,建立了基于LS-SVM(LeastSquares Support Vector Machine)的迭代学习预测模型。该模型不依赖于交通需求的分布,能够动态跟踪交通需求变化规律,通过利用支持向量机技术获得连续的交通需求预测函数;经检验该模型具有良好的预测效果,为建立有效的模式识别方案奠定了基础。
3.在对交通需求可靠预测的基础上,提出了采用交通模式临界点和交通模式临界区间的概念建立电梯交通需求的模式识别方案的方法。分别给出了基于滤波函数的模式识别方案和基于支持向量机技术的模式识别方案,不仅具有理论上的参考价值,而且将交通模式临界点的概念推广为交通模式临界区间,可根据电梯群调度策略切换的动态特性给出确定临界区间的具体方法,具有较好的工程意义。
4.为了有效地实施电梯群的优化调度,需要确知在不同模式下不同时段的交通需求的O-D(Origin and Destination)分布,并依此来为电梯群控系统提供驱动数据。本文提出一种层间客流O-D矩阵的预测方法,将预测得到的O-D分布信息作为先验交通需求,用于电梯群的优化调度。该预测方法融合灰色预测和神经网络方法各自的优点,将灰色预测的方法与RBF神经网络有机结合,构造灰色神经网络预测模型。利用灰色预测技术的累加生成运算(AGO,Accumulated Generating Operation)对原始观测数据进行变换,得到规律性较强的累加数据,作为神经网络的建模和训练样本;同时,提出了对不良交通需求数据的修正方法,以降低观测数据的随机性,既避免了灰色预测方法存在的理论误差,又提高了神经网络的训练速度和预测精度。所得到的数据包含了建筑物真实的客流信息,为一类建筑物的电梯群控系统的优化调度提供了重要客流数据。
5.为了实现电梯群控系统的优化调度,本文提出了考虑多个评价指标,包括:平均候梯时间、平均行程时间、平均服务时间、超过约定时间的长候梯率及停站次数等,并基于这些评价指标构造多目标函数的方案。为求解电梯群的多目标优化调度问题,提出了一种改进的微粒群算法(IPSO,Improved PSO),通过求解最优的Hamilton cycles,实现了在主要交通模式下电梯群的多目标优化调度,从而有效地改善了系统的性能指标,提高了电梯交通系统的综合服务水平。
The characteristics of the fast rhyme of the modern life, the high efficiency requirements of modern business transactions and the diversities function of the building service make the vertical passenger traffic undergo a potential change. The classical methods of the traffic design, analysis, forecast and elevator group dispatching of elevator traffic system, are facing unprecedented challenges. So, the research on traffic configuring and optimal dispatching of elevator traffic system are of important theory significance with enormous potential economic benefit.
In this dissertation, taking the elevator vertical traffic system in high rise buildings as the background, the relatively thorough and systematic study of the problems related with which including the problems of elevator traffic configuring, the elevator traffic demand forecasting, elevator traffic pattern recognition and the optimal dispatching of elevator group are conducted respectively in details. The main studies and obtained results in this dissertation are as follows:
1. In view of the up peak traffic condition, the performance indices of elevator traffic system are analyzed and based on which the bi-level programming mathematical mode with multiple-objective of elevator banking in high rise buildings is established; A method employing genetic algorithm to solve a kind of multi-objective question is proposed, in which the objective function is optimized by globally optimizing the decision variables. The presented approach overcomes the drawback of the conventional method of determining weight value by preference in dealing with linear combination weighted method; The final optimal configuring scheme of elevator banking is achieved by analyzing the cost caused by the layout of elevator banks in the given high rise building for each obtained optimal solution, which provides the basic conditions for implementing the optimal dispatching of the elevator group control system.
2. Aiming at the properties of time-variant and uncertain of elevator traffic demand in high rise buildings, the prediction model based on the LS-SVM (Least Squares Support Vector Machine) with iterative learning is established, the proposed model is independent of the distribution of traffic demand and it can dynamically track the varying regularity of traffic demand. Mean while, the continuous forecasting function of traffic demand is obtained by employing the LS-SVM; The inspection results show that the new model has a fairly ideal prediction effect which lay a foundation for establishing the efficient scheme of recognizing elevator traffic patterns.
3. Based on the reliable prediction of elevator traffic demand, the method of establishing schemes for recognizing elevator traffic pattern by using the proposed concepts of critical points and the critical time ranges of elevator traffic pattern is presented. Two schemes for recognizing elevator traffic patterns are conducted in this dissertation, one is the filter function based method of pattern recognition with very important reference value in theory, and the other is the least squares support vector machine based method with better engineering significance in which the concept of critical points of elevator traffic pattern are extended to the critical time ranges which can be determined according to the dynamical property of dispatching strategy switching.
4. In order to efficiently implement the optimal dispatching of elevator group, the Origin and Destination distribution ("O-D distribution" for short) of passengers traffic under different traffic patterns is needed to drive elevator group control system. In this dissertation, a prediction method of passengers' O-D matrix is proposed where the predicted information of O-D distribution is taken as the prior traffic demand information employed to conduct the optimal dispatching of elevator group. The novel method employs the merits of both grey theory and neural network by introducing the grey forecasting technique to RBF neural network to construct GM-RBFNN. By conducting the conversion of the accumulated generating operation (AGO) on the initial observed traffic data, the sample data which are exponentially distributed for modeling and training GM-RBFNN are obtained; Meanwhile, a method of modifying abnormal initial observed traffic data is presented to further reduce the randomness of observed traffic data. The proposed method not only avoid the theoretical error of grey model, but enhanced both the training speed greatly and the precision of prediction of the neural network. The predicted O-D distribution including the actual traffic information provides the important traffic data for the optimal dispatching of elevator group control system for a class of high-rise buildings.
5. In order to realize the optimal dispatching of elevator group control system, in this dissertationseveral, multi-assessment indices are considered including average waiting time (AWT), average riding time (ART), average service time (AST), the average number of stops(ANS) and the average long waiting percent (LWP), etc, based on which the method of constructing multi-objective function is conducted. To solve the multi-objective optimal dispatching problem of elevator group , an improved particle swarm optimization algorithm(IPSO, Improved PSO) is proposed. The optimal dispatching of elevator group with multi-objective under the main traffic patterns is realized by finding the optimum Hamilton cycles based on minimizing the multi-objective function. So the system performance and the comprehensive service level are efficiently improved.
1.针对上行高峰交通条件,分析了电梯交通系统的性能指标,建立了高层建筑电梯群组分区运行方式的多目标双层规划模型;提出了采用遗传算法求解一类多目标问题的方法,通过决策变量的整体优化,获取目标函数的最优值,克服了传统方法依据偏好确定权值的缺点;通过对最优解进行成本消耗分析,给出了与建筑物特性相关的电梯群优化运行的配置方案,为电梯群系统优化调度目标的实现提供基本条件。
2.针对电梯交通需求的时变性、不确定性的特点,建立了基于LS-SVM(LeastSquares Support Vector Machine)的迭代学习预测模型。该模型不依赖于交通需求的分布,能够动态跟踪交通需求变化规律,通过利用支持向量机技术获得连续的交通需求预测函数;经检验该模型具有良好的预测效果,为建立有效的模式识别方案奠定了基础。
3.在对交通需求可靠预测的基础上,提出了采用交通模式临界点和交通模式临界区间的概念建立电梯交通需求的模式识别方案的方法。分别给出了基于滤波函数的模式识别方案和基于支持向量机技术的模式识别方案,不仅具有理论上的参考价值,而且将交通模式临界点的概念推广为交通模式临界区间,可根据电梯群调度策略切换的动态特性给出确定临界区间的具体方法,具有较好的工程意义。
4.为了有效地实施电梯群的优化调度,需要确知在不同模式下不同时段的交通需求的O-D(Origin and Destination)分布,并依此来为电梯群控系统提供驱动数据。本文提出一种层间客流O-D矩阵的预测方法,将预测得到的O-D分布信息作为先验交通需求,用于电梯群的优化调度。该预测方法融合灰色预测和神经网络方法各自的优点,将灰色预测的方法与RBF神经网络有机结合,构造灰色神经网络预测模型。利用灰色预测技术的累加生成运算(AGO,Accumulated Generating Operation)对原始观测数据进行变换,得到规律性较强的累加数据,作为神经网络的建模和训练样本;同时,提出了对不良交通需求数据的修正方法,以降低观测数据的随机性,既避免了灰色预测方法存在的理论误差,又提高了神经网络的训练速度和预测精度。所得到的数据包含了建筑物真实的客流信息,为一类建筑物的电梯群控系统的优化调度提供了重要客流数据。
5.为了实现电梯群控系统的优化调度,本文提出了考虑多个评价指标,包括:平均候梯时间、平均行程时间、平均服务时间、超过约定时间的长候梯率及停站次数等,并基于这些评价指标构造多目标函数的方案。为求解电梯群的多目标优化调度问题,提出了一种改进的微粒群算法(IPSO,Improved PSO),通过求解最优的Hamilton cycles,实现了在主要交通模式下电梯群的多目标优化调度,从而有效地改善了系统的性能指标,提高了电梯交通系统的综合服务水平。
The characteristics of the fast rhyme of the modern life, the high efficiency requirements of modern business transactions and the diversities function of the building service make the vertical passenger traffic undergo a potential change. The classical methods of the traffic design, analysis, forecast and elevator group dispatching of elevator traffic system, are facing unprecedented challenges. So, the research on traffic configuring and optimal dispatching of elevator traffic system are of important theory significance with enormous potential economic benefit.
In this dissertation, taking the elevator vertical traffic system in high rise buildings as the background, the relatively thorough and systematic study of the problems related with which including the problems of elevator traffic configuring, the elevator traffic demand forecasting, elevator traffic pattern recognition and the optimal dispatching of elevator group are conducted respectively in details. The main studies and obtained results in this dissertation are as follows:
1. In view of the up peak traffic condition, the performance indices of elevator traffic system are analyzed and based on which the bi-level programming mathematical mode with multiple-objective of elevator banking in high rise buildings is established; A method employing genetic algorithm to solve a kind of multi-objective question is proposed, in which the objective function is optimized by globally optimizing the decision variables. The presented approach overcomes the drawback of the conventional method of determining weight value by preference in dealing with linear combination weighted method; The final optimal configuring scheme of elevator banking is achieved by analyzing the cost caused by the layout of elevator banks in the given high rise building for each obtained optimal solution, which provides the basic conditions for implementing the optimal dispatching of the elevator group control system.
2. Aiming at the properties of time-variant and uncertain of elevator traffic demand in high rise buildings, the prediction model based on the LS-SVM (Least Squares Support Vector Machine) with iterative learning is established, the proposed model is independent of the distribution of traffic demand and it can dynamically track the varying regularity of traffic demand. Mean while, the continuous forecasting function of traffic demand is obtained by employing the LS-SVM; The inspection results show that the new model has a fairly ideal prediction effect which lay a foundation for establishing the efficient scheme of recognizing elevator traffic patterns.
3. Based on the reliable prediction of elevator traffic demand, the method of establishing schemes for recognizing elevator traffic pattern by using the proposed concepts of critical points and the critical time ranges of elevator traffic pattern is presented. Two schemes for recognizing elevator traffic patterns are conducted in this dissertation, one is the filter function based method of pattern recognition with very important reference value in theory, and the other is the least squares support vector machine based method with better engineering significance in which the concept of critical points of elevator traffic pattern are extended to the critical time ranges which can be determined according to the dynamical property of dispatching strategy switching.
4. In order to efficiently implement the optimal dispatching of elevator group, the Origin and Destination distribution ("O-D distribution" for short) of passengers traffic under different traffic patterns is needed to drive elevator group control system. In this dissertation, a prediction method of passengers' O-D matrix is proposed where the predicted information of O-D distribution is taken as the prior traffic demand information employed to conduct the optimal dispatching of elevator group. The novel method employs the merits of both grey theory and neural network by introducing the grey forecasting technique to RBF neural network to construct GM-RBFNN. By conducting the conversion of the accumulated generating operation (AGO) on the initial observed traffic data, the sample data which are exponentially distributed for modeling and training GM-RBFNN are obtained; Meanwhile, a method of modifying abnormal initial observed traffic data is presented to further reduce the randomness of observed traffic data. The proposed method not only avoid the theoretical error of grey model, but enhanced both the training speed greatly and the precision of prediction of the neural network. The predicted O-D distribution including the actual traffic information provides the important traffic data for the optimal dispatching of elevator group control system for a class of high-rise buildings.
5. In order to realize the optimal dispatching of elevator group control system, in this dissertationseveral, multi-assessment indices are considered including average waiting time (AWT), average riding time (ART), average service time (AST), the average number of stops(ANS) and the average long waiting percent (LWP), etc, based on which the method of constructing multi-objective function is conducted. To solve the multi-objective optimal dispatching problem of elevator group , an improved particle swarm optimization algorithm(IPSO, Improved PSO) is proposed. The optimal dispatching of elevator group with multi-objective under the main traffic patterns is realized by finding the optimum Hamilton cycles based on minimizing the multi-objective function. So the system performance and the comprehensive service level are efficiently improved.
引文
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