基于本体和进化算法的散杂货港口堆场智能调度系统研究
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摘要
随着经济一体化、全球化趋势的发展,我国国民经济和对外贸易迅速增加,散杂货运输量呈现较高的增长态势。快速上升的散杂货输运要求和客户需求的日趋多样化在给港口企业带来机遇的同时也给港口服务能力带来了极大的挑战。因此,如何利用港口现有的设施资源,采用现代化物流管理模式,增强港口信息化建设和应用水平,以缓解港口吞吐量压力,提高装卸作业效率、服务能力将成为我国散杂货港口进一步发展的关键。
堆场调度是港口企业生产作业的核心业务之一,特别对于我国南方散杂货港口,由于堆场资源极度紧缺以及货物堆存地点选择的多样性,根据货物的属性和堆场特点,合理进行堆位分配可以有效提高港口作业效率和优势堆场利用率,降低港口作业复杂度和转栈作业量。本文面向多限制条件下的南方散杂货港口堆场多目标优化调度问题,以广东省教育部产学研项目《广州港集团生产业务管理系统及通用软件产业》(2008B090500244)、《基于RFID的港口汽车滚装管理系统应用示范工程》(2009B090300467)和国家自然科学基金重点项目《物流资源整合与调度优化研究》(71132008)等为支持,深入分析了以广州港集团为代表的我国南方散杂货港口堆场调度的特点和现状,综合应用本体、本体推理、神经网络和遗传算法等理论和方法,设计并构建了基于本体和进化算法的散杂货港口堆场智能调度系统,主要研究内容和成果如下：
(1)提出了基于本体和进化算法的散杂货港口堆场智能调度系统框架
本文在对我国南方散杂货港口堆场调度业务规则、优化目标充分分析的基础上,针对目前港口堆场调度过分依赖人工经验,缺乏优化标准、计划性和反馈机制等问题,提出了基于本体和进化算法的散杂货港口堆场智能调度系统(Ontology and Evolutionary Algorithm Based Bulk-Port Stack-Scheduling System, OEABSS)体系框架,采用定性推理和定量计算相结合的方式,解决多限制条件、多影响因素下的堆场多目标优化调度问题。应用本体作为系统的知识描述语言,并实现基于本体的货物和堆场约束推理；应用神经网络和遗传算法等进化方法实现堆场智能调度系统中的疏运量预测和堆位分配问题求解。
(2)提出了基于多项改进BP神经网络的散杂货港口月疏运量预测模型
本文在对反向传播(BP)神经网络存在问题以及改进策略深入分析的基础上,根据散杂货港口月疏运量预测问题不确定性高、样本波动大等特点,从样本选择和预处理、BP网络结构确认、权值阈值初始化、网络训练仿真等多个方面引入多种优化策略,提出了一种基于多项改进BP神经网络的预测模型构建方法(Multi-Improved BP-ANN Forecast Model Building,MBPFB),为堆位智能分配提供依据。通过应用补偿策略、比对法、遗传算法、滑动窗口法等多种优化策略和改进方法,较好地解决了预测过程中由于样本波动、训练顺序等原因而引起的过拟合问题,提高了神经网络算法的收敛效率,以及模型对样本以外数据的预测精度。
(3)提出了港口综合物流交叉领域本体构建和集成方法
本文在对已有的本体构建和集成方法深入研究的基础上,以实现港口堆场调度推理作为本体构建的最终目标,针对港口综合物流交叉领域本体数据源分散、知识完备性要求低等特点,提出了交叉领域本体构建的4项原则,并在此基础上提出了一种自顶向下的基于本体集成的港口综合物流交叉领域本体构建方法IDOBM (Intersect Domain Ontology Building Method)。并针对其中交叉领域本体集成问题,提出了交叉领域本体集成体系框架,以及基于目标关联度的港口综合物流交叉领域本体集成方法IDOIM (Intersect Domain Ontology Integration Method)。该本体构建和集成方法可以有效提高交叉领域本体构建效率,降低构建复杂度,克服传统本体构建方法中现有本体重用困难和共享词表难以建立等问题。
(4)提出了基于改进NSGAⅡ算法的复杂多目标优化问题求解方法
本文在对多目标优化问题求解方法充分研究的基础上,针对散杂货港口堆位分配问题搜索空间大、限制条件复杂、影响因素众多等特点,结合港口综合物流本体,提出了基于改进NSGAⅡ算法(带精英策略的快速非支配排序遗传算法)的多目标优化方法。通过应用约束限制矩阵、随机修复算子以及基于遗传代数的自适应交叉、变异概率等改进方法,提高了算法的运算效率和收敛性,并更好地保持了解的多样性。
(5)实现了OEABSS原型系统开发
在前文研究的基础上,应用J2EE-MVC、Hibernate和Spring联合技术框架,融合RCP、Protege、Jess等关键技术,完成了OEABSS原型系统的构建,实现了基础数据管理、堆位匹配度评价和堆场智能调度等核心功能。
With the development of economic integration and the trend of globalization, rapid growth has been seen in our economy, foreign trade as well as the volume of bulk freight. The increasing requirements of bulk freight and the diversity of clients' demands not only bring opportunities to port enterprises, but also pose great challenges to the port services. Therefore, it becomes critical to further development of bulk port that how to use the existing port facilities by the adoption of modern logistics management mode to enhance the construction and application of port information to ease the pressure of port throughput, to improve the efficiency of operations of loading and uploading and service of bulk port.
Stack-Scheduling is one of the core businesses of port enterprises, especially for the southern bulk port of China. Due to the extremely scarce resources of stack and diversity stockpiling site of different cargo, proper distribution of stacks could improve port utilization, increase the efficiency of overall operations of port and waterway logistics, and lower the cost of operations to better satisfy the clients. Based on the scheduling complexity of southern bulk port of China with multi objectives and restrictions, and with references to The Operation Management System and General Software Industry of Guangzhou Port Group (2008B090500244), The Application Trial Project Based on Motor Ro-Ro Management System of RFID Port (2009B090300467), and The Optimization Research of Integration and Scheduling of Logistics Resources (71132008), which is a key program of National Natural Science Foundation, this dissertation conducted a deep analysis of the features and status quo of stack-scheduling of southern bulk port of China by a comprehensive use of theories and methodologies of ontology, ontology reasoning, BP-ANN and genetic algorithm, designed and built a smart scheduling system of bulk port stack based on ontology and evolutionary algorithm. And the main research contents and results are as follo wings:
(1) Framework of scheduling system of bulk port based on ontology and evolutionary algorithm
Based on operating rules of stack-scheduling of southern bulk port of China and optimization of objectives, and considering the problems of stack-scheduling over dependent on workers'experiences, lack of optimization standards, planning and feedback mechanism, this dissertation initiated Ontology and Evolutionary Algorithm Based Bulk-Port Stack-Scheduling System (OEABSS) with the method of qualitative reasoning and quantitative calculation to solve the problem of multi-objective optimization scheduling of bulk port under the condition of many restrictions and influencing factors. Ontology was used in this system to achieve restriction reasoning of ontology-based cargo and stack. BP-ANN and genetic algorithm was applied for the settlement of complicated problems of traffic forecasting and stack distribution.
(2) Mode of forecasting the traffic of bulk port based on multi-improved BP-ANN.
Based on the analysis of the existing problems and improvements of BP-ANN, in accordance with the features of traffic forecasting of bulk port, and from the aspects of sample selection, pre-treatment, the confirmation of BP structure, the initialization of weights and thresholds, and network training simulation, etc. this dissertation initiated Multi-Improved BP-ANN Forecast Model Building (MBPFB). With the application of various optimization methods and improvements including compensation strategy, comparison, genetic algorithm and sliding windows, this dissertation better resolved the over-fitting problems easily coming up in the process of forecasting, enhanced the capacity of nonlinear forecasting of handling uncertainty in ANN processing and sample fluctuation, which has a wild generalization.
(3)Intersect domain ontology building and integration methodology
Based on the existing ontology building and integration methodology and taking the features of intersect domain ontology in to consideration, this dissertation initiated4principles of intersect domain ontology building and Intersect Domain Ontology Building from above to below. For intersect domain ontology integration, this dissertation initiated cross domain ontology integration system structure and Intersect Domain Ontology Integration Method. This ontology building and integration methodology served to improve the efficiency of cross domain ontology building, lower the complexity of building, solve the re-use difficulty of ontology, and largely realize the sharing of ontology.
(4) Method for solving problems of complicated multi-objective optimization based on improving NSGAII
Based on the research of methods for solving problems of multi-objective optimization and in accordance with the huge searching space, restrictions and influencing factors of bulk port, this dissertation initiated the multi-objective optimization method based on improved NSGAII. Through the application of improved methods of restrictions on matrix, random repaired operators, adaptive crossover based on genetic algebra and mutation rate, the capacity of multi-objective optimization was enhanced under the condition of many restrictions and influencing factors to avoid algorithm from local optima, which increased algorithm convergence efficiency.
(5) Development of OEABSS prototype system
Based on former research, this dissertation achieved the building of OEABSS prototype system and realized the core functions of basic date management, stack matching evaluation and smart stack-scheduling by applying joint technology framework of J2EE-MVC, Hibernate and Spring and integrating key technology of RCP, Protege and Jess.
堆场调度是港口企业生产作业的核心业务之一,特别对于我国南方散杂货港口,由于堆场资源极度紧缺以及货物堆存地点选择的多样性,根据货物的属性和堆场特点,合理进行堆位分配可以有效提高港口作业效率和优势堆场利用率,降低港口作业复杂度和转栈作业量。本文面向多限制条件下的南方散杂货港口堆场多目标优化调度问题,以广东省教育部产学研项目《广州港集团生产业务管理系统及通用软件产业》(2008B090500244)、《基于RFID的港口汽车滚装管理系统应用示范工程》(2009B090300467)和国家自然科学基金重点项目《物流资源整合与调度优化研究》(71132008)等为支持,深入分析了以广州港集团为代表的我国南方散杂货港口堆场调度的特点和现状,综合应用本体、本体推理、神经网络和遗传算法等理论和方法,设计并构建了基于本体和进化算法的散杂货港口堆场智能调度系统,主要研究内容和成果如下：
(1)提出了基于本体和进化算法的散杂货港口堆场智能调度系统框架
本文在对我国南方散杂货港口堆场调度业务规则、优化目标充分分析的基础上,针对目前港口堆场调度过分依赖人工经验,缺乏优化标准、计划性和反馈机制等问题,提出了基于本体和进化算法的散杂货港口堆场智能调度系统(Ontology and Evolutionary Algorithm Based Bulk-Port Stack-Scheduling System, OEABSS)体系框架,采用定性推理和定量计算相结合的方式,解决多限制条件、多影响因素下的堆场多目标优化调度问题。应用本体作为系统的知识描述语言,并实现基于本体的货物和堆场约束推理；应用神经网络和遗传算法等进化方法实现堆场智能调度系统中的疏运量预测和堆位分配问题求解。
(2)提出了基于多项改进BP神经网络的散杂货港口月疏运量预测模型
本文在对反向传播(BP)神经网络存在问题以及改进策略深入分析的基础上,根据散杂货港口月疏运量预测问题不确定性高、样本波动大等特点,从样本选择和预处理、BP网络结构确认、权值阈值初始化、网络训练仿真等多个方面引入多种优化策略,提出了一种基于多项改进BP神经网络的预测模型构建方法(Multi-Improved BP-ANN Forecast Model Building,MBPFB),为堆位智能分配提供依据。通过应用补偿策略、比对法、遗传算法、滑动窗口法等多种优化策略和改进方法,较好地解决了预测过程中由于样本波动、训练顺序等原因而引起的过拟合问题,提高了神经网络算法的收敛效率,以及模型对样本以外数据的预测精度。
(3)提出了港口综合物流交叉领域本体构建和集成方法
本文在对已有的本体构建和集成方法深入研究的基础上,以实现港口堆场调度推理作为本体构建的最终目标,针对港口综合物流交叉领域本体数据源分散、知识完备性要求低等特点,提出了交叉领域本体构建的4项原则,并在此基础上提出了一种自顶向下的基于本体集成的港口综合物流交叉领域本体构建方法IDOBM (Intersect Domain Ontology Building Method)。并针对其中交叉领域本体集成问题,提出了交叉领域本体集成体系框架,以及基于目标关联度的港口综合物流交叉领域本体集成方法IDOIM (Intersect Domain Ontology Integration Method)。该本体构建和集成方法可以有效提高交叉领域本体构建效率,降低构建复杂度,克服传统本体构建方法中现有本体重用困难和共享词表难以建立等问题。
(4)提出了基于改进NSGAⅡ算法的复杂多目标优化问题求解方法
本文在对多目标优化问题求解方法充分研究的基础上,针对散杂货港口堆位分配问题搜索空间大、限制条件复杂、影响因素众多等特点,结合港口综合物流本体,提出了基于改进NSGAⅡ算法(带精英策略的快速非支配排序遗传算法)的多目标优化方法。通过应用约束限制矩阵、随机修复算子以及基于遗传代数的自适应交叉、变异概率等改进方法,提高了算法的运算效率和收敛性,并更好地保持了解的多样性。
(5)实现了OEABSS原型系统开发
在前文研究的基础上,应用J2EE-MVC、Hibernate和Spring联合技术框架,融合RCP、Protege、Jess等关键技术,完成了OEABSS原型系统的构建,实现了基础数据管理、堆位匹配度评价和堆场智能调度等核心功能。
With the development of economic integration and the trend of globalization, rapid growth has been seen in our economy, foreign trade as well as the volume of bulk freight. The increasing requirements of bulk freight and the diversity of clients' demands not only bring opportunities to port enterprises, but also pose great challenges to the port services. Therefore, it becomes critical to further development of bulk port that how to use the existing port facilities by the adoption of modern logistics management mode to enhance the construction and application of port information to ease the pressure of port throughput, to improve the efficiency of operations of loading and uploading and service of bulk port.
Stack-Scheduling is one of the core businesses of port enterprises, especially for the southern bulk port of China. Due to the extremely scarce resources of stack and diversity stockpiling site of different cargo, proper distribution of stacks could improve port utilization, increase the efficiency of overall operations of port and waterway logistics, and lower the cost of operations to better satisfy the clients. Based on the scheduling complexity of southern bulk port of China with multi objectives and restrictions, and with references to The Operation Management System and General Software Industry of Guangzhou Port Group (2008B090500244), The Application Trial Project Based on Motor Ro-Ro Management System of RFID Port (2009B090300467), and The Optimization Research of Integration and Scheduling of Logistics Resources (71132008), which is a key program of National Natural Science Foundation, this dissertation conducted a deep analysis of the features and status quo of stack-scheduling of southern bulk port of China by a comprehensive use of theories and methodologies of ontology, ontology reasoning, BP-ANN and genetic algorithm, designed and built a smart scheduling system of bulk port stack based on ontology and evolutionary algorithm. And the main research contents and results are as follo wings:
(1) Framework of scheduling system of bulk port based on ontology and evolutionary algorithm
Based on operating rules of stack-scheduling of southern bulk port of China and optimization of objectives, and considering the problems of stack-scheduling over dependent on workers'experiences, lack of optimization standards, planning and feedback mechanism, this dissertation initiated Ontology and Evolutionary Algorithm Based Bulk-Port Stack-Scheduling System (OEABSS) with the method of qualitative reasoning and quantitative calculation to solve the problem of multi-objective optimization scheduling of bulk port under the condition of many restrictions and influencing factors. Ontology was used in this system to achieve restriction reasoning of ontology-based cargo and stack. BP-ANN and genetic algorithm was applied for the settlement of complicated problems of traffic forecasting and stack distribution.
(2) Mode of forecasting the traffic of bulk port based on multi-improved BP-ANN.
Based on the analysis of the existing problems and improvements of BP-ANN, in accordance with the features of traffic forecasting of bulk port, and from the aspects of sample selection, pre-treatment, the confirmation of BP structure, the initialization of weights and thresholds, and network training simulation, etc. this dissertation initiated Multi-Improved BP-ANN Forecast Model Building (MBPFB). With the application of various optimization methods and improvements including compensation strategy, comparison, genetic algorithm and sliding windows, this dissertation better resolved the over-fitting problems easily coming up in the process of forecasting, enhanced the capacity of nonlinear forecasting of handling uncertainty in ANN processing and sample fluctuation, which has a wild generalization.
(3)Intersect domain ontology building and integration methodology
Based on the existing ontology building and integration methodology and taking the features of intersect domain ontology in to consideration, this dissertation initiated4principles of intersect domain ontology building and Intersect Domain Ontology Building from above to below. For intersect domain ontology integration, this dissertation initiated cross domain ontology integration system structure and Intersect Domain Ontology Integration Method. This ontology building and integration methodology served to improve the efficiency of cross domain ontology building, lower the complexity of building, solve the re-use difficulty of ontology, and largely realize the sharing of ontology.
(4) Method for solving problems of complicated multi-objective optimization based on improving NSGAII
Based on the research of methods for solving problems of multi-objective optimization and in accordance with the huge searching space, restrictions and influencing factors of bulk port, this dissertation initiated the multi-objective optimization method based on improved NSGAII. Through the application of improved methods of restrictions on matrix, random repaired operators, adaptive crossover based on genetic algebra and mutation rate, the capacity of multi-objective optimization was enhanced under the condition of many restrictions and influencing factors to avoid algorithm from local optima, which increased algorithm convergence efficiency.
(5) Development of OEABSS prototype system
Based on former research, this dissertation achieved the building of OEABSS prototype system and realized the core functions of basic date management, stack matching evaluation and smart stack-scheduling by applying joint technology framework of J2EE-MVC, Hibernate and Spring and integrating key technology of RCP, Protege and Jess.
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