Application of system dynamics in analyzing the carrying capacity of water resources in Yiwu City,China

A risk assessment model for water shortage is constructed using a risk analysis method based on the information diffusion theory. The application of this model is demonstrated in the city of Yiwu in Zhejiang Province, China. Based on the analytical results from a small sample, this study indicates that the present model is more stable and effective than the traditional model. Risk assessment results are used to analyze the carrying capacity of water resources from an ecological angle. For this study, the carrying capacity of water resources is defined as the maximum volume of water suitable for supporting human activity in certain stages of social development that can be borne by water resources under favorable ecological conditions. Further study on Yiwu is also performed, with results indicating that water shortages in this city are not related to types of water source, but can be classified in terms of water quality and conservation. To verify the results of theoretical investigation in this paper, the authors simulate changes in the carrying capacity of water resources under the conditions of future water management policies. This simulation uses the system dynamics (SD) model, based on the historical data collected by the city over the past 20 years and governmental plans to raise inhabitants’ living standards between the present and 2020. The paper simultaneously indicates that both singularly pursuing fast economic development at the expense of the environment and promoting environmental protection via reduced economic development are undesirable for Yiwu. Simultaneously giving consideration to both the economic development and environmental protection is likely to produce better overall results. However, if the present water supply level is maintained but does not increase in the near future, Yiwu's water supply will be unable to satisfy requirements even under this scheme. In this case, the carrying capacity of water resources in the region can only be effectively improved by promoting more efficient use of water and water conservation schemes, as well as strengthening long-term investment in environmental protection.     

Pareto蚁群算法与遥感技术耦合的水资源优化配置

为了尝试用Pareto蚁群算法(PACA)和遥感技术(RS)来求解复杂的水资源优化配置问题,建立了以经济、社会和生态环境综合效益最大为目标,以供水、需水、水质等为约束条件的基于像元的水资源优化配置模型.通过局部信息素强度限制、全局信息素动态更新、Pareto解集过滤器构建等策略,使蚂蚁向信息素浓度大的优化边界移动,以提高PACA的全局搜索能力和收敛速度.以中原地区某县为仿真对象,借助RS获取其土地利用类型,利用PACA在栅格地图上求解水资源优化配置模型,并得到水资源最优配置方案.最后PACA与遗传算法(GA)和BP神经网络算法(BP-ANN)进行了比较.结果表明,PACA能有效地求解大范围、多目标水资源优化配置模型,并提高了算法的全局搜索能力、收敛速度和计算结果的精度.     

基于人工蜂群算法的灌区水资源优化配置方法

灌区水资源优化配置对于提高灌溉的水资源利用率,增加灌区经济效益,降低地下水的消耗速度,促进水资源可持续发展,建立良好的生态环境具有重要意义。综合考虑灌区有效降雨量与作物需水量,建立以灌区经济效益最大化为目标的水资源优化配置模型,采用人工蜂群算法对水资源优化配置模型进行求解,以江苏省宿迁市皂河灌区为实例,对基于人工蜂群算法的灌区水资源优化配置方法的实际应用效果进行验证。结果表明：相比传统经验式配水方法,所提出的灌区水资源优化配置方法可使灌区经济效益达到 8.33 亿元,总用水量减少约 1 000 万 m³ ,且用水量主要来自地表水,灌区作物水资源分配情况更加合理,同时灌区的作物种植结构也得以改善。     

面向移动社交云的资源分配机制

移动社交云是结合了移动云和社交云的一种新型模式,它可以为用户提供安全可靠的资源分享平台.在传统的资源分配中,移动用户从远程数据中心获取资源的时间开销大,显著地降低了用户的体验质量,与此同时也极少考虑用户之间根据社交属性建立的信任关系,从而导致交易机制存在一定信用风险和交易用户信誉度低的问题.为此,在考虑用户的社交效益和经济效益的基础上提出了一种新型的移动社交云资源分配机制.首先,利用改进Gale-Shapley算法为买方在其朋友圈中匹配出合适的卖方,促进社交朋友间资源的共享.其次,利用多对多的买方多标密封拍卖算法,对未成功匹配的用户再次分配资源,最大化利用空闲的移动云资源.最后,实现了提出的资源分配机制,仿真结果表明,与激励与拍卖兼容的移动云资源分配机制相比,提出的资源分配机制在价格满意度、社交信誉满意度和资源交易成功率方面具有更好的性能.     

四网协同资源配置关键影响因素探讨

四网协同作为中国移动通信集团三大战略之一,是实现可持续发展的重要基础。对四网协同资源配置的关键影响因素进行探讨分析,对于建立四网协同资源配置模型,实现四网协同战略落地意义重大。本文在四网大协同的战略背景下,对影响四网协同资源配置的关键影响因素进行探讨分析,从而为搭建体系化、标准化、可操作的四网协同资源配置模型提供更科学的指标。基于该模型,可以实现中国移动通信集团公司及各省公司的精细化管理工作,提升投资管理效益。     

资源分配算法的比较和研究

许多领域都涉及资源分配问题,怎样合理的把各种有限的经济资源分配给企业内各生产部门,使得本企业在相对较低的成本投入下得到较大利润是每一个企业所追求的目标。于是就产生了如何分配以使工程目标或生产目的达到最优的问题。本文针对这一类资源分配问题,阐述了用动态规划方法和多段图方法的求解思想,并通过实例比较了两种算法。     

An improved whale optimization algorithm for forecasting water resources demand

Water demand forecasting can promote the rational use of water resources and alleviate the pressure on water demand. By analyzing the use of water resources, this paper establishes three models of water demand forecasting, logarithmic model, linear and exponential combination model and linear, exponential and logarithmic hybrid models. In order to accurately estimate the demand for water resources, an improved whale optimization algorithm based on social learning and wavelet mutation strategy is proposed. The new algorithm designs a new linear incremental probability, which increases the possibility of global search of the algorithm. Based on the social learning principle, the social ranking and social influence are used to construct the social network for the individual, and the adaptive neighborhood learning strategy based on the network relationship is established to achieve the exchange and sharing of information between groups. The Morlet wavelet mutation mechanism is integrated to realize the dynamic adjustment of the mutation space, which enhances the ability of the algorithm to escape from local optimization. The latest CEC2017 benchmark functions confirms the superiority of the proposed algorithm. The water consumption from 2004 to 2016 in Shaanxi Province of China is used for the experiment. The results show that the performance of the proposed algorithm for solving the three water resources forecasting model is better in comparison to other algorithms. The prediction accuracy is as high as 99.68%, which verified the validity of the model and the practicality of the proposed algorithm.     

疏勒河流域水资源管理决策支持信息系统设计

世界银行贷款的疏勒河农业综合开发暨移民安置工程的实施,使流域内水土资源得到了极大规模的开发与利用,但同时也产生了湿地萎缩、植被退化、土地沙化和盐渍化等一系列较为严重的水环境问题。为实现流域整体生态、社会、经济效益的统一,保证区域经济与社会的可持续发展,建立科学、可靠、操作性强的流域水资源管理决策支持信息系统是实现上述目标的根本保障。在分析系统需求的基础上,结合疏勒河流域水资源特点对系统的结构、系统功能、系统实现等方面进行了 初步探讨,认为疏勒河流域水资源管理决策支持信息系统应是一个以硬件、系统软件、组织机构以及水资源数据为基础,“3S”技术及水资源管理专业模型为支撑的,集数据的采集、传输、存储、管理、分析、决策、输入、输出为一体的集成系统,以进行疏勒河流域水资源的评价、科学管理和决策。     

基于信任度的合一系统社会任务分配

在多智体社会网络中,传统的任务分配模型一般采用直接面向任务执行者的分配机制.它们不考虑社会网络组织结构对任务分配性能的巨大影响,也很少透彻地研究不可靠社会中的任务分配.针对这些问题,本文开创性地研究了软硬件合一系统的任务分配,即按递阶、分层的思想设计了协作组织模型,并基于此提出了面向社区基于社会协调“软件人”的任务分配模型.模型研究过程中,提出了基于直接信任度和社区声誉的社区信任度评估机制、基于社区信任度和社区物理能力的节点选择机制、基于负载均衡的社区内任务分配机制和基于上下文资源的任务再分配策略.实验结果表明：与常见的直接面向任务执行者和基于资源的任务分配模型相比,所提出的模型具有更优的任务分配性能,且对社会任务环境变化具有更好的鲁棒性;社区内基于负载均衡的分配机制和基于上下文资源的再分配策略也有效提高了分配性能,降低了网络中的通信密度.     

Online merchant resource allocation and matching for open community collaborative manufacturing (OCCM) in mass personalization model

In order to meet the requirements of customers' pursuit of personalized and diversified needs, enterprises need to change from the traditional mass manufacturing model to mass personalization manufacturing model. On the other hand, collaborative manufacturing based on stakeholders will effectively solve the problems of information asymmetry, low operational efficiency and insufficient resource utilization in the process of manufacturing. Moreover, the new model of open community manufacturing is conducive to the planning, allocation, optimization and management of various networked and social resources. Therefore, combined with above three innovative models of mass personalization manufacturing, collaborative manufacturing and open community manufacturing, this paper studies the online merchant resource allocation and matching of open community collaborative manufacturing (OCCM) for mass personalization (MP) model to realize the innovation of design model, improve design efficiency and save design resource cost. Firstly, the workflow for OCCM in MP model based on system engineering analysis method is studied. Secondly, online merchant resource allocation for OCCM based on social network analysis (SNA) and online merchant resource matching for OCCM based on fuzzy clustering are proposed. At last, a case study for OCCM in the automotive industry is given to verify the effectiveness of the proposed model and methods.     

基于议价机制的网格资源分配研究

资源管理和分配是网格计算的核心问题,用市场机制可有效地管理和分配网格资源。提出了一个基于议价机制的网格资源分配模型,设计了资源分配策略。模拟实验表明该模型能够较好地适应网格环境的动态变化,并实现各个网格计算资源的负载平衡。     

Are the Portuguese public hospitals sustainable? A triple bottom line hybrid data envelopment analysis approach

The harmonious interaction between humans and the biosphere determines how sustainable existence and coexistence are. However, when social challenges appear, sustainability is heavily compromised. Health is one of the areas affected by such challenges, with the delivery of health services being ensured by health systems. Consequently, understanding how sustainable health services are, especially those that consume the majority of resources—hospitals—is indispensable for a sustainable future. For this reason, we propose using a hybrid data envelopment analysis (DEA) approach to study hospital sustainability in Portugal under environmental, social, and economic perspectives, in cooperation with the Portuguese Ministry of Health. In particular, the proposed methodology incorporates the preference information of decision makers (via the construction of utility scales and the determination of Möbius coefficients) and criteria interactivity, due to the integration of the Choquet multiple criteria preference aggregation model in the DEA approach. In the end, despite approximately 30% of the sampled 29 assessed hospitals were deemed as efficient across the three perspectives in 2018, only 1 was entirely sustainable.     

一种基于市场机制的计算网格资源分配方法

针对计算网格提出了一种基于市场机制的资源分配方法，以一般均衡理论为基础，依靠市场机制，实现计算网格资源的优化分配，首先，描述了基于代理的资源分配框架，它包括3个 层次：资源层、代理层和用户层；接着，给出了计算网络资源分配的市场模型，其中效用函数用于刻画用户对给定资源的满意程度；然后定义了市场模型的均衡状态并证明了均衡状态撮优性，这意味着在均衡状态下资源分配不仅有效而且公平，最后引入了资源代理的迭代算法。     

Digital twin-driven service model and optimal allocation of manufacturing resources in shared manufacturing

The sharing economy has been recognized a mutually beneficial economic mode. Deriving from the concept of sharing economy, shared manufacturing was proposed under the support of advanced information and manufacturing technologies. As a core part of implementing shared manufacturing, manufacturing resource allocation aims to coordinate cross-organizational resources to provide on-demand services for personalized manufacturing requirements. However, some challenges still hinder effective and efficient resource allocation in shared manufacturing. Traditional centralized optimization methods with only one decision model are difficult to maintain autonomous decision rights of resource providers. Thus, they could hardly adapt to the situation of cross-organizational resource coordination. In addition, the credit of resource providers is rarely considered in the resource allocation process, which is unfavorable for promoting more reliable trades in shared manufacturing. To address these issues, this study proposes an integrated architecture to promote the resource allocation in shared manufacturing. A digital twin-driven service model is built to perform the seamless monitoring and control of shared manufacturing resources. The resource allocation model is constructed based on the consideration of the credit of resource providers. To keep the decision autonomy of resource providers, augment Lagrangian coordination is adopted to analyze the constructed resource allocation model. A case study is further employed to validate the effectiveness and efficiency of the proposed method in performing the resource allocation in shared manufacturing.     

一种基于在线反向拍卖技术的计算网格资源分配方法

利用经济模型研究网格资源管理是当前网格研究新的热点。在已有的网格资源管理方法的基础上,针对供大于求的计算网格环境,提出了一种基于在线反向拍卖技术的计算网格资源分配方法,并定义了相应的QoS函数,分析了该方法的适用范围和优点。最后通过模拟实验验证了该方法的效用,实验结果证明该方法是一种有效的计算网格资源分配方法。     

Resource allocation by genetic algorithm with fuzzy inference

Assuming that a make-to-order manufacturing company has customer orders, the addressed capacity allocation problem is a due-date assignment problem for multiple manufacturing resources. The purpose of this study is to develop an intelligent resource allocation model using genetic algorithm and fuzzy inference for reducing lateness of orders with specific due dates. While the genetic algorithm is responsible for arranging and selecting the sequence of orders, the fuzzy inference module conveys how resources are allocated to each order. Experimental results showed that the proposed model has solved the capacity allocation problem efficiently.     

基于SAC强化学习的车联网频谱资源动态分配

针对车联网频谱资源稀缺问题,提出一种基于柔性致动-评价（SAC）强化学习算法的多智能体频谱资源动态分配方案。以最大化信道总容量与载荷成功交付率为目标,建立车辆-车辆（V2V）链路频谱资源分配模型。将每条V2V链路作为单个智能体,构建多智能体马尔科夫决策过程模型。利用SAC强化学习算法设计神经网络,通过最大化熵与累计奖励和以训练智能体,使得V2V链路经过不断学习优化频谱资源分配。仿真结果表明,与基于深度Q网络和深度确定性策略梯度的频谱资源分配方案相比,该方案可以更高效地完成车联网链路之间的频谱共享任务,且信道传输速率和载荷成功交付率更高。     

A game theory-based dynamic resource allocation strategy in Geo-distributed Datacenter Clouds

Geo-distributed Datacenter Cloud is an effective solution to store, process and transfer the big data produced by Internet-of-Things (IoT). A key challenge in this distributed system is how to allocate the bandwidth resources among these geo-distributed datacenters of this cloud efficiently. This paper aims to address this challenge by optimizing the transfer bandwidth resources among different geo-distributed datacenters. To this end, we firstly analyze the interaction between the traffic of physical networks and the data flow of Geo-distributed Datacenter Clouds, and then establish a game theory-based model for cloud resource allocation. Based on this model, a dynamic resource allocation strategy and its corresponding algorithm that are adaptable to the Internet conditions are proposed. Since the background traffic, capacity limit of physical networks as well as the flows and resource demands of geo-distributed datacenters are taken into account, this new strategy can achieve the load balance of the physical networks and content transferring among different geo-distributed datacenters effectively. The real-world trace data is adopted to validate the effectiveness and efficiency of the proposed resource allocation strategy. Compared with existing strategies, the evaluation results demonstrate that our proposed strategy can balance the workloads of physical networks, reduce the response delay of cloud applications, and possess an excellent adaptability.     

基于蒙特卡罗贝叶斯推理的移动群智感知可靠任务分配机制

针对现有任务分配策略的不足,研究了在工人数量有限的移动群智感知系统中任务分配策略,借助社交网络来分配任务并获得高收益。首先,建立了社交网络的动态不确定环境,利用社交网络完成任务,传播任务。然后考虑到不同社交网络对任务的偏好不同,设置任务偏好度这一不确定指标,借助经济学风险价值的理论描述任务分配的可靠性。最后利用蒙特卡罗贝叶斯推理方法研究任务动态传播模型的复杂参数的高斯过程,设计基于知识梯度的采样算法选择蒙特卡罗采样点,从而实现高收益的任务分配方案。为了验证所提策略的性能,将其与四种基准的采样算法进行比较。实验结果表明,所提任务分配策略在提高收益方面是有效的。