基于Choquet积分的多属性灰靶群决策方法

针对属性间具有关联性且属性值为区间灰数的多属性群决策问题,提出一种基于Choquet 积分的多属性灰靶群决策方法.根据模糊测度和Choquet积分的性质,定义关于区间灰数的Choquet积分信息集成算子,并证明其相关性质;通过灰靶决策方法,对方案集的综合靶心距进行排序.仿真实例验证了所提出方法的合理性.     

配网三相负荷不平衡自动调度方法

受用户用电不确定性的影响,配电网三相负荷不平衡现象严重,为此,本文提出了一种考虑无功损耗和电压稳定的自动调度方法。该方法构建了无功损耗最小化和电压稳定裕度最大化的多目标调度模型,利用粒子群算法求解模型,避免陷入局部最优,以实现三相负荷不平衡的调度。实验结果表明,该方法具有较强的稳定性和较高的效率,可有效实现配网三相负荷不平衡的自动调度。     

可再生能源电力系统通信负荷平衡化分散调度方法研究

可再生能源接入电力系统后,受到通信负荷调度方案的影响,调度方法的抗干扰性能较差。因此,提出可再生能源电力系统通信负荷平衡化分散调度方法研究。考虑到可再生能源的工作特点,建立可再生能源场景模型。利用模糊控制器,设计电力系统通信负荷控制结构。结合电力系统接入可再生能源后产生的不确定性因素,设计负荷平衡化调度方案,结合上下层级分散调度模式,实现负荷分散协调调度。实验结果表明：所设计的负荷平衡化分散调度方法与文献提出方法相比,将抗干扰性能提升了58%、49%。     

基于属性不完全判断的语言群决策方法

研究多个决策者对属性有不完全类别偏好的语言案例决策方法.基于案例学习框架定义属性公共提及因子,提出考虑多重类别偏好的信息增益系数;基于灰靶决策框架建立综合靶心距最小的属性权重优化模型;基于决策者个体和群体的关联度确定决策者权重,进而给出方案排序.案例分析表明了所提出方法的应用步骤和可行性.     

基于处理能力预约的工作站机群协同调度

本文提出了一种基于处理能力预约的工作站机群协同调度方法,它以一定的处理能力为基础,对参与并行计算的所有工作站进行处理能力的预约,使得并行程序以协调一致的步调得到执行。模拟结果表明,这不仅提高了应用程序所能获得的有效并行计算性能,而且还提高了处理器的利用率,同时它在不同应用环境一的适应性好。     

面积敏捷制造柔性决策的可视化研究

面积敏捷制造柔性决策的可视化实现对提高面积敏捷制造柔性决策的效率和效益具有重要的意义。在简述可视化技术发展的基础上，研究了面向敏捷制造柔性决策的基本的和基于Ｗｅｂ的可视化方法。最后，对面向敏捷制造柔性决策的可视化界面进行了研究。     

基于链式SVG三相不平衡负荷的平衡补偿方法的仿真分析

本文对链式静止无功发生器的工作原理进行了分析,在深入研究三相不平衡负荷的平衡化补偿原理的基础上,提出了一种基于链式静止无功发生器在不平衡补偿时的分相补偿控制方法.该方法是通过对静止无功发生器各相输出电压与电网电压的相角差δ进行调节来控制各相输出电流,从而可以有效地对三相负载不平衡的系统进行平衡补偿.对所提出的补偿方法进行了仿真研究,仿真结果表明运用分相控制方法补偿三相不平衡负载,具有比较好的稳态补偿效果,补偿后电网电流三相平衡,各相电流与电压基本同相位并且幅值成相同的比例,达到了负载功率平衡和无功补偿的目的.     

消纳大规模风电的多目标负荷平衡化分散调度方法

当前风电发力不能满足人们日常生活需求,使用传统调度模型存在风电出力与预测值相差较大情况,基于此,提出了消纳大规模风电的多目标负荷平衡化分散调度方法.依据风电日发电量年度分布情况,确定风电出力特性.分析多目标负荷平衡化分散对风电消纳的影响,由此获取等效负荷和受限的风电电量.为保证调度安全,设计极限场景集,描述分散调度模型...     

调整三相负荷平衡降损的原因及对策

最大程度的降低线损,是电力企业为了获得更大的经济效益所采取的重要措施之一。而就目前而言,降低线损的最简单、实用且有效的方法就是采用调整三相负荷平衡法。本文主要对采用三相负荷平衡法可以达到低压降损目的的原因进行剖析,并提出相关促进三相负荷平衡度的对策,以期为电力行业降损节电方面提出一些参考性建议。     

Designing cyclic appointment schedules for outpatient clinics with scheduled and unscheduled patient arrivals

We present a methodology to design appointment systems for outpatient clinics and diagnostic facilities that offer both walk-in and scheduled service. The developed blueprint for the appointment schedule prescribes the number of appointments to plan per day and the moment on the day to schedule the appointments. The method consists of two models; one for the day process that governs scheduled and unscheduled arrivals on the day and one for the access process of scheduled arrivals. Appointment schedules that balance the waiting time at the facility for unscheduled patients and the access time for scheduled patients are calculated iteratively using the outcomes of the two models. Two methods to calculate appointment schedules, complete enumeration and a heuristic procedure, are compared in various numerical experiments. Furthermore, an appointment schedule for the CT-scan facility at the Academic Medical Center Amsterdam, The Netherlands, is developed to demonstrate the practical merits of the methodology. The method is of general nature and can therefore also be applied to scheduling problems in other sectors than health care.     

Multiobjective study to implement outpatient appointment system at Hedi Chaker Hospital

The outpatient department at Hedi Chaker Hospital in Sfax, Tunisia, is currently using a block scheduling appointment whereby all patients are required to come at the beginning of the session. This brings about a tremendous waste of their time, creates congestion and puts further pressure on the medical staff. This paper evaluates a number of outpatient scheduling schemes based on the most recent research findings. Each model uses an appointment system scheme for scheduling with a well-performing appointment rule crossed with a sequencing rule. Environmental factors, such as the probability of no-shows and the probability of walk-ins, are included in the simulation of each of the different scenarios. Key measures of performance are determined for each of the different scenarios: waiting time in queues for each class of patients and resource utilisation. A Goal Programming Model is developed to choose the most appropriate schedule that optimizes the measures. Management preferences are incorporated to obtain a solution in line with their satisfaction.     

需求不确定条件下柔性供应链生产决策模型及优化

研究需求不确定条件下柔性供应链生产决策优化问题，以供应链期望总成本最小化为目标函数，考虑生产柔性为约束条件，建立了柔性供应链生产决策模型；通过优化分析，给出了供应链生产柔性有效边界的定义和经济意义，在此基础上引入成本柔性系数最小化的判断标准，从而得到最优方案的确定方法，最后通过算例对模型进行了验证。     

Flexible decision support in dynamic inter-organisational networks

An effective Decision Support System (DSS) should help its users improve decision making in complex, information-rich environments. We present a feature gap analysis that shows that current decision support technologies lack important qualities for a new generation of agile business models that require easy, temporary integration across organisational boundaries. We enumerate these qualities as DSS Desiderata, properties that can contribute both effectiveness and flexibility to users in such environments. To address this gap, we describe a new design approach that enables users to compose decision behaviours from separate, configurable components, and allows dynamic construction of analysis and modelling tools from small, single-purpose evaluator services. The result is what we call an ‘evaluator service network’ that can easily be configured to test hypotheses and analyse the impact of various choices for elements of decision processes. We have implemented and tested this design in an interactive version of the MinneTAC trading agent, an agent designed for the Trading Agent Competition for Supply Chain Management.     

Development of a guideline-based decision support system with explanation facilities for outpatient therapy

For effective guideline implementation it is recommended to develop and apply carefully designed implementation strategies and instruments. Computerized decision support systems (CDSSs) are such instruments as they can improve guideline adherence by providing advice at the point of care. To improve the implementation of the Dutch cardiac rehabilitation guidelines a CDSS, named CARDSS, was developed. CARDSS actively provides care professionals with patient-specific, guideline-based treatment recommendations at the onset of a patient's rehabilitation trajectory. To maximize the chances of acceptance, CARDSS also provides explanation facilities and other additional information management services, and takes the working procedures specific to multidisciplinary outpatient care into account. CARDSS is currently used in over 40 Dutch cardiac rehabilitation outpatient clinics. This paper describes the development of the CARDSS system. In particular, technical issues are discussed concerning the delivery of active decision support, and the provision of advice rationales to users while taking account of dynamic clinical contexts and changing guidelines.     

Flexible decision control in an autonomous trading agent

Modern electronic commerce creates significant challenges for decision-makers. The trading agent competition for supply-chain management (TAC SCM) is an annual competition among fully-autonomous trading agents designed by teams around the world. Agents attempt to maximize profits in a supply-chain scenario that requires them to coordinate Procurement, Production, and Sales activities in competitive markets. An agent for TAC SCM is a complex piece of software that must operate in a competitive economic environment. We report on results of an informal survey of agent design approaches among the competitors in TAC SCM, and then we describe and evaluate the design of our MinneTAC trading agent. We focus on the use of evaluators – configurable, composable modules for data analysis, modeling, and prediction that are chained together at runtime to support agent decision-making. Through a set of examples, we show how this structure supports Sales and Procurement decisions, and how those decision process can be modified in useful ways by changing evaluator configurations.     

干部信息管理和选拔任用决策支持系统的设计与实现

基于为县市级组织部门能够客观公正地选拔任用干部提供技术支持的思想,通过分析县市级干部信息管理和选拔任用决策支持系统总体目标及系统结构,构建了干部选拔任用决策支持模型.该模型可根据岗位任职资格要求和干部自身信息对领导干部进行筛选、计分、排序,从而为组织部门公开、公正,公平地选拔任用干部提供决策支持.该系统的应用为县市级组织部门的日常工作提供了有效的帮助.     

分布式入侵监测中负载平衡实现策略研究

传统的分布式入侵检测系统存在着大量丢包等问题.通过对现有的入侵监测系统问题的分析,寻找出传统系统存在问题的原因并进行针对性地改进.通过对不同数据包特性的研究,通过实验的方式确定出影响入侵检测系统处理效率的主要因素,找出了一种标示负载量的方式,将负载平衡的思想引入其中并与分布式思想相结合提出了一种提高入侵监测系统性能的系统结构设计方案.     

A DES-based group decision model for group decision making with large-scale alternatives

To solve group decision making problems with large-scale alternatives, this paper proposes a dynamic ensemble selection (DES) based group decision model by using historical decision data. The historical decision data of a group of experts are collected from the same multi-criteria decision framework and are mixed to train a set of base classifiers (BCs) to learn group preferences. For each new alternative, the predictions derived from BCs are used to determine its similar historical alternatives from historical data, and the BC with the highest accuracy in predicting the similar historical alternatives is identified as the best individual BC for the new alternative. By iteratively comparing the accuracy of an ensemble of randomly selected BCs and the best individual BC in predicting the similar historical alternatives of the new alternative, a novel DES method is developed to select a competent subset of BCs for the new alternative. The developed DES method effectively avoids the error-independence assumption to a certain extent. Based on the similar historical alternatives determined by the ensemble of selected BCs, a group decision optimization model is developed to learn criterion weights from their assessments on criteria and ensemble predictions derived from the selected BCs. With the learned criterion weights, the understandable group decision result is generated for the new alternative. Case study validates the superiority of the proposed model in diagnosing thyroid nodules using group capabilities. Empirical comparisons on thirty real datasets examine the competence of the proposed DES method against five representative DES methods.