A probabilistic reasoning-based decision support system for selection of remediation technologies for petroleum-contaminated sites

Selection of remediation technologies for petroleum-contaminated sites is difficult given the large number of technologies available and inherent uncertainties involved in the selection process. In this paper, we explore the use of an inexact algorithm for probability reasoning for dealing with the uncertainties involved in the problem. By incorporating domain knowledge as well as the stochastic uncertainty, a probabilistic rule-based decision support system (PDSS) has been developed to support the decision making process. The system has been applied to two case studies, in which the best option of remediation technology can be determined according to calculated probability values. In comparison to deterministic and fuzzy decision support systems, the PDSS can provide a recommendation together with a measure on the reliability or degree to which the recommended decision can be trusted.     

An intelligent decision support system for digital preservation

This paper describes a Service Oriented Architecture (SOA) based on Web services technology designed to assist cultural heritage institutions in the implementation of migration based preservation interventions. The proposed SOA delivers a recommendation service and a method to carry out complex format migrations. The recommendation service is supported by three evaluation components that assess the quality of every migration intervention in terms of its performance (Migration Broker), suitability of involved formats (Format Evaluator) and data loss (Object Evaluator). Throughout the paper the whole workflow between these three components is explained in detail as well as the most relevant tasks that are carried out internally in each of them. The proposed system is also able to produce preservation metadata that can be used by client institutions to document preservation interventions and retain objects’ authenticity. Although the primary goal of this SOA is the implementation of migration based preservation interventions, it can also be used for other purposes such as comparing file formats or evaluating the performance of conversion applications.     

面向体系虚拟采办的智能决策支持系统

为满足信息化战争形态对体系采办的需求,基于体系性能固有特征对虚拟采办的技术要求,在传统单系统虚拟采办技术基础上建立协同工程环境CEE,有效支持基于模型的系统工程MBSE．应用层新增智能决策支持系统IDSS旨在提高决策者的认知决策和群体研讨决策能力以获取初始需求定义模型,运用综合集成法与建模与仿真使能工具以优化模型构造与演化过程．IDSS既是CEE上的MBSE应用,与SBA应用共享可拓展资源层,又是SBA的支持系统,它运用构件按需组合的机制自动链接和执行工作流程而达到具体SBA问题求解的目标,并以紧致耦合和Web服务两种决策支持方式选项来适应用户需要．配合IDSS,在CEE中间件平台和资源层进行了相应的技术拓展．已实现的IDSS实用原型系统的可用性、有效性和技术优势,已通过体系采办实例得到了验证．文章最后给出了继续努力的几个方向．     

基于主体的智能协同决策支持系统

当今社会下,决策过程必须的信息资源和必要的决策因素越来越多地分散在较大的活动范围内,传统的集中式的决策支持系统已经越来越无法满足这种分布式的需求.针对决策支持系统需求的现状和发展前景,首先简要分析了当今的决策支持系统应该具有的特性,然后提出了一种基于主体的智能协同决策支持系统的模型.该模型将不同领域的专家知识封装入多个主体推理机当中,并依靠这些主体推理机之间的协同与交互解决复杂的决策支持问题.最后结合具体的例子介绍了该系统在实际项目中的应用.     

Service-oriented intelligent group decision support system: Application in transportation management

In today’s ever changing consumer driven market economy, it is imperative for providers to respond expeditiously to the changes demanded by the customer. This phenomenon is no different in the transportation sector in which a service-oriented Group Decision Support System (GDSS) provides an important role in transportation enterprise to effectively manage and rapidly respond to the varying needs of the customer. In this paper, we explore the integration problem of service-oriented system and intelligence technology through the use of a GDSS. Initially, we analyze a service-oriented architecture and then, propose the design architecture of a service-oriented GDSS. Next, we put forward a general framework that integrates the intelligent techniques as a component into the architecture of service oriented GDSS. In addition, we illustrate how Artificial Intelligence techniques can resolve the conflicts of distributed group decisions. The paper is concluded by providing a number of applications in the railway management system that demonstrates the benefits of the utilization of a service oriented intelligent GDSS.     

An intelligent decision support system for supply,distribution, and marketing planning

The paper focuses on a new and successful application of an optimization-based decision support system (DSS) in the Petroleum Industry. It involves the design, development and implementation of a model and computer system to address the complex short-term planning and operational issues associated with the supply, distribution and marketing of refined petroleum products. This has evolved into an intelligent DSS that uses the tools of Knowledge Engineering and Expert Systems to build an effective, integrated DSS. Several unique modeling features, hitherto untried in any major modeling effort, have been successfully implemented in a Network Optimization framework. The use of a fourth generation modeling language called GENASYS has been instrumental in capturing the intricacies of the network model and facilitates creating new model structures for various parametric changes. Concepts from Knowledge Based Systems and Artificial Intelligence applied in setting up Exception Reports through the use of production rules are another major characteristic of this application. The impact of this decision support system on integrated short-term planning and decision making and in stimulating changes in the attitudes and environment of the users and management is extensive.This research was supported in part by the Center for Business Decision Analysis, the Hugh Roy Cullen Centennial Chair in Business Administration, and the Office of Naval Research under contract N00014-78C-0222. Reproduction in whole or in part is permitted for any purpose of the U.S. Government.     

面向虚拟采办的智能决策支持系统概念框架

提出一种面向虚拟采办全寿命周期、全系统、全方位决策的智能决策支持系统SBA—IDSS概念框架．作为真实世界采办最终需求的抽象描述,它属于与实现无关的规范性模型体系,由它定义应用领域、用户概念和环境特征．同时它是数字世界中拟实现的初始工程模型,包括与实现无关的设计可行性模型、规划模型及功能顶层分解．该框架支持系统的自组织、自适应智能行为,预期可按需组成模型与仿真、文件、知识、通信及数据驱动的各类实用决策支持系统．该框架通过一项SBA实例的验证,得到可用性的正面评价．     

Case study: an intelligent decision support system

Information technology applications that support decision-making processes and problem-solving activities have proliferated and evolved. Distributing used cars to various automobile auctions is a complicated problem with multiple variables. We developed a software system to address these complexities and implemented it on a real distribution problem for a large car manufacturer. The system detects data trends in a dynamic environment, incorporates optimization modules to recommend a near-optimum decision, and includes self-learning modules to improve future recommendations. A software system that combines prediction, optimization, and adaptation techniques has generated impressive profits for a large auto manufacturer.     

An intelligent support system for presentation-based meta-learning

Here, we propose a presentation-based meta-learning scheme. Firstly, we present support functions that we embed into the system. Secondly, we conduct experiments to verify the meaningfulness of our learning scheme. These suggest that the system can stimulate learners to reflect on their learning processes. Furthermore, the scheme can stimulate learners’ meta-learning communication. The results show that users tightened their criteria when evaluating their own learning processes and understanding states. It is useful for learners to facilitate changes in their learning processes.     

灰云逻辑及其智能决策支持系统

针对经典的数理逻辑不能满足智能决策决策支持系统对逻辑柔性化的要求,在分析各种经典和非经典逻辑的基础上,指出了智能决策决策支持系统的逻辑柔性化的发展趋势,并给出了一种基于灰云模型的具体的柔性逻辑——灰云逻辑。首先分析了智能决策支持系统对逻辑柔性化的需求,然后给出了灰云逻辑及基于灰云逻辑的智能决策支持系统,给出了灰云逻辑的具体表示形式,并给出了基于灰云逻辑的具体的知识推理方法。最后,给出了基于灰云逻辑的智能决策支持系统框架和原理。其研究特点在于明确了智能决策决策支持系统的逻辑柔性化的发展方向,并给出了一种表示信息不完全性和随机性的具体的柔性逻辑推理方法。     

A knowledge-based intelligent decision support system for national defense budget planning

In this paper, we present a new framework for knowledge-based intelligent decision support systems for developing a national defense budget planning. The planning procedure for and architecture of the national defense budget in Taiwan are discussed in detail. In particular, the theories and techniques of intelligent decision support are used in the yearly practical budget planning process. Based on data in the financial database and knowledge in the knowledge base, we easily adjust the beforehand budget proposal. Furthermore, a knowledge-based intelligent decision support system has been implemented and it collects a series of rules extracted from national defense experts for successful reasoning. By using forward reasoning and knowledge rules, the system can automatically change and regenerate the national defense budget plan immediately. Finally, the empirical functions of the KIDSS system are also addressed.     

Agent-based intelligent system development for decision support in chemical process industry

This paper presents an agent-based intelligent system to support coordinate manufacturing execution and decision-making in chemical process industry. A multi-agent system (MAS) framework is developed to provide a flexible infrastructure for the integration of chemical process information and process models. The system comprise of a process knowledge base and a group of functional agents. Agents in the system can communicate and cooperate with each other to exchange and share information, and to achieve timely decisions in dealing with various scenarios in process operations and manufacturing management. Process simulation, artificial intelligent technique, rule-based decision supports are integrated in this system for process analysis, process monitoring, process performance prediction and operation suggestion. The implementation of this agent-based system was illustrated with two case studies, including one application in process monitoring and process performance prediction for a chemical process and one application in de-bottlenecking of a site utility system.     

基于时间序列算法与多层次分布式智能决策支持系统

时间序列分析方法的研究和应用飞速发展,越来越多的工程实际工作者开始研究并应用时间序列分析法.采用了博克思-詹金斯预测方法,主要对随机时间序列预测模型进行详细的研究,对时间序列数据进行分析,从中获取所蕴含的关于生成时间序列的系统的演化规律,以完成对系统的观测及其未来行为的预测,这在工程应用中具有一定的价值和意义.     

Representing modeling knowledge in an intelligent decision support system

Knowledge representation for data, models, and other decision support system (DSS) elements is a complex and ever-adapting task. The representation scheme for an intelligent DSS will need to provide general problem-solving model management activities as well as a mechanism for refining and testing the applicability of these models for each problem instance it encounters. We present traditional knowledge representation alternatives, and demonstrate why a multi-level scheme is superior for DSS use. We advance a two-level scheme, joining the advantages of connection graphs for the generalized analytical requirements and a frame component for problem-specific query resolution.     

Model management system for IRT-based test construction decision support system

Decision support system (DSS) has become widespread for some specific domains in recent years. However, DSS for IRT-based (item response theory) test construction has not yet been developed. This domain basically imposes a semi-structured or unstructured decision and, therefore, involves a very complex modeling process. This study develops a model management system (MMS) architecture to assist a non-expert user in manipulating test construction process efficiently and effectively. This architecture consists of four components: problem analysis, model type selection, model formulation and solver. The model type selection subsystem is further organized into three levels of hierarchy, i.e., environment, structure and parameter. A prototype is presented to demonstrate the feasibility of this architecture. The results indicate that this approach can be applied for providing an integrated, flexible and user-friendly DSS environment for producing better quality of results in less solution time.     

Development of medical decision support system for leukemia management

A prototype Medical Decision Support System (MDSS) for leukemia patients was developed with emphasis on total management approach from patient registration to diagnosis and treatment. Thus, the MDSS consists of four modules: registry, knowledge model, simulator, and Computer-Assisted Instruction (CAI). Integration of each module improves overall patient management capability and knowledge acquisition capability of the system. Four different knowledge models were developed to predict diagnosis: rule-based reasoning, case-based reasoning, neural network, and discriminant analysis. Among the four, rule-based reasoning produced the most accurate prediction in diagnosis. In the future, the method of leukemia registry can further be extended to the hospital-based cancer registry for other types of cancer. In order to be more effective, the registry should also be integrated with the hospital information system for an easier data entry.     

An optimization-based decision support system for ship scheduling

The bulk carriers in the world merchant fleet typically operate full between a loading and discharging port, then run empty until they reach the next loading port. The shipping rates of bulk trades are set on supply/demand bases and fluctuate considerably. Thus the proper scheduling of ships in bulk trade has the great potential of improving the owner's profit and economic performance of shipping. This paper considers an optimization-based Decision Support System for ship scheduling. The typical optimization models for scheduling the ships are briefly reviewed and classified by the underlying idea. Then a prototype MoDiSS(Model-based SS in Ship Scheduling) which is based on a set-packing model has been developed on PC base with proper GUI. The performance of the system has been tested and examined using various ship scheduling scenarios and thereby the effectiveness of the system is validated satisfactorily.     

An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank

Developing decision support system (DSS) can overcome the issues with personnel attributes and specifications. Personnel specifications have greatest impact on total efficiency. They can enhance total efficiency of critical personnel attributes. This study presents an intelligent integrated decision support system (DSS) for forecasting and optimization of complex personnel efficiency. DSS assesses the impact of personnel efficiency by data envelopment analysis (DEA), artificial neural network (ANN), rough set theory (RST), and K-Means clustering algorithm. DEA has two roles in this study. It provides data to ANN and finally it selects the best reduct through ANN results. Reduct is described as a minimum subset of features, completely discriminating all objects in a data set. The reduct selection is achieved by RST. ANN has two roles in the integrated algorithm. ANN results are basis for selecting the best reduct and it is used for forecasting total efficiency. Finally, K-Means algorithm is used to develop the DSS. A procedure is proposed to develop the DSS with stated tools and completed rule base. The DSS could help managers to forecast and optimize efficiencies by selected attributes and grouping inferred efficiency. Also, it is an ideal tool for careful forecasting and planning. The proposed DSS is applied to an actual banking system and its superiorities and advantages are discussed.     

基于多Agent的决策支持系统模型研究

为了解决目前市场上许多企业推出的商务智能产品模型存在主动性差、智能性较低和系统集成困难等缺陷,利用现有的多Agent技术建立了一个商务智能系统MABIS,力图弥补现有商务智能系统存在的缺陷,减少对用户的依赖性.给出了一个基于多Agent技术的商务智能系统--MABIS及其系统架构,并分析了MABIS系统的工作流程和模型的可扩展性.介绍了实现MABIS的关键技术,分析了MABIS系统各部分的实现细节,给出了规则的描述方法以及基于规则的推理过程.     

An optimization-model-based interactive decision support system for regional energy management systems planning under uncertainty

In this study, an interactive decision support system (UREM-IDSS) has been developed based on an inexact optimization model (UREM, University of Regina Energy Model) to aid decision makers in planning energy management systems. Optimization modeling, scenario development, user interaction, policy analysis and visual display are seamlessly integrated into the UREM-IDSS. Uncertainties in energy-related parameters are effectively addressed through the interval linear programming (ILP) approach, improving the robustness of the UREM-IDSS for real-world applications. Thus, it can be used as an efficient tool for analyzing and visualizing impacts of energy and environmental policies, regional/community sustainable development strategies, emission reduction measures and climate change in an interactive, flexible and dynamic context. The Region of Waterloo has been selected to demonstrate the applicability and capability of the UREM-IDSS. A variety of scenarios (including a reference case) have been identified based on different energy management policies and sustainable development strategies for in-depth analysis of interactions existing among energy, socio-economy, and environment in the Region. Useful solutions for the planning of energy management systems have been generated, reflecting complex tradeoffs among energy-related, environmental and economic considerations. Results indicate that the UREM-IDSS can be successfully used for evaluating and analyzing not only the effects of an individual policy scenario, but also the variations between different scenarios compared with a reference case. Also, the UREM-IDSS can help tackle dynamic and interactive characteristics of the energy management system in the Region of Waterloo, and can address issues concerning cost-effective allocation of energy resources and services. Thus, it can be used by decision makers as an effective technique in examining and visualizing impacts of energy and environmental policies, regional/community development strategies, emission reduction measures, and climate change within an integrated and dynamic framework.