基于云计算的生产决策支持系统

为辅助企业进行高效率的生产,设计并实施了一种基于云计算的生产决策支持系统,云计算的运行平台为大规模应用和庞大数据处理提供了保障。系统架构为基于MVC(Model View Controller)模式的四层体系结构,系统模型库为决策功能的实现提供了大量的模型支持,数据层作为数据存储的媒介为系统提供了数据支持。在系统设计上充分考虑了人机交互,运行基本稳定,各模块衔接良好,能有效为企业提供科学化的生产决策支持。     

An intelligent decision support system for management of petroleum-contaminated sites

Groundwater and soil contamination resulted from LNAPLs (light nonaqueous phase liquids) spills and leakage in petroleum industry is currently one of the major environmental concerns in North America. Numerous site remediation technologies have been developed and implemented in the last two decades. They are classified as ex-situ and in-situ remediation techniques. One of the problems associated with ex-situ remediation is the cost of operation. In recent years, in-situ techniques have acquired popularity. However, the selection of the optimal techniques is difficult and insufficient expertise in the process may result in large inflation of expenses. This study presents an expert system (ES) for the management of petroleum contaminated sites in which a variety of artificial intelligence (AI) techniques were used to construct a support tool for site remediation decision-making. This paper presents the knowledge engineering processes of knowledge acquisition, conceptual design, and system implementation. The results from some case studies indicate that the expert system can generate cost-effective remediation alternatives to assist decision-makers.     

Multi-objective optimization for process control of the in-situ bioremediation system under uncertainty

In-situ bioremediation is a commonly used remediation technology to clean up the subsurface of a petroleum-contaminated site. The process control of such a system is complex and may involve more than one objective. This study discusses the development of a simulation model-based, dynamic, and multi-objective predictive control system for generating cost-effective control strategies for a bioremediation site, which involves substantial uncertain data. This control system was developed by enhancing the single objective control system presented in Hu et al. [2004. Dynamic process control for in-situ bioremediation system. In: Proceedings of the 2004 IEEE Canadian Conference on Electrical & Computer Engineering, May 4–7]. It shares the objective of minimizing overall cost as that presented in Hu et al. (2004), and it also has the added objective of maximizing system efficiency. An optimized linear interpolation method has been developed to handle the uncertainty involved in the changes of the hydraulic characteristics in groundwater transport simulation, and an interactive decision-making tool is built for multi-objective process control. The solution method includes generation of a set of optimal control strategies and costs to meet different efficiency requirements, normalization of the costs and efficiencies, and construction of the optimal control strategy to satisfy the decision maker's particular preferences on tradeoff between cost and efficiencies.The developed system has been applied on data, which is obtained from lab experiment and a hypothetical site. The results indicate that the optimized linear interpolation function could model inherent uncertainties that result from inadequacies in the chosen sampling points, and enhance overall accuracy of the simulation model. The results show that the control system could generate a set of control strategies, which assign different importance to each objective, thereby providing an optimal strategy to meet particular requirements of the decision maker.     

Impact of the shape of demand distribution in decision models for operations management

Decision support tools are increasingly used in operations where key decision inputs such as demand, quality, or costs are uncertain. Often such uncertainties are modeled with probability distributions, but very little attention is given to the shape of the distributions. For example, state-of-the-art planning systems have weak, if any, capabilities to account for the distribution shape. We consider demand uncertainties of different shapes and show that the shape can considerably change the optimal decision recommendations of decision models. Inspired by discussions with a leading consumer electronics manufacturer, we analyze how four plausible demand distributions affect three representative decision models that can be employed in support of inventory management, supply contract selection and capacity planning decisions. It is found, for example, that in supply contracts flexibility is much more appreciated if demand is negatively skewed, i.e., has downside potential, compared to positively skewed demand. We then analyze the value of distributional information in the light of these models to find out how the scope of improvement actions that aim to decrease demand uncertainty vary depending on the decision to be made. Based on the results, we present guidelines for effective utilization of probability distributions in decision models for operations management.     

Support for real-time decision making in mobile financial applications

Mobile users making real-time decisions based on current information need confidence that their context has been taken into consideration in producing the system’s recommendations. This chapter reviews current use of mobile technologies for context-aware real-time decision support. Specifically, it describes a framework for assessing the impact of mobility in decision making. The framework uses dynamic context model of data quality to represent uncertainties in the mobile decision-making environment. This framework can be used for developing visual interactive displays for communicating to the user relevant changes in data quality when working in mobile environments. As an illustration, this chapter proposes a real-time decision support procedure for on-the-spot assistance to the mobile consumer when choosing the best payment option to efficiently manage their budget. The proposed procedure is based on multi-attribute decision analysis, scenario reasoning, and a quality of data framework. The feasibility of the approach is demonstrated with a mobile decision-support system prototype implementation. This article is part of the “Handbook on Decision Support Systems” edited by Frada Burstein and Clyde W. Holsapple (2008) Springer.     

A decision support system for strategic asset allocation

Strategic asset allocation is a crucial activity for any institutional or individual investor. Given a set of asset classes, the problem concerns the definition and management over time of the best asset mix to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. Although a considerable attention has been placed by the scientific community to address this problem by proposing sophisticated optimization models, limited effort has been devoted to the design of integrated framework that can be systematically used by financial operators. The paper presents a decision support system which integrates simulation techniques for forecasting future uncertain market conditions and sophisticated optimization models based on the stochastic programming paradigm. The system has been designed to be accessed via web and takes advantages of the increased computational power offered by high performance computing platforms. Real-world instances have been used to assess the performance of the decision support system also in comparison with more traditional portfolio optimization strategies.     

A data analysis decision support system for the carbon dioxide capture process

This paper presents the development process of an expert decision support system for pre-filtering and analysis of data from the carbon dioxide (CO₂) capture process. Chemical absorption has become one of the dominant CO₂ capture technologies because of its efficiency and low cost. Since the chemical absorption process consists of dozens of components, it generates more than a 100 different types of data. Monitoring the vast amount of data can be complex, and data filtering and analysis processes are desirable. Specifically, invalid data captured as the equipment is started and shut down need to be filtered, and the filtered data need to be analyzed for different purposes. The expert decision support system for data pre-filtering and analysis not only filters out invalid data using different expert rules, but it can also modify or reuse filtering settings, and export the filtered data to various file formats for further analysis. During development of the expert decision support system, knowledge acquisition was emphasized. The system development process incorporated various technologies including the model-view-control (MVC) design pattern, the embedded database technology, the Java event delivery techniques and the eXtensible Markup Language (XML). Some sample sessions from system executions and some results generated from pre-filtering the data will also be discussed.     

基于虚拟机的可编程器件仿真系统中编译器技术的研究

论文针对可编程器件的仿真问题,对基于虚拟机的编译器技术进行了较深入的研究,提出了具体的设计方案。通过采用两遍编译及地址回填技术和构建比较完备且存取效率较高的符号仓库,有效地实现了源程序到目标程序的等价转换,并为可编程器件仿真系统PDSS设计了一个可编程器件编译器PDC。在PDSS中,PDC与虚拟机相互配合,使可编程器件的仿
仿真脱离了特定处理器体系结构的限制,而且不依赖于具体操作系统的实现,达到了对编译、运行直至仿真的完全控制。     

A two-stage programming approach for water resources management under randomness and fuzziness

In this study, a fuzzy stochastic two-stage programming (FSTP) approach is developed for water resources management under uncertainty. The concept of fuzzy random variable expressed as parameters’ uncertainties with both stochastic and fuzzy characteristics was used in the method. FSTP has advantages in uncertainty reflection and policy analysis. FSTP integrates the fuzzy robust programming, chance-constrained programming and two-stage stochastic programming (TSP) within a general optimization framework. FSTP can incorporate pre-regulated water resources management policies directly into its optimization process. Thus, various policy scenarios with different economic penalties (when the promised amounts are not delivered) can be analyzed. FSTP is applied to a water resources management system with three users. The results indicate that reasonable solutions were generated, thus a number of decision alternatives can be generated under different levels of stream flows, α-cut levels and different levels of constraint-violation probability. The developed FSTP was also compared with TSP to exhibit its advantages in dealing with multiple forms of uncertainties.     

特种设备智能系统设计中的关键技术

特种智能决策辅助系统能够有效地保证项目系统能够在最大程度上,为管理和决策提供有效的数据与信息。该系统具有高度的业务复杂性以及特殊性,因此在设计的过程中应该根据特殊的流程与技术加以完成。为了更好地进行管理和决策,企业应该做好信息的收集工作,从而实现快捷的综合业务分析。同时,必须遵循系统架构以及技术构成的规律,从而让特种智能系统能够有充足应用的空间。     

一种认知行为模式的概率化确定方法

研究认知行为模式的确定,提出一种认知行为模式的概率化确定方法,首先,简要介绍认知行为模式的SRK框架,分析三种认知行为模式的特点以及该框架在人因可靠性分析中的应用,然后,概述认知行为模式的确定方法-Hanaman决策树,分析该方法的特点以及存在的不足,强调在认知行为模式的确定过程中考虑不确定性的必要性.随后,将不确定性...     

Gambling versus Designing: Organizing for the Design of the Probability Space in the Energy Sector

The objective of this paper is to elucidate an organizational process for the design of generic technologies (GTs). While recognizing the success of GTs, the literature on innovation management generally describes their design according to evolutionary strategies featuring multiple and uncertain trials, resulting in the discovery of common features among multiple applications. This random walk depends on multiple market and technological uncertainties that are considered exogenous: as smart as he can be, the ‘gambler’ must play in a given probability space. However, what happens when the innovator is not a gambler but a designer, i.e., when the actor is able to establish new links between previously independent emerging markets and technologies? Formally speaking, the actor designs a new probability space. Building on a case study of two technological development programmes at the French Center for Atomic Energy, we present cases of GTs that correspond to this logic of designing the probability space, i.e. the logic of intentionally designing common features that bridge the gap between a priori heterogeneous applications and technologies. This study provides another example showing that the usual trial‐and‐learning strategy is not the only strategy to design GTs and that these technologies can be designed by intentionally building new interdependences between markets and technologies. Our main result is that building these interdependences requires organizational patterns that correspond to a ‘design of exploration’ phase in which multiple technology suppliers and application providers are involved in designing both the probability space itself and the instruments to explore and benefit from this new space.     

Hierarchical decision making for proactive quality control: system development for defect reduction in automotive coating operations

Product quality control (QC) in manufacturing usually relies solely on inspection. Once a quality problem is found, a solution is sought usually based on experience, which is basically ad hoc. A new generation of QC requires the integration of both quality prediction and inspection. Automotive coating is a typical example. In the paint shop of an automotive assembly plant, topcoat filmbuild quality on vehicle surface has been a major concern. In production, defects are frequently generated in the very thin coating layers, which can degrade severely both coating appearance and durability. Trial and error in troubleshooting is a usual practice.In this paper, we introduce a proactive QC approach by resorting to artificial intelligence and engineering fundamentals. The approach is developed for solving a class of engineering problems for which conventional reactive QC approaches are feeble due to system complexity and uncertainties, such as that in paint applications. The main focus of the approach is on-process, rather than post-process. Thus, the domain knowledge about a process is fully explored and correlation of the process to product quality is established in a systematic way. In this approach the knowledge is expressed either symbolically or numerically, and structured in a hierarchy as reasoning progresses. Decision making is performed by a fuzzy MIN–MAX algorithm for heuristic knowledge and optimization for fundamental knowledge. To demonstrate the efficacy of the methodology, an application to QC of automotive topcoat is illustrated through developing an intelligent decision support system. This system is capable of evaluating process performance, and providing various valuable decision supports for defect prevention in different stages of a topcoat application process.     

An evolutionary Bayesian belief network methodology for optimum management of groundwater contamination

An integrated methodology, based on Bayesian belief network (BBN) and evolutionary multi-objective optimization (EMO), is proposed for combining available evidence to help water managers evaluate implications, including costs and benefits of alternative actions, and suggest best decision pathways under uncertainty. A Bayesian belief network is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. In complex applications where the task of defining the network could be difficult, the proposed methodology can be used in validation of the network structure and the parameters of the probabilistic relationship. Furthermore, in decision problems where it is difficult to choose appropriate combinations of interventions, the states of key variables under the full range of management options cannot be analyzed using a Bayesian belief network alone as a decision support tool. The proposed optimization method is used to deal with complexity in learning about actions and probabilities and also to perform inference. The optimization algorithm generates the state variable values which are fed into the Bayesian belief network. It is possible then to calculate the probabilities for all nodes in the network (belief propagation). Once the probabilities of all the linked nodes have been updated, the objective function values are returned to the optimization tool and the process is repeated. The proposed integrated methodology can help in dealing with uncertainties in decision making pertaining to human behavior. It also eliminates the shortcoming of Bayesian belief networks in introducing boundary constraints on probability of state values of the variables. The effectiveness of the proposed methodology is examined in optimum management of groundwater contamination risks for a well field capture zone outside Copenhagen city.     

The quality of expert probability judgement: issues and analysis

Abstract: Many decision-aiding technologies require valid probability judgements to be elicited from domain experts. But how valid are experts' probability judgements? We describe two approaches to the assessment of quality of probability judgement—calibration and coherence—and review the research findings following from these two approaches. In many cases, expert probability judgement has been found to lack validity and this sub-optimality has largely been attributed to computational errors on the part of the expert. The preferred solution to poor validity in probability judgement has therefore been to reduce the amount of computation performed by the expert. Complex probabilities can be calculated mechanically from simple probability judgements elicited from the expert. We present evidence which suggests that this recomposition technique doesn't guarantee valid probabilities. Our explanation for this finding is that there are various problems concerned with eliciting even the simple probabilities which are necessary for subsequent recomposition. We conclude by proposing some solutions to these elicitation problems which should help ensure that probability judgements of increased validity are available to those attempting to capture subjective assessments for input into decision support systems.     

精准农业管理决策支持系统的设计与实现

“精准农业”是基于“3S”技术和农学知识支持的现代农业。而精准农业管理决策支持系统是实现“精准农业”的核心系统。为了解决精准农业中信息的获取、管理、分析和专家智能决策生成问题,研究并集成了精准农业中的关键技术--全球定位系统、地理信息系统、专家系统和决策支持系统。通过本系统,用户可以管理农田信息,进行有关品种、施肥、病虫害防治等决策支持,以实现农田的变量管理,减少生产成本和环境污染,增加经济效益。本系统采用C/S结构,具有可靠性、易扩充性和易操作性等特点。系统已在宁夏精准农业示范基地中得到部分应用。     

MATISSE: a relational expert system for industrial site selection

This paper reports on the development of a relational knowledge-based decision support system for urban planning in general and industrial site selection in particular. The system treats the concept of site suitability as a matching process, using decision tables (DTs). The proposed computer-based system is tested using the locational choice problem of an industrial company. The system has been given the acronym MATISSE: “Matching Algorithm, A Technique for Industrial Site Selection and Evaluation”. The knowledge base of the system was created by conducting a series of in-depth interviews supplemented with a detailed survey of the relevant literature. Using this information, a series of decision tables could be constructed using prologa95. In total, 90 crisp (sub)decision tables were constructed. This set of DTs can be used as a decision support system to select and evaluate potential sites, given a set of locational requirements.     

Systematic modeling and model handling for manpower planning systems

The primary purpose of this paper is to show an efficient way of handling models and model data in a decision support system, in which it is usual to consider several variants of a model. The model data primarily consist of model-defining data, but the same approach may be used for the generated results as well. By efficient handling is meant the handling by the computer as well as by the user. For the user it is particularly important that new models can be conveniently defined as variants of existing models. The approach is introduced within the context of a decision support system for manpower planning based on Markov models. In the mean time the same approach has been used for the implementation of other decision support systems and has been found to be more generally applicable.     

A hybrid fuzzy group decision support framework for advanced-technology prioritization at NASA

The prioritization of advanced-technology projects at the National Aeronautic and Space Administration (NASA) is a difficult task. This difficulty is due to the multiple and often conflicting objectives in addition to the inherent technical complexities and valuation uncertainties involved in the assessment process. As such, a systematic and transparent decision support framework is needed to guide the assessment process, shape the decision outcomes and enable confident choices to be made. Methods for solving Multi-Criteria Decision Making (MCDM) problems have been widely used to select a finite number of alternatives generally characterized by multiple conflicting criteria. However, applying these methods is becoming increasingly difficult for technology assessment in the space industry because there are many emerging risks for which information is not available and decisions are made under significant uncertainty. In this paper, we propose a hybrid fuzzy group decision support framework for technology assessment at NASA. The proposed objective framework is comprised of two modules. In the first module, the complicated structure of the assessment criteria and alternatives are represented and evaluated with the Analytic Network Process (ANP). In the second module, the alternative advanced-technology projects are ranked using a customized fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We demonstrate the applicability of the proposed framework through a case study at the Kennedy Space Center.