A decision support system for dynamic truck dispatching

The feasibility of developing and implementing a real-time Decision Support System (DSS) on a microcomputer to assist line managers in making decisions, which have many subjective and intangible constraints, is demonstrated. A dynamic truck dispatching system was chosen as a prototype example.

The results of the research presented here are twofold. First, insight was gained in how tasks should be divided between the computer and the human operator and in the required colorgraphics interface between them in complex decision making processes. Second, a prototype DSS for a real-time vehicle dispatching system was developed on a microcomputer based upon these insights. The DSS was designed to accommodate all the characteristics of a large corporation's national dispatch system using contract carriers.     

Corporate dashboards for integrated business and engineering decisions in oil refineries: An agent-based approach

It is generally very challenging for an oil refinery to make integrated decisions encompassing multiple functions based on a traditional Decision Support System (DSS), given the complexity and interactions of various decisions. To overcome this limitation, we propose an integrated DSS framework by combining both business and engineering systems with a dashboard. The dashboard serves as a human-computer interface and allows a decision maker to adjust decision variables and exchange information with the DSS. The proposed framework provides a two-stage decision making mechanism based on optimization and agent-based models. Under the proposed DSS, the decision maker decides on the values of a subset of decision variables. These values, or the first-stage decision, are forwarded through the dashboard to the DSS. For the given set of first-stage decision variables, a multi-objective robust optimization problem, based on an integrated business and engineering simulation model, is solved to obtain the values for a set of second-stage decision variables. The two-stage decision making process iterates until a convergence is achieved. A simple oil refinery case study with an example dashboard demonstrates the applicability of the integrated DSS.     

Towards a Framework for Visual Model Development

A number of decision support systems (DSS) based on a visual interactive simulation (VIS) model have been implemented in recent years. A key component of a VIS model is the visual model. Existing VIS model development methodologies suggest methods (such as the paper prototype method) for developing a visual model. However, these methods are quite general and do not contain a framework to guide development. This paper describes a framework which combines the paper prototype method with systems concepts to develop the visual model. The proposed framework consists of eight stages. A project proof of principle example is described in which a VIS-based DSS provides support to the owner of a company selling telephone systems.     

一个新的支持多学科模型集成的流域决策支持系统框架研究

传统流域决策支持系统往往使用一两个模型来解决某些特定目标,限于系统结构而不能综合考虑多领域问题。提出了一种新的支持多学科模型集成的流域决策支持系统原型,在一个系统中实现多种学科模型的跨时空尺度集成,为流域综合管理提供全面科学支持。系统采用开放式多学科模型管理方法,其脚本扩展机制使得系统可以容纳几乎所有模型,模型因子方法使得形式多样的科学模型模拟结果可以应用到决策方法中,使系统在多学科模型支持下提供科学合理的决策方案。情景驱动的决策流程、向导式交互界面及GIS等图形化工具支持使得系统用户界面友好\,易用性强。     

The role of artificial intelligence in understanding the strategicdecision-making process

A survey is given which focuses on research issues involving strategic planning and artificial intelligence (AI), including the nature of the formulation task, cognitive studies of strategic planners and computer-based support for strategic planning. The author reviews the research to date, and argues that, like traditional decision support systems (DSS) research, much of the potential for future research in this area lies in modeling the ill-structured, early stages of the strategic decision making process namely, the strategic intelligence analysis and the issue diagnosis. Therefore, he discusses a specific model-based approach to the study of these early stages. Research in artificial intelligence-including investigations into diagnosis and situation assessment, analogical reasoning, plan recognition, nonmonotonic reasoning and distributed intelligence, among others-can be used to build models of strategic decision making that help researchers in better understanding this traditionally unstructured activity     

An examination of decision-makers' utilization of decision support system output

A study was conducted of 18 decision support systems in major U.S. corporations in order to examine their methods of operation and the ways in which the DSS contribute to the decision making process. These DSS were studied with respect to organizational level of the decision maker, phases of the decision making process, interaction among decision makers using the DSS, requirement for and regularity of DSS use, impact on job tasks and performance, and perceived value of the DSS. Study results provide substantial support for generalized conceptualizations in the literature. Decision support is primarily for upper and/or middle management working in interaction. Use of the DSS is at the discretion of the decision maker and the majority of DSS users are highly satisfied with their systems.     

Intermediating user-DSS interaction with autonomous agents

The use of advanced decision support system (DSS) capabilities is hampered by the inadequacy of a "toolbox" organization of DSS from the user's perspective. In such a setup, the user is assumed to have all the knowledge and skills necessary to appropriately use the tools provided by the system in the decision-making process. This paper proposes a model for the use of autonomous agents as intermediaries between the users and the system. The model is organized around the human problem-solving process. The paper elaborates on the types of intermediary agents and the architecture for a DSS. The approach is illustrated using the prototype for an investment DSS.     

Adaptive decision support systems

The effectiveness of decision support systems (DSS) is enhanced through dynamic adaptation of support to the needs of the decision maker, to the problem, and to the decision context. We define this enhanced DSS as adaptive decision support systems (ADSS) and propose its architecture. In an ADSS, the decision maker controls the decision process. However, the system monitors the process to match support to the needs. The proposed architecture evolves from the traditional DSS models and includes an additional intelligent‘Adaptation’ component. The ‘Adaptation’ component workd with the data, model, and interface components to provide adaptive support. The architecture also integrates enhancements proposed in the past research. In this paper, we have illustrated the proposed architecture with two examples, a prototype system, and results from a preliminary empirical investigations     

Introducing hypertext in primary health care: a study on the feasibility of decision support for practitioners

A study on the feasibility of the introduction of hypertext systems for communication of medical knowledge in primary care is described. Shortliffe's constraints on areas for application of decision support are evaluated (i.e., theoretical barriers, observable and recognized needs among users, sources of usable knowledge, available system development method). Considering the barriers derived from the knowledge types and forms used by practitioners, hypertext was found suitable as support at 'breakdowns' in practice routines, to generate 'alarms', as well as for continued medical education. A survey of the a priori acceptance of decision support systems by general practitioners showed that 84% would use the computer support if it was available today and that full-text databases, such as hypertext, were given highest priority for introduction. Local specialist physicians were identified as knowledge sources for therapy advice. Implementation of prototype systems envisioned use of hypertext in primary care as the introduction of a communication medium for co-operative health care decision making. A model for the introduction into the work environment is described.     

基于Web的决策支持系统的设计与实现

实现了一个基于Web的原型决策支持系统。该系统依据以往天气条件的记录,递归地构建一个决策树,用其预测未来网球比赛能否举行。详细展示了一个原型决策支持系统开发周期,包括从分析、设计到实现,如何使用面向对象技术来开发系统。     

DSS for micro and mainframe

Decision support systems (DSS) are becoming more popular as a means of assisting managers in the decision making process. The real benefits of DSS are to be found when micros become more integrated with mainframes. A DSS which runs on both mainframes and micros facilitates this development.     

A decision support system for automotive product planning and competitive market analysis

A decision support system (DSS) for automotive product marketing, design and manufacturing in China is presented in this paper. The DSS is developed as a tool to support product planning, competitive market analysis, supply chain analysis and subsequent manufacturing systems planning and deployment. The system consists of a number of automotive related databases which provide information about manufacturers' performance in each market segment as well as production information of all existing market players in the Chinese auto industry. Product planning, one of the key modules of the DSS prototype, is highlighted in this paper. It supports decision makers in determining suitable strategies for market entry by analyzing existing competitors' status, growth estimation of each market segment, and competitive market analysis for new vehicle products. A case study for new market entry is included here to demonstrate the feasibility and effectiveness of the proposed methodology.     

基于语义推理的DSS模型研究与应用

基于语义网理论建立基于语义推理的DSS模型。该模型引用W3C提出的资源描述框架（RDF）以描述业务领域中各要素,形成领域本体。并对领域中业务数据进行语义标注,构建事实库。在所构建事实库的基础上,系统根据预设规则进行推理,以达到决策支持的目的。最后基于以上方法对施工项目设备推荐领域进行实例建模,建立设备推荐系统。该系统将Semantic Web与Ontology理论引入到施工项目领域,以解决施工项目过程中存在的设备选型推荐问题,并作为语义Web理论在该领域内应用的一次尝试。     

Top management perspectives on decision support systems

In an increasingly turbulent business environment, Decision Support Systems (DSS) are receiving growing attention in the literature, particularly at top management level. Since the decision making milieu of senior management is extraordinarily difficult to define clearly, the question arises as to how far DSS are addressing the real problems of assisting with such decisions. Two hundred and eighty-five senior managers from a stratified sample completed a questionnaire designed to test their perceptions on this issue. In general, the respondents were reasonably aware of DSS; about a third of them have information systems that seem to fulfil the functions of a DSS to a degree, and the majority believe that decision aids such as DSS can, at least to some extent, be useful in addressing what they perceive to be their major difficulties in decision making. Of the ninety-eight managers who already have computer-based assistance, ninety-one consider them to be DSS.     

A decision support system for electric utilities: compliance with Clean Air Act

The Clean Air Act (CAA) Amendments established an absolute national limit for sulfur dioxide (SO₂) emissions by the year 2000. The Act is based on a market-oriented system under which power plants will be granted ‘emission allowances', each of which grants the right to emit a ton of SO₂ into the atmosphere. Utilities that reduce their emissions below their allocated allowances may sell the additional allowances in what will presumably be a developing market. Utilities are free to choose how to comply with the Act, but a few generally recognized options are likely to form the basis for compliance strategies. While the options are known, the costs of implementing the options are not known with certainty. Each utility will need to determine the lowest cost, most effective strategy given its current configuration and generation requirements. The compliance strategy decision must also be presented and defended to the appropriate Public Utility Commission (PUC). This paper outlines a prototype decision support system (DSS) using an optimization engine integrated with a database to help aid utilities in making decisions regarding their compliance strategy. This DSS was created specifically with a mid-western utility company in mind; however, it is generic enough to be useful to any utility. The results obtained using the DSS are very encouraging. The decisions supported by it are consistent with those offered by experts in the industry. The DSS promises to be a very useful tool for strategic planning related to CAA compliance.     

A structured modeling based methodology to design decision support systems

Many Decision Support Systems (DSS) support the decision making process through the use of mathematical models and data. DSS design involves modeling data as well as mathematical relationships in a domain. The process of model formulation and subsequent integration of model with data in a DSS is a complex and ill-structured process. This paper proposes a methodology based on Structured Modeling (SM), originally introduced by Geoffrion together with the modeling language SML, to model and design the DSS. The methodology includes rigorous and step by step procedures to design and integrate data and modelbases. The main contribution of our approach lies in the integration of research in database design, and mathematical model formulation within the structured modeling framework. The resultant procedures can be easily automated and taught to students in DSS courses. The motivation for our research stemmed from our constant frustrations in teaching DSS courses over the last five years. In the last two years, when we used our methodology, the performance of the students improved significantly. The average score in the DSS project went up to 85 from 60. Our positive experience in using our methodology in classes over the past two years suggests that the methodology imposes structure into the analysis of decision problems, and as a result students produce better DSS designs for classroom cases.     

Multi-material structural topology optimization with decision making of stiffness design criteria

A new methodology for making design decisions of structures using multi-material optimum topology information is presented. Multi-material analysis contributes significant applications to enhance the bearing capacity and performance of structures. A method that chooses an appropriate material combination satisfying design stiffness requirement economically is currently needed. An alternative method of making design-decision is to utilize a multi-material topology optimization (MMTO) approach. This study provides a new computational design optimization procedure as a guideline to find the optimal multi-material design by considering structure strain energy and material cost. The MMTO problem is analyzed using an alternative active-phase approach. The procedure consists of three design steps. First, steel grid configurations and composite with material properties are defined as a given structure for automatic design decision-making (DDM). And then design criteria of the steel composites structure is given to be limited strain energy by designers and engineers. Second, topology changes in the automatic distribution of multi-steel materials combination and volume control of each material during optimization procedures are achieved and at the same time, their converged minimal strain energy is produced for each material combination. And third, the strain energy and material cost which is computed based on the material ratio in the combinations are used as design decision parameters. A study in constructional steel composites to produce optimal and economical multi-material designs demonstrates the efficiency of the present DDM methodology.     

决策过程与决策支持系统关系的新视角

该文以新的视角来研究决策过程和决策支持系统。首先,以全新的观点阐释了决策过程和决策支持系统的结构,并绘制出相应的结构图;其次,以此为基础,绘制出了二者之间的层次结构模型图,揭示了决策支持系统和决策过程的内在联系。     

An AHP model of DSS effectiveness

Empirical studies have reported equivocal, or even dysfunctional, results from the use of decision support systems (DSS). Recent examples are the Davis, Kottemann, and Remus production planning experiments. According to the researchers, these experiments demonstrate that DSS what-if analysis creates an ‘illusion of control’ that causes users to overestimate its effectiveness. Such experimental findings are contrary to case-supported DSS theory. This paper examines the discrepancy. It first overviews the decision-making process, presents a generic DSS, identifies the theoretical role of the DSS in improving decision making, develops a multiple criteria model of DSS effectiveness, and gives a DSS for delivering the model to users. Illustrating with recent empirical investigations and the Davis, Kottemann, and Remus studies, the DSS-delivered model is used to reconcile the incongruity between the experimental findings and the case-supported theory. The paper concludes with a discussion of the article's implications for information systems research and practice.