Analysis of heuristic validity,efficiency and applicability of the profile distance method for implementation in decision support systems

This article seeks to enhance acceptance of the profile distance method (PDM) in decision support systems. The PDM is a multiple attributive-based decision making as well as a multiple method approach to support complex decision making and uses a heuristic to avoid computationally complex global optimization. We elaborate on the usability of the method and question the heuristic used. We present a bi-section algorithm, which efficiently supports the discovery of transition profiles needed in a user-friendly and practical application of the method. Additionally, we provide empirical evidence showing that the proposed heuristic is efficient and delivers results within 5% of the global optimizer for a wide range of datasets.     

Knowledge Representation for Model Management Systems

This paper examines the concept of a model management system, what its functions are, and how they are to be achieved in a decision support context. The central issue is model representation which involves knowledge representation and knowledge management within a database environment. The model abstraction structure is introduced as a vehicle for model representation which supports both heuristic and deterministic inferencing as well as the conceptual/external schema notion familiar to database management. The model abstraction is seen as a special instance of the frame construct in artificial intelligence. Model management systems are characterized as frame-systems and a database implementation of this approach is described.     

Network service scheduling and routing

Real‐life vehicle routing problems generally have both routing and scheduling aspects to consider. Although this fact is well acknowledged, few heuristic methods exist that address both these complicated aspects simultaneously. We present a graph theoretic heuristic to determine an efficient service route for a single service vehicle through a transportation network that requires a subset of its edges to be serviced, each a specified (potentially different) number of times. The times at which each of these edges are to be serviced should additionally be as evenly spaced over the scheduling time window as possible, thus introducing a scheduling consideration to the problem. Our heuristic is based on the tabu search method, used in conjunction with various well‐known graph theoretic algorithms, such as those of Floyd (for determining shortest routes) and Frederickson (for solving the rural postman problem). This heuristic forms the backbone of a decision support system that prompts the user for certain parameters from the physical situation (such as the service frequencies and travel times for each network link as well as bounds in terms of acceptability of results) after which a service routing schedule is suggested as output. The decision support system is applied to a special case study, where a service routing schedule is sought for the South African national railway system by Spoornet (the semi‐privatised South African national railways authority and service provider) as part of their rationalisation effort, in order to remain a lucrative company.     

基于决策支持度的决策树生成算法

从条件属性对决策支持程度不同的角度出发,引入了决策支持度的概念,提出了一种以其为启发式信息的决策树生成算法。实验分析表明,相对于传统的决策树生成算法,此算法改善了决策树的结构,有效提高了决策分类的精度。     

Information tradeoffs in model building: A network routing application

An integral part of the model-building process is the modeler's choice of how much information to gather and encode in the decision model. Obtaining more detailed and accurate information enables a more precise problem representation which, in turn, leads to more effective decision making. However, acquiring extensive and accurate information entails higher costs and delays. This paper uses a network routing decision context to illustrate the tradeoff between model precision and decision effectiveness, and explores a formal decision-theoretic approach to determine an appropriate model specification that balances information gathering costs and decision quality. We propose optimal and heuristic methods for generating good information search strategies, and report computational results based on random test problems. Our results highlight the importance of simultaneously considering information costs and decision payoffs for constructing a decision model to support routing decisions. The issues raised in this paper are especially significant for modeling dynamic, real-time decision contexts where delays induced by information gathering activities could have significant economic impact.     

一种条件向量约简的启发式算法

粗糙决策支持方法是一组用于决策支持的粗糙分析方法,该方法能够充分挖掘决策表的决策能力,以提供强有力的决策支持,并且本质上提供容错的决策支持,条件向量约简是这一方法的重要研究内容。论文以决策强度、条件向量的覆盖度和属性的重要性为启发式信息,提出了条件向量约简的一种启发式算法,通过实验验证了该算法是有效的。     

基于不完备信息系统的决策树生成算法

决策树是一种有效地进行实例分类的数据挖掘方法。在处理不完备信息系统中的缺省值数据时,现有决策树算法大多使用猜测技术。在不改变缺失值的情况下,利用极大相容块的概念定义了不完备决策表中条件属性对决策属性的决策支持度,将其作为属性选择的启发式信息。同时,提出了一种不完备信息系统中的决策树生成算法IDTBDS,该算法不仅可以快速得到规则集,而且具有较高的准确率。     

A comparative study on heuristic algorithms for generating fuzzydecision trees

Fuzzy decision tree induction is an important way of learning from examples with fuzzy representation. Since the construction of optimal fuzzy decision tree is NP-hard, the research on heuristic algorithms is necessary. In this paper, three heuristic algorithms for generating fuzzy decision trees are analyzed and compared. One of them is proposed by the authors. The comparisons are twofold. One is the analytic comparison based on expanded attribute selection and reasoning mechanism; the other is the experimental comparison based on the size of generated trees and learning accuracy. The purpose of this study is to explore comparative strengths and weaknesses of the three heuristics and to show some useful guidelines on how to choose an appropriate heuristic for a particular problem.     

Finding good nurse duty schedules: a case study

Constructing duty schedules for nurses at large hospitals is a difficult problem. The objective is usually to ensure that there is always sufficient staff on duty, while taking into account individual preferences with respect to work patterns, requests for leave and financial restrictions, in such a way that all employees are treated fairly. The problem is typically solved via mixed integer programming or heuristic (local) search methods in the operations research literature. In this paper the problem is solved using a tabu search approach as a case study at Stikland Hospital, a large psychiatric hospital in the South African Western Cape, for which a computerized decision support system with respect to nurse scheduling was developed. This decision support system, called NuRoDSS (short for Nurse Rostering Decision Support System) is described in some detail.     

论如何建立销售业经理决策支持系统

以上海市燃料总公司油品公司经理决策支持系统为例 ,对如何建立销售业经理决策支持系统进行探讨研究 ,为销售企业经理决策提供借鉴     

一种基于粗糙集理论的启发式特征选择算法

在数据分析中,特征选择是能够保留信息的数据约简的一个有效方法。粗糙集理论提供了一种发现所有可能的特征子集的数学工具。提出了一种新的基于粗糙集的启发函数叫做加权平均支持启发函数。该方法的优点是它考虑了可能性规则集的整体质量。也就是说,对所有的决策类,它考虑了规则的加权平均支持度。最后,实例表明该方法是有效的。     

基于小生境遗传算法的知识约简方法

提出基于小生境遗传算法的知识约简算法,利用遗传算法的搜索寻优技术,可以得到多种约简属性集。实验证明,该算法是有效的,它可以解决启发式算法不能解决的部分问题,并能求解出知识系统中存在的不同约简,从而为特征提取、决策支持和数据挖掘等提供更多的信息。     

An appropriateness framework for the Dutch Meuse decision support system

Models are essential in a decision support system for river basin management. In a decision support system for integrated planning and management, the use of appropriate models is important to avoid models being either too simple or too complex. In this paper, appropriate models refer to models that are good-enough-but-not-more-than-that to obtain an acceptable ranking of river engineering measures under uncertainty. A systematic approach called ‘appropriateness framework’ is proposed to determine appropriate models that can be used in a decision support system. The approach is applied to a decision support system for the Dutch Meuse River. One important component of this decision support system, flood safety, is used in this paper to demonstrate how this approach works. The results show that the approach is very useful in helping to determine appropriate models. Potential applications of the approach in other decision support systems are discussed. The approach presented in this paper is designed as a tool to stimulate the communication between decision makers and modelers and to promote the use of models in decision-making for river basin management.     

Alcod idss: Assisting the Australian stock market surveillance team's review process

ALCOD is a cooperative multiagent intelligent decision support system to assist stock market surveillance teams to classify alerted noncompliant events transacted on the exchange. ALCOD facilitates the review of the classifications. The system combines heuristic approxi mate and causal reasoning and is centered around a relational database which is used as a control blackboard.     

启发式知识约简算法的研究与应用

从信息角度对决策系统中的属性重要度进行度量,在此基础上,提出一种知识约简的启发式算法,它以信道容量为启发式信息,减小了知识约简过程中的搜索空间。实例分析表明,本算法能够获得决策系统的一种良好的相对约简。     

基于粗糙集理论的客户关系管理

粗糙集理论是一种新型的数据挖掘和决策分析方法,利用粗糙集理论进行决策表的知识约简与决策规则挖掘已经成为研究热点。本文介绍了粗糙集的基本理论,通过决策属性支持度定义的条件属性对决策属性重要性的启发式信息,求取决策表的最小约简。并将该方法用于对企业客户进行分类,为客户关系管理的决策支持提供了新的解决方法。     

A Neural Network Integrated Decision Support System for Condition-Based Optimal Predictive Maintenance Policy

This paper develops an integrated neural-network-based decision support system for predictive maintenance of rotational equipment. The integrated system is platform-independent and is aimed at minimizing expected cost per unit operational time. The proposed system consists of three components. The first component develops a vibration-based degradation database through condition monitoring of rolling element bearings. In the second component, an artificial neural network model is developed to estimate the life percentile and failure times of roller bearings. This is then used to construct a marginal distribution. The third component consists of the construction of a cost matrix and probabilistic replacement model that optimizes the expected cost per unit time. Furthermore, the integrated system consists of a heuristic managerial decision rule for different scenarios of predictive and corrective cost compositions. Finally, the proposed system can be applied in various industries and different kinds of equipment that possess well-defined degradation characteristics     

The Importance of Active Exploration,Optical Flow,and Task Alignment for Spatial Learning in Desktop 3D Environments

The Decision Support Systems (DSS) field has grown rapidly, drawing technology from many disciplines and pursuing applications in a variety of domains but developing little underlying theoretical structure and poor linkage between research and practice. This article presents a classification scheme for DSS techniques that provides a common theoretical framework for DSS research and also structures and simplifies the process of designing application systems. The classification system is functional, grouping DSS techniques according to their ability to provide similar kinds of support (i.e., functions) to a human decision maker. It is also cognitively based, defining the kinds of support that decision makers need in terms of architectural features and procedural aspects of human cognition. The classification is expressed as a taxonomy, encompassing six primary classes of decision support techniques representing the six general kinds of cognitive support that human decision makers need. The six classes are process models, which assist in projecting the future course of complex processes; choice models, which support integration of decision criteria across aspects and/or alternatives; information control techniques, which help in storage, retrieval, organization, and integration of data and knowledge; analysis and reasoning techniques, which support application of problem-specific expert reasoning procedures; representation aids, which assist in expression and manipulation of a specific representation of a decision problem; and judgment amplification/refinement techniques, which help in quantification and debiasing of heuristic judgments. Additional distinctions are provided to distinguish the individual techniques in each of these primary categories. The taxonomy also has practical use as a design aid for decision support systems. The kinds of decision support needs represented by the taxonomy are general and can be used to guide the analysis and decomposition of a given decision prior to decision aid design. Specific needs for assistance can then be tied to specific computational techniques in the taxonomy. Methodological suggestions for using the taxonomy as a design aid are given.