An integrative evaluation framework for intelligent decision support systems

Evaluation of the overall effectiveness of decision support systems (DSS) has been a research topic since the early 1980s. As artificial intelligence methods have been incorporated into systems to create intelligent decision support systems (IDSS), researchers have attempted to quantify the value of the additional capabilities. Despite the useful and relevant insights generated by previous research, existing evaluation methodologies offer only a fragmented and incomplete view of IDSS value and the contribution of its technical infrastructure. This paper proposes an integrative, multiple criteria IDSS evaluation framework through a model that links the decision value of an IDSS to both the outcome from, and process of, decision making and down to specific components of the IDSS. The proposed methodology provides the designer and developer specific guidance on the intelligent tools most useful for a specific user with a particular decision problem. The proposed framework is illustrated by evaluating an actual IDSS that coordinates management of urban infrastructures.     

Interactive multicriteria procedure exploiting a knowledge-based module to select electricity production alternatives: The CASTART system

For some decisional problems, it is difficult to have a set of potential alternatives a priori. This is true, for instance, when the object of the decision is dependent on an external environment, as in the case of disturbance situations in industrial processes. Computer assistance must support, first, the elaboration of a potential alternatives set and, second, the decision-making activity which will determine the decision elements leading to recovery from the disturbance. This paper deals with such a situation encountered in an electricity production context. An interactive multicriteria procedure exploiting a knowledge-based module to select electricity production alternatives is presented. Section 1 introduces the electricity production-consumption framework and identifies the Spinning Reserve Mobilization (SRM) function. Next, our contribution to the systematization and the experimentation of a decision-making process which supports the dispatcher confronted with an SRM situation is described. The decision-making process includes an intelligent module and a decisional procedure. The module and the procedure, and their interconnections, are developed in Section 2. Section 3 describes the DSS called CASTART which implements this process. An overview of DSS functionalities is given, as well as the results obtained by a dispatcher working in cooperation with the system, in the case of a simulated production incident.     

作战方案评估智能决策支持系统研究

作战决策是作战指挥的核心，作战指挥智能决策支持系统（IDSS）是用于辅助指挥员进行作战决策的计算机软件。它是指挥自动化的重要组成部分。本通过研究作战方案评估IDSS，探讨了作战指挥IDSS的结构及功能。     

Hit or miss: A decision support system framework for signing new musical talent

In the music industry, the process of signing new musical talent is one of the most complex decision-making problems. The decision, which is generally made by an artist and repertoire (A&R) team, involves consideration of various quantitative and qualitative criteria, and usually results in a low success rate. We conducted a series of mental model interviews with the aim of developing a decision support framework for A&R teams. This framework was validated by creating a decision support system that utilises multi-criteria decision analysis to support decision-making. Our framework and subsequent implementation of the decision support system involving decision rule and weighted sum methods show an improvement in the ability to analyse and decide on greater amounts of talent. This paper serves as a building block for developing systems to aid in this complex decision-making problem.     

A comparative study of methodologies for designing IDSSs

In this paper, we describe a comprehensive study conducted to understand the methodologies which are being used to design Intelligent Decision Support Systems (IDSSs) and to identify the key methodological problems and benefits with using these methodologies. This comprehensive study consists of two parts. The first part is two surveys which together identify the design methodologies being used by a group of IDSS developers and how acceptable they believe their methodologies were for designing their IDSSs. The second part is a comparison of six major formal IDSS design methodologies recently published in the literature and which are not yet known to many developers. This paper is presented to assist IDSS developers in understanding what support can be gained from using existing design methodologies and hence choose the correct one for their project. Furthermore, the paper may be used by IDSS developers to compare the way that they work with the approach proposed by other developers.     

Intelligent Support for Exploratory Data Analysis

Abstract

Exploratory data analysis (EDA) is as much a matter of strategy as of selecting specific statistical operations. We have developed a knowledge-based planning system, called AIDE, to help users with EDA. AIDE strikes a balance between conventional statistical packages, which need guidance for every step in the exploration, and autonomous systems, which leave the user entirely out of the decision-making process. AIDE's processing is based on artificial intelligence planning techniques, which give us a useful means of representing some types of statistical strategy. In this article we describe the design of AIDE and its behavior in exploring a small, complex data set.     

Team coordination in decision support projects

Coordination is one of the key design components for group decision support systems. Coordination strategies refer to the meta-level knowledge for enticing decision-making units to work effectively with each other. The actual problems of coordination in projects that make use of decision support tools (e.g. shared databases, mathematical, model bases, reporting tools, etc.) refer to information sharing concerning various types of information (e.g. the input and output data), coordination between group members in the use of mathematical models, and to the standardisation of the work process in the form of rigid procedures. The present paper analyses the coordination issues that arise in decision-support groupwork and introduces the job concept as a medium for modelling multi-user processes in decision-support projects. The paper discusses the conceptual design, empirical development and actual use of a system that supports model-based reasoning and aids the coordination of multi-user work-flow processes.     

A DSS design framework for competitive strategy formulation

This paper discusses the design framework that is actually being used in a large corporation for building a special purpose decision support system for competitive strategy formulation. The main features of the design framework employed are its ability to provide (i) support for decision-making through management science/operations research, (ii) decision-making support to managers of different levels and functions, (iii) methods to evaluate the performances of managers of different levels as well as that of the firm, (iv) to integrate a variety of decision-making models into a competitive strategy formulation model, and (v) to increase the effectiveness through an evolutionary and participatory development process.     

A structured modeling technology

This paper presents the methodological background and implementation of a structured modeling environment developed to meet the requirements of modeling activities undertaken to support intergovernmental negotiations aimed at improving European air quality. Although the motivation for the reported work came from the actual complex application presented in the paper, the actual scope of the paper covers a wide range of issues related to model-based decision-making support. The paper starts with a summary of the context of modeling composed of: the role of models in decision-making support; modeling paradigms; and state-of-the-art aspects of modeling complex problems. The modeling process is then characterized, and the requirement analysis for implementation of structured modeling is specified. The main part of the paper presents the structured modeling technology which was developed to support the implementation of the structured modeling principles for modeling complex problems.     

Knowledge-based manpower planning

The capacity performance of knowledge-based systems differs essentially from the one of traditional systems. The result is that already today these systems are employed in a number of application areas, and that the intensity of research and development in this field is very high.In this paper the development of a knowledge-based manpower planning system (KBMS) is described. The two main components of this system are a knowledge-based system developed for management planning and a model designed for manpower planning in personnel leasing companies. This model was realized as a KBMPS by applying the knowledge-based system, which was developed for management planning.     

Automated model construction: A logic based approach

Attempts to integrate Artificial Intelligence (AI) techniques into Decision Support Systems (DSS) have received much attention in recent years. Significant among these has been the application of knowledge-based techniques to support various phases of the modeling process. This paper describes a logic based approach to mechanically construct Linear Programming (LP) models from qualitative problem specifications and illustrates it in the context of production, distribution and inventory planning problems. Specifically, we describe the features of a first-order logic based formal language called PM which is at the heart of an implemented knowledge-based tool for model construction. Problems specified in PM define a logic model which is then used to generate problem-specific inferences, and as input to a set of logic programming procedures that perform model construction.     

Knowledge-based improvement: simulation and artificial intelligence for identifying and improving human decision-making in an operations system

The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as ‘knowledge-based improvement’ (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant.     

Data analysis and decision support

In this paper, we consider developmental lines of computer-assisted decision support (with consideration of knowledge-based approaches) for data analysis problems. First, we discuss some situations where it is obviously appropriate to apply computer-assisted decision support in connection with data analysis tasks. Then, a brief historical retrospect is given viewing the development of this area of research and its interfaces to knowledge-based approaches. Against this background we illustrate two prototypes of knowledge-based decision support systems for specific data-analysis problems related to fields of interest of our own. Finally, we indicate possible progress and future activities in this area.     

On the evolution of an intelligent maintenance optimization system

In this paper the author reviews the development of an intelligent maintenance optimization system over the past 16 years. The paper starts with discussion of the initial motivation behind developing the system and the designs of the early versions of a computer program to access maintenance history data and provide an analysis. The concept behind this system was gradually developed to incorporate a rule base for the selection of a suitable model for preventive maintenance (PM) scheduling and then to a fully developed knowledge-based system for decision support. The need to incorporate case-based reasoning thus creating a hybrid system that can learn with use in addition to using elicited knowledge from experts is discussed. The experience with system validation with two versions of the system is analysed. The paper also reviews the extensive fundamental work on developing appropriate PM models that can deal with real data patterns. Finally, the scope for future development is presented.     

A Knowledge-based Approach to Production Planning

This paper discusses a knowledge-based approach to production planning, the process of establishing production rates, work force levels and on-hand inventories for product families as a possible alternative to traditional operational research methods. It describes one company's experienced-based approach to production planning and how this was incorporated into a knowledge-based system. The production planning state-space, use of common planning constraints and heuristic procedures, and a prototype of a knowledge-based system that develops production plans for one product family are also described.     

Soft computing-based aggregation methods for human resource management

We are interested in the personnel selection problem. We have developed a flexible decision support system to help managers in their decision-making functions. This DSS simulates experts’ evaluations using ordered weighted average (OWA) aggregation operators, which assign different weights to different selection criteria. Moreover, we show an aggregation model based on efficiency analysis to put the candidates into an order.     

城市CIS物理关联脆弱性的动态分析方法

本文以城市关键基础设施系统(Critical Infrastructure System, CIS)为研究对象,将关键基础设施之间组成的复杂物理网络划分为个体层面、中观层面和系统层面三种类型,从物理关联势差的角度分析了城市CIS内部物理关联机理。根据城市CIS物理关联特性,引入元胞自动机(Cellular Automata, CA),以一种物理资源为主导建立模型,采用“自底向上”的方法,从微观关键基础设施活动推出宏观城市CIS表现。选取元胞连通性、元胞正常状态变化率和传输效率作为脆弱性评估指标,估算不同时间点城市CIS脆弱性的动态变化,实现物理关联脆弱性的动态分析,并运用加权平均值作为一段时间内城市CIS脆弱值。最后,以S城市为例,对物理关联脆弱性动态分析方法的合理性和有效性进行验证,评估结果为城市CIS保护提供支持。     

Computerized support systems for emergency decision making

In all fields of human society, occasional emergencies are almost inevitable. Once an emergency occurs, rapid and proper decision making is required. The purpose of this paper is to explore the design and development of computerized support systems for emergency decision making (EDM). First the characteristics of EDM problems are examined. Then, in view of limited human computer rationality, requirements for a computerized support system for EDM are determined. A conceptual structure for knowledge-based distributed emergency decision support systems is proposed. Finally, a prototype system for safety protection and disaster response in coal mines, developed using the proposed structure, is briefly described.This work is partly supported by the State Science and Technology Commission of China, the National Key Laboratory on Industrial Control of China and Fok Ying Tung Education Foundation.     

FMS scheduling as cooperative problem solving

This paper describes an FMS scheduling method that treats an FMS as a group of problem-solving agents cooperating to perform manufacturing jobs. The main thrusts of such a method include the ability to handle the dynamically changing production conditions, its taking into account the communication method, the improved reliability, and the use of distributed control. The paper emphasizes research issues associated with various aspects of the cooperative problem-solving method, including: (1) dynamic task assignments, (2) the coordination mechanism, and (3) knowledge-based scheduling as problem solving. A simulation study which compares the performance of the cooperative problem solving approach with that of the more traditional scheduling approaches is also reported.     

A knowledge-based multi-role decision support system for ore blending cost optimization of blast furnaces

Literature illustrates the difficulties in obtaining the lowest-cost optimal solution to an ore blending problem for blast furnaces by using the traditional trial-and-error method in iron and steel enterprises. To solve this problem, we developed a cost optimization model which we have implemented in a multi-role-based decision support system (DSS). On the basis of analyzing the business flow and working process of ore blending, we propose an architecture of DSS which is built based on multi-roles. This DSS construction pre-processes the data for materials and elements, builds a general database, abstracts the related optimal operations research models and introduces the reasoning mechanism of an expert system. A non-linear model of ore blending for blast furnaces and its solutions are provided. A database, a model base and a knowledge base are integrated into the expert system-based multi-role DSS to meet the different demands of data, information and decision-making knowledge for the various roles of users. A comparison of the results for the DSS and the trial-and-error method is provided. The system has produced excellent economic benefits since it was implemented at the Xiangtan Iron & Steel Group Co. Ltd., China.