Exploring new directions for research in problem structuring methods: on the role of cognitive style

Prior research has argued that cognitive style can have a significant impact on group decision making. In addition, several scholars have proposed that cognitive style can play a key role in the design and use of group decision support systems. However, cognitive style has not received a great deal of attention in the problem structuring methods (PSMs) community. This is surprising, given that PSMs are specifically developed to support a group in their decision making. The purpose of this paper is thus to examine the significance of cognitive style within PSMs. The paper identifies and explores the role of four different cognitive style functions in problem structuring interventions. This analysis is carried out by focusing on the different tasks embedded within a group process supported by PSMs. Implications for the research and practice of PSMs are then discussed.     

Integrated decision support for aviation safety inspectors

This paper presents a systems viewpoint for developing an advanced decision support system for aircraft safety inspectors. Research results from a Federal Aviation Administration (FAA) sponsored project to use neural network and expert systems technology to analyze aircraft maintenance databases are summarized. One of the main objectives of this research is to define more refined “alert” indicators for national comparison purposes that can signal potential problem areas by aircraft type for safety inspector consideration.

Integration aspects are addressed on two levels: (1) integration of the various technical components of the decision support system, and (2) integration of the decision support system with individual behavior, management systems and organizational structure, as well as corporate culture across both formal and informal dimensions. The paper summarizes the creation of strategic “inspection profiles” for aging aircraft and reliability curve fitting for structural components both based upon using neural network technology. Also, the potential use of a model-based expert system to facilitate field inspection diagnostics is presented. Finally, a framework for developing an intelligent decision system to support aircraft safety inspections is proposed that links expert systems, neural networks, as well as a paradigm of the decision making process typically used in unstructured situations.     

Bad numbers: coping with flawed decision support

Evidence is accumulating that many spreadsheet-based decision support systems contain errors. These errors can result in bad numbers, which in turn could lead to bad decisions. We review the literature on the origins and consequences of bad (erroneous) numbers produced by models and/or decision support systems built around them. Then, we present a case study in which an experiment in visual support for a sequential decision-making task was tainted by bad numbers. Both the literature review and the experiment indicate a robust human ability to overcome flawed decision support. We conclude with questions that need to be addressed in order to better understand the capabilities of humans to deal with erroneous results from decision support systems.     

The Validation of Expert Systems — Contrasts with Traditional Methods

Recent research has demonstrated that little validation is carried out during and after the building of operational research models for decision support. In a subject area that has been of great interest for more than a decade, this reflects the fact that validation is not yet firmly embedded into the methodology of mathematical modelling that underpins conventional decision support systems. With the interest now being shown in expert systems, it is important to devise an appropriate methodology of validation for such systems. This paper contributes to this development by describing the types of validation that should be performed with decision support systems, and compares and contrasts the features found in the validation of mathematical models with those required of validation of expert systems.     

Supporting Learning in Evolving Dynamic Environments

In dynamic decision environments such as direct sales, customer support, and electronically mediated bargaining, decision makers execute sequences of interdependent decisions under time pressure. Past decision support systems have focused on substituting for decision makers' cognitive deficits by relieving them of the need to explicitly account for sequential dependencies. However, these systems themselves are fragile to change and, further, do not enhance decision makers' own adaptive capacities. This study presents an alternative strategy that defines information systems requirements in terms of enhancing decision makers' adaptation. In so doing, the study introduces a simulation model of how decision makers learn patterns of sequential dependency. When a system was used to manage workflows in a way predicted by the model to enhance learning, decision makers in a bargaining experiment learned underlying patterns of sequential dependencythat helped them adapt to new situations. This result is rare if not unique in the study of dynamic decision environments. It indicates that a shift, away from substituting for short-term deficits and toward enhancing pattern learning, can substantially improve the effectiveness of decision support in dynamic environments. Based on the specific findings in this study, this shift has important implications for designing information system workflows and potential future applications in interface design.     

Modelling and decision support in physiology and medicine: a methodological framework with lllustration

This paper presents a methodological framework for modelling that has found wide application in the complex domains of physiology and medicine. The processes of model validation are centrally embedded within this framework. The parallelism between modelling per se and the development of model-based decision support systems is then considered, showing that it is possible to devise a unified methodological framework which encompasses the requirements both of model validation and decision support system evaluation. The applicability of the framework is demonstrated in the validation of a mathematical model of blood glucose dynamics; and in the development and evaluation of decision support systems such as those which are aimed at addressing the problem of advising the insulin-dependent diabetic patient on the adjustment of insulin dosage     

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.     

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.     

Operational risk: Emerging markets,sectors and measurement

The role of decision support systems in mitigating operational risks in firms is well established. However, there is a lack of investment in decision support systems in emerging markets, even though inadequate operational risk management is a key cause of discouraging external investment. This has also been exacerbated by insufficient understanding of operational risk in emerging markets, which can be attributed to past operational risk measurement techniques, limited studies on emerging markets and inadequate data.     

University students’ perspectives on diagnostic testing in mathematics

Many universities issue mathematical diagnostic tests to incoming first-year students, covering a range of the basic concepts with which they should be comfortable from secondary school. As far as many lecturers are concerned, the purpose of this test is to determine the students' mathematical knowledge on entry. It should also provide an early indication of which students are likely to need additional help, and hopefully encourage such students to avail of extra support mechanisms at an early stage. However, it is not clear that students recognize these intentions and there is a fear that students who score poorly in the test will have their confidence further damaged in relation to mathematics and will be reluctant to seek help. To this end, a questionnaire was developed to explore students’ perspectives on diagnostic testing. Analysis of responses received to the questionnaire provided an interesting insight into students’ perspectives including the optimum time to conduct such a test, their views on the aims of diagnostic testing, whether they feel that testing is a good idea, and their attitudes to the support systems put in place to help those who scored poorly in the test.     

A fuzzy approach to using expert knowledge for tuning paper machines

Paper machines are very complex production systems, but their scope is simple: they consume materials and resources, called factors, to produce paper, which in turn can be described by its characteristics. In this paper, a decision support system is developed in cooperation with an industrial partner to help them with operational decision making when tuning a paper machine. The decision support system was developed in two phases. Firstly, the knowledge of experts is collected and stored in the form of a fuzzy ontology. Secondly, this knowledge is made usable so that a user of the decision support system can specify what characteristics of the produced paper to increase or to decrease and be returned with a recommendation on what factors to change. In this paper, we will work out the optimization problems on which the system is based. Additionally to a basic goal programming model, two extensions are explored, accounting for uncertainty and non-linearity, respectively.     

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.     

Spatial multicriteria decision support for robust land-use suitability: The case of landfill site selection in Northeastern Greece

Multicriteria spatial decision support systems (MC-SDSS) have emerged as an integration of geographical information systems (GIS) and multiple criteria decision aid (MCDA) methods for incorporating conflicting objectives and decision makers’ preferences into spatial decision models. In this paper, we present spatial UTASTAR (S-UTASTAR), a raster-based MC-SDSS for land-use suitability analysis. The multicriteria component of the system is based on the UTA-type disaggregation-aggregation approach. S-UTASTAR is applied in a raster-based case study concerning land-use suitability analysis to identify appropriate municipal solid waste landfill (MSW) sites in Northeast Greece. Moreover, robustness analysis tools are implemented to guarantee robust decision support results. More specifically, during the aggregation phase, the Stochastic Multiobjective Acceptability Analysis (SMAA) is used to indicate the frequency at which a site achieves the best ranking positions within a large set of alternative landfill sites.     

Intelligent management systems in operations: a review

Operations management is an area that has recently started to benefit from the use of AI techniques such as expert systems, neural networks and genetic algorithms. These techniques can extend the usefulness of OR modelling and enable new types of decision tasks to be supported by computer-based systems. This paper attempts to review ‘intelligent’ decision support systems and their potential to address some of the problems faced in various areas of operations management. Some useful techniques developed in the field of artificial intelligence are outlined and examples of attempts to use these approaches to support decision making in various areas of operations management are described. Recognising the scale of a complete review of all these areas, emphasis has been given to the most significant and more recent publications.     

Comparison of some AI and statistical classification methods for a marketing case

Recent progress in data processing technology has made the accumulation and systematic organization of large volumes of data a routine activity. As a result of these developments, there is an increasing need for data-based or data-driven methods of model development. This paper describes data-driven classification methods and shows that the automatic development and refinement of decision support models is now possible when the machine is given a large (or sometimes even a small) amount of observations that express instances of a certain task domain. The classifier obtained may be used to build a decision support system, to refine or update an existing system and to understand or improve a decision-making process. The described AI classification methods are compared with statistical classification methods for a marketing application. They can act as a basis for data-driven decision support systems that have two basic components: an automated knowledge module and an advice module or, in different terms, an automated knowledge acquisition/retrieval module and a knowledge processing module. When these modules are integrated or linked, a decision support system can be created which enables an organization to make better-quality decisions, with reduced variance, probably using fewer people.     

O.R. and Micros: O.R. Workers,Information Systems Analysts and the Challenge of the Micro

An assessment of the effects of microcomputing on O.R. should take account of the impact of micros on other professional groups involved in the provision of information and analysis to managers. The paper examines the likely changes in the role of information systems analysts associated with present trends in computer-based information systems and, by concentrating on the decision support systems area, argues that O.R. workers and systems analysts may find themselves competing for new roles in ways which make if difficult to set out the contribution of each in terms of what they do at present. A modest proposal is made for a new form of team for the development of decision support systems. This incorporates a division of skills which cuts across existing lines of differentiation and foresees an increased contribution on the part of user-managers.     

Planning performance based contracts considering reliability and uncertain system usage

This paper investigates a novel quantitative approach for planning and contracting performance-based logistics in the presence of uncertain system usage. Our efforts focus on an integrated service delivery environment where the manufacturer develops capital-intensive systems and also provides after-sales support. We propose an analytical model to characterize system operational availability by comprehending five performance drivers: inherent failure rate, usage rate, spare parts inventory, repair time, and the fleet size. This analytical insight into the system performance allows the service supplier to minimize the total cost across system design, production, maintenance, and repair. Two contracting schemes are investigated under cost minimization and profit maximization schemes. For the first time in literature, reliability design and service parts logistics are seamlessly integrated into one decision support model for improving operational availability while lowering the lifecycle cost. Numerical examples are provided to demonstrate the applicability and the effectiveness of the proposed decision support tool.     

An interactive decision-making approach based on intelligent systems to redraw district boundaries in regional planning

Environmental planning decisions have become a major challenge and are receiving increasing social attention. To contend with the absence of a decision theory in this domain and to solve conflicts in group decision-making, an interactive decision approach has been defined and supported by a computer-based system to automate the different steps of the modelling process. In this paper, the emphasis is on the advent of intelligent systems and the improvement of management support. This support enables individual decision-makers to augment their knowledge about a problem domain which results in a gain in productivity. The different steps of the approach are described and illustrated through a worked example which is a specific decision problem and deals with redrawing the district boundaries in regional planning.     

Verbal ordinal classification with multicriteria decision aiding

Professionals in neuropsychology usually perform diagnoses of patients’ behaviour in a verbal rather than in a numerical form. This fact generates interest in decision support systems that process verbal data. It also motivates us to develop methods for the classification of such data. In this paper, we describe ways of aiding classification of a discrete set of objects, evaluated on set of criteria that may have verbal estimations, into ordered decision classes. In some situations, there is no explicit additional information available, while in others it is possible to order the criteria lexicographically. We consider both of these cases. The proposed Dichotomic Classification (DC) method is based on the principles of Verbal Decision Analysis (VDA). Verbal Decision Analysis methods are especially helpful when verbal data, in criteria values, are to be handled. When compared to the previously developed Verbal Decision Analysis classification methods, Dichotomic Classification method performs better on the same data sets and is able to cope with larger sizes of the object sets to be classified. We present an interactive classification procedure, estimate the effectiveness and computational complexity of the new method and compare it to one of the previously developed Verbal Decision Analysis methods. The developed and studied methods are implemented in the framework of a decision support system, and the results of testing on artificial sets of data are reported.