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.     

A framework for group decision support systems: Combining AI tools and OR techniques

Work on the implementation of Group Decision Support Systems has to exploit recent advancements of computer science. Existing frameworks for single-user Decision Support Systems, based on well-established Operations Research methods such as Multicriteria Decision Making techniques, have to be integrated with successful technical developments in electronic communication and computing. Starting from the presentation of the related Operations Research background, this paper proceeds by discussing challenges coming from the areas of Computer-Supported Cooperative Work and Information Systems on the World Wide Web platform. Based on this discussion, a framework for an ‘open’, computer-mediated Group Decision Support System is proposed. The term ‘open’ is related to a platform-independent system, which can efficiently support alternative types of goals and control protocols between its users.     

A new decision support framework for managing foot-and-mouth disease epidemics

Animal disease epidemics such as the foot-and-mouth disease (FMD) pose recurrent threat to countries with intensive livestock production. Efficient FMD control is crucial in limiting the damage of FMD epidemics and securing food production. Decision making in FMD control involves a hierarchy of decisions made at strategic, tactical, and operational levels. These decisions are interdependent and have to be made under uncertainty about future development of the epidemic. Addressing this decision problem, this paper presents a new decision-support framework based on multi-level hierarchic Markov processes (MLHMP). The MLHMP model simultaneously optimizes decisions at strategic, tactical, and operational levels, using Bayesian forecasting methods to model uncertainty and learning about the epidemic. As illustrated by the example, the framework is especially useful in contingency planning for future FMD epidemics.     

A strategic decision support system framework for energy-efficient technology investments

Energy systems optimization under uncertainty is increasing in its importance due to on-going global de-regulation of the energy sector and the setting of environmental and efficiency targets which generate new multi-agent risks requiring a model-based stakeholders dialogue and new systemic regulations. This paper develops an integrated framework for decision support systems (DSS) for the optimal planning and operation of a building infrastructure under appearing systemic de-regulations and risks. The DSS relies on a new two-stage, dynamic stochastic optimization model with moving random time horizons bounded by stopping time moments. This allows to model impacts of potential extreme events and structural changes emerging from a stakeholders dialogue, which may occur at any moment of the decision making process. The stopping time moments induce endogenous risk aversion in strategic decisions in a form of dynamic VaR-type systemic risk measures dependent on the system’s structure. The DSS implementation via an algebraic modeling language (AML) provides an environment that enforces the necessary stakeholders dialogue for robust planning and operation of a building infrastructure. Such a framework allows the representation and solution of building infrastructure systems optimization problems, to be implemented at the building level to confront rising systemic economic and environmental global changes.     

A common framework and taxonomy for multicriteria scheduling problems with interfering and competing jobs: Multi-agent scheduling problems

Most classical scheduling research assumes that the objectives sought are common to all jobs to be scheduled. However, many real-life applications can be modeled by considering different sets of jobs, each one with its own objective(s), and an increasing number of papers addressing these problems has appeared over the last few years. Since so far the area lacks a unified view, the studied problems have received different names (such as interfering jobs, multi-agent scheduling, and mixed-criteria), some authors do not seem to be aware of important contributions in related problems, and solution procedures are often developed without taking into account existing ones. Therefore, the topic is in need of a common framework that allows for a systematic recollection of existing contributions, as well as a clear definition of the main research avenues. In this paper we review multicriteria scheduling problems involving two or more sets of jobs and propose an unified framework providing a common definition, name and notation for these problems. Moreover, we systematically review and classify the existing contributions in terms of the complexity of the problems and the proposed solution procedures, discuss the main advances, and point out future research lines in the topic.     

A preference disaggregation decision support system for financial classification problems

Classification is one of the most extensively studied problems in the fields of multivariate statistical analysis, operations research and artificial intelligence. Decisions involving a classification of the alternative solutions are of major interest in finance, since several financial decision problems are best studied by classifying a set of alternative solutions (firms, loan applications, investment projects, etc.) in predefined classes. This paper proposes an alternative approach to the classical statistical methodologies that have been extensively used for the study of financial classification problems. The proposed methodology combines the preference disaggregation approach (a multicriteria decision aid method) with decision support systems. More specifically, the FINancial CLASsification (FINCLAS) multicriteria decision support system is presented. The system incorporates a plethora of financial modeling tools, along with powerful preference disaggregation methods that lead to the development of additive utility models for the classification of the considered alternatives into predefined classes. An application in credit granting is used to illustrate the capabilities of the system.     

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.     

An intelligent fuzzy agent for meeting scheduling decision support system

An Intelligent Fuzzy Agent for Meeting Scheduling Decision Support System is proposed in this paper. The proposed Intelligent Fuzzy Agent including Meeting Negotiation Agent, Fuzzy Inference Agent and Genetic Learning Agent can search and decide the suitable meeting time for the specified meeting in an organization. When a meeting host requests a meeting, the Meeting Negotiation Agent immediately sends the invitees’ names to Meeting Scheduling Decision Support System for retrieving their schedules from Group Calendar Data Base, then Meeting Scheduling Decision Support System will compute the possible meeting time and respond the results to Meeting Negotiation Agent. Moreover, the Fuzzy Inference Agent infers the adequate meeting time based on the information provided by Meeting Negotiation Agent and Personalized Knowledge Base, and sends the computing result back to Meeting Scheduling Decision Support System. The meeting host will decide the final meeting time based on Meeting Scheduling Decision Support System and announce the meeting information by various devices including PDA, WAP, FAX, or E-mail. Furthermore, the invitees’ decisions for attending the meeting or not will be stored into Meeting Information Knowledge Base, then the Genetic Learning Agent will adjust the Personalized Knowledge Base for the next meeting. By the experimental results, the proposed Intelligent Fuzzy Agent can work efficiently and effectively for Meeting Scheduling Decision Support System.     

Organizational decision support systems: Centralized support for decentralized organizations

Organizations are becoming increasingly decentralized in their operations and decision making. Advanced information technologies provide the glue that holds such organizations together and facilitates their operations. Organizational decision support systems (ODSSs) provide mechanisms for ensuring that the decisions being made throughout such organizations are consistent with each other and with the overall organization goals. Through means of an ODSS, information and guidance is automatically passed from higher levels to lower levels for use in decision-making models.     

A decision theory based scheduling procedure for single-machine weighted earliness and tardiness problems

We consider the dynamic single-machine scheduling problem where the objective is to minimize the sum of weighted earliness and weighted tardiness costs. A single pass heuristic, based on decision theory, is developed for constructing schedules. The heuristic permits schedules with idle time between jobs and behaves like a dispatching procedure. The performance of the new heuristic is examined using 116 published problems for which the optimum solution is known. Its performance is also investigated using 540 randomly generated problems covering a variety of conditions by comparing it to two well known dispatching procedures, adapted for dynamic early/tardy problems. The results indicate that the heuristic performs very well.     

A two-phase decision support framework for the automatic screening of digital fundus images

In this paper we give a brief review on the present status of automated detection systems describe for the screening of diabetic retinopathy. We further detail an enhanced detection procedure that consists of two steps. First, a pre-screening algorithm is considered to classify the input digital fundus images based on the severity of abnormalities. If an image is found to be seriously abnormal, it will not be analysed further with robust lesion detector algorithms. As a further improvement, we introduce a novel feature extraction approach based on clinical observations. The second step of the proposed method detects regions of interest with possible lesions on the images that previously passed the pre-screening step. These regions will serve as input to the specific lesion detectors for detailed analysis. This procedure can increase the computational performance of a screening system. Experimental results show that both two steps of the proposed approach are capable to efficiently exclude a large amount of data from further processing, thus, to decrease the computational burden of the automatic screening system.     

Interactive bicriterion decision support for a large scale industrial scheduling system

In this paper we develop an interactive decision analysis approach to treat a large scale bicriterion integer programming problem, addressing a real world assembly line scheduling problem of a manufacturing company. This company receives periodically a set of orders for the production of specific items (jobs) through a number of specialised production (assembly) lines. The paper presents a non compensatory approach based on an interactive implementation of the ε-constraint method that enables the decision maker to achieve a satisfactory goal for each objective separately. In fact, the method generates and evaluates a large number of non dominated solutions that constitute a representative sample of the criteria ranges. The experience with a specific numerical example shows the efficiency and usefulness of the proposed model in solving large scale bicriterion industrial integer programming problems, highlighting at the same time the modelling limitations.     

A fuzzy multiobjective programming algorithm in decision support systems

Multicriteria analysis is one of the analytical functions in the problem processing system of decision support systems (DSS). In this paper, an interactive and iterative fuzzy programming method for solving a quasi-optimization problem in complex decisions under constraints involving a multiple objective function is proposed. Comparing with an adapted gradient search method, a surrogate worth tradeoff method, and a Zionts—Wallenius method, an approximate preference structure is emphasized in the proposed method.     

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.     

Expert systems approach to decision support

An approach to building decision support systems based on expert systems methods is proposed. The relatively weak basic assumptions include only stationarity (repeatability) of the decision in the same environment (circumstances) and ordering of the values of all the attributes with regard to the decision maker's preferences. The proposed approach is aimed at reflecting the experienced domain expert's and decision maker's knowledge and preferences, both in the form of facts and rasoning rules. Among the purposes of the described class of decision support systems there are the storage and retrieval of the expert's knowledge and decisions, decision making support and ranking of admissible decision alternatives. A general model of the decision process is proposed and a language for representation of the expert's knowledge is introduced in brief. The structure, reasoning control, and an example of application of the proposed system are discussed and possible further extensions are pointed out.