AI-Concepts and OR-tools in advanced DSS

The relation between Decision Support Systems and Expert Systems (as far as they are related to decisions) is discussed. We distinguish three different levels: the conceptual, the model-theoretic and the data structure level. In analyzing the differences between these two approaches we isolate the problems which arise in attempts to amalgamate the two different methodologies.     

Design of a decision support system for detailed scheduling

Design of a decision support system ( ) based on a simulation model of the detailed scheduling activities in a tractor manufacturing company is dealt with. The system analysis phase of the design process is overviewed briefly. The main decision points involved and the problems faced in the production planning and control subsystem are presented. Expectations from a for detailed scheduling are discussed and performance measures are defined. The links between computer programs are shown. Utilization of the designed for production planning and control oriented decision making is discussed using decision tables.     

A new class of intelligent knowledge-based systems with an optimisation-based inference engine

In this paper we describe a new class of intelligent knowledge-based system (IKBS) which can be used principally for managerial decision making applications. This class of applications often requires a framework for knowledge acquisition which allows the system to use the knowledge of several experts. In addition, since in most business decision making the objective is maximise profits, there is a need for an inference engine which allows optimisation to be carried out. The new class of IKBS which is described in this paper has both these properties, i.e., the ability to use the knowledge of multiple experts in a convenient way and an inference engine which by performing optimisations is able to pick out the profit maximising decisions. As an illustration of these concepts, a system for allocation decision making is described. The system ‘Retail-opt’ allows the user to solve problems like allocation of space in retail outlets, allocation of space in mail order catalogues, pricing policy decisions for discounted airline tickets, etc. In the paper, the basic concepts behind ‘Retail-opt’ are described and an application of ‘Retail-opt’ to the problem of retail space allocation in a Scandinavian Department Store is given. A number of other systems which use these concepts for more complicated competitive decision making situations are also described.     

An interactive algorithm for portfolio bi-criteria optimization of R&D projects in public organizations

In this paper we propose an interactive algorithm for the selection of portfolios of research and development (R&D) projects in public organizations based on a bi-criteria optimization model, the need for such a model arises when the decision maker (DM) does not trust enough on the portfolio quality measure. This algorithm efficiently exploits the structure and nature of the problem to support the DM. An interesting proposal is also the representation of a portfolio as a set of “rules of support/rejection”; in this way the DM can not only valuate the portfolio by its numerical measures but also compare against his/her beliefs in a way that is more natural for him, which also allows for supporting with more arguments the solution obtained so far. Rough set methodology is employed for rule discovering. The text was submitted by autors in English.     

Database inferencing for decision support

The use of databases for management decision support requires flexible inferencing mechanisms. The use of logic programming for these purposes is explored. To be flexible, however, this requires the logical decomposition of the database into elementary predicates.     

Problem-solving strategies for DSS design

For a successful design and implementation of DSS, competent understanding of decision-making processes in organizational settings and sensitivity to the interpersonal and organizational dimensions of the relationship between decision-makers and DSS designers are essential. Towards this end, a three-faceted strategy is broadly outlined and proposed. The strategy encompasses (a) a diagnostic attitude to the empirical tasks and contextual properties of decision-making, (b) emphasis on decision-makers rather than on decisions, and (c) the concept of DSS design as a joint undertaking for organizational problem-solving.     

遗传算法多目标优化及其在决策支持系统中的应用

针对区域经济发展的现状,提出了改进的经济多目标优化模型。并用多目标遗传算法来对模型进行优化计算与仿真。在此基础上,通过将MATLAB与Web技术相结合,提出了一种新的基于B/S模式的决策支持系统的解决方案。     

销售利润决策支持系统的开发

在系统需求分析的基础上完成了销售利润决策支持系统的开发，解决了销售利润决策中涉及的许多半结构化问题。该系统总体设计为八个子系统，融合了辅助决策、数据库和管理信息系统的相关技术。在系统实现过程中对决策支持系统的模型库、数据库和方法库的接口问题进行了探讨与实践。     

The development of decision support systems in China

The short history of development of Decision Support Systems (DSS) in China is described. Some examples of DSS are illustrated. And four main directions involved in the DSS development are pointed out.     

The emerging role of DSS: From passive to active

Existing decision support systems (DSS) are passive in their operation; their sole aim is to help maintain the current position of the firm. Conversely, Information Technology has recently adopted a more active role in the corporate strategy. This suggests that DSS could also undertake a far more active stance by identifying gaps in existing operations and suggesting ways to strengthen the standing of the firm. We renamed DSS containing this characteristics as Active Decision Support Systems (or ADSS) to emphasize their new orientation. Managers draw on their knowledge to suggest ways of alleviating problems or exploiting opportunities, and to evaluate each alternative in relation to the corporate objectives. Patterns and alternatives must be identified and incorporated into the ADSS. They should then be compared with the current position of the firm in order to determine whether any such problems/opportunities exist. This paper proposes an approach for designing ADSS; it introduces a knowledge-based component within the DSS framework and applies the concept of triggers to control the invocation of rule sets. A logical structure for ADSS is provided and the system use is illustrated through an example of strategic management. Issues for future research are highlighted.     

Designing for creativity: Considerations for DSS development

Decision support systems (DSS) can be designed to support the creative and intuitive aspects of decision making. Our purpose is to provide a new perspective for the design of DSS by focusing on the important external factors that have been shown to influence creative activity. Design guidelines can then be developed by viewing a DSS as a special environment that incorporates these factors.     

NEGO — Group decision support system

There are two major frameworks for decision making: maximizing and satisficing. A combination of both may be used to describe group decision making (GDM). In the satisficing approach, decision makers (DMs) formulate aspiriation levels or demands which take the form of constraints. Choosing from among different decisions, DMs take into account their preferences or wants, which take the form of objective functions.GDM is divided into two stages: first, each DM makes a decision, and second, DMs negotiate so as to achieve a compromise decision. Negotiating is an iterative process. Negotiations are completed when all demands have been met.The group decision support system “NEGO” assists DMs in finding a compromise. It has been used for solving a GDM problem at the corporate level and is currently utilized in management courses.     

SD-Solver: Towards a “multidirectional” CLP-based simulation tool

SD-Solver is a general purpose simulation environment grounded on the Constraint Logic Programming technology. Its main aim is to facilitate the development of Decision Support Systems based on dynamic models. Using SD-Solver, forward and backward simulations can be performed -to some extent- on the basis of a single model. This paper outlines the underlying framework and presents the most important aspects of SD-Solver using two elementary financial examples.[/p]     

Can DSS evolve without changing our view of the concept of ‘problem’?

DSS have almost exclusively been presented in the context of problem solving. But the term ‘problem’ is never defined. It is unfortunately a fairly ambiguous term whose meaning oscillates from the observation of an unsatisfactory, objective reality which must be corrected, to the subjective representation of one or more actors faced with a reality which he or they perceived as unsatisfactory. The first of these interpretations (i.e., a problem as an unsatisfactory objective reality) implicitly dominates the DSS literature and design methods, which does not avoid the hidden major stumbling block of ‘problem definition’. We believe that the adoption of the second interpretation (i.e., a problem as a subjective representation) is a better guarantee of the effectiveness of DSS and leads to different design methods (we propose one based on a systemic method oriented towards ‘soft problems’) and opens new horizons for potential application to DSS.     

Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software

Forecasting, using historic time-series data, has become an important tool for fisheries management. ARIMA modeling, Modeling for Optimal Forecasting techniques and Decision Support Systems based on fuzzy mathematics may be used to predict the general trend of a given fish landings time-series with increased reliability and accuracy. The present paper applies these three modeling methods to forecast anchovy fish catches landed in a given port (Thessaloniki, Greece) during 1979–2000 and hake and bonito total fish catches during 1982–2000. The paper attempts to assess the model's accuracy by comparing model results to the actual monthly fish catches of the year 2000. According to the measures of forecasting accuracy established, the best forecasting performance for anchovy was shown by the DSS model (MAPE = 28.06%, RMSE = 76.56, U-statistic = 0.67 and R² = 0.69). The optimal forecasting technique of genetic modeling improved significantly the forecasting values obtained by the selected ARIMA model. Similarly, the DSS model showed a noteworthy forecasting efficiency for the prediction of hake landings, during the year 2000 (MAPE = 2.88%, RMSE = 13.75, U-statistic = 0.19 and R² = 0.98), as compared to the other two modeling techniques. Optimal forecasting produced by combined modeling scored better than application of the simple ARIMA model. Overall, DSS results showed that the Fuzzy Expected Intervals methodology could be used as a very reliable tool for short-term predictions of fishery landings.     

Systematic modeling and model handling for manpower planning systems

The primary purpose of this paper is to show an efficient way of handling models and model data in a decision support system, in which it is usual to consider several variants of a model. The model data primarily consist of model-defining data, but the same approach may be used for the generated results as well. By efficient handling is meant the handling by the computer as well as by the user. For the user it is particularly important that new models can be conveniently defined as variants of existing models. The approach is introduced within the context of a decision support system for manpower planning based on Markov models. In the mean time the same approach has been used for the implementation of other decision support systems and has been found to be more generally applicable.     

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.     

Retrieval for decision support resources by structured models

The number of available DSS within organizational Intranets will soon require efficient retrieval functionality. While Web retrieval technology performs excellent on documents, computational services need approaches that capture the semantics of resources. We present a retrieval approach that uses a variant of Structured Modeling to represent resources. It allows the use of similarity of models for retrieval. Exact similarity computation is shown to be NP-hard, and efficient heuristics for similarity computation and filter algorithms are introduced. We report an evaluation in a classroom experiment and give computational results on a benchmark library.     

Knowledge engineering concepts for decision support system design and implementation

The design of computer-based systems that simulate expert human consulting by drawing on large amounts of task-specific knowledge has been a major research activity of applied artificial intelligence over the last ten years. Building decision support systems that incorporate aspects of this research is a promising new field. The purpose of this paper is to discuss concepts of “knowledge engineering” that are most relevant in designing and building knowledge-based decision support systems.