Adaptive Neural Network Feedforward Control for Dynamically Substructured Systems

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Supply chain management: An opportunity for metaheuristics

In today’s highly competitive and global marketplace the pressure on organizations to find new ways to create and deliver value to customers grows ever stronger. In the last two decades, logistics and supply chain has moved to the center stage. There has been a growing recognition that it is through an effective management of the logistics function and the supply chain that the goal of cost reduction and service enhancement can be achieved. The key to success in Supply Chain Management (SCM) require heavy emphasis on integration of activities, cooperation, coordination and information sharing throughout the entire supply chain, from suppliers to customers. To be able to respond to the challenge of integration there is the need of sophisticated decision support systems based on powerful mathematical models and solution techniques, together with the advances in information and communication technologies. The industry and the academia have become increasingly interested in SCM to be able to respond to the problems and issues posed by the changes in the logistics and supply chain. We present a brief discussion on the important issues in SCM. We then argue that metaheuristics can play an important role in solving complex supply chain related problems derived by the importance of designing and managing the entire supply chain as a single entity. We will focus specially on the Iterated Local Search, Tabu Search and Scatter Search as the ones, but not limited to, with great potential to be used on solving the SCM related problems. We will present briefly some successful applications.Supply chain management, metaheuristics, iterated local search, tabu search and scatter search

Development and Validation of a Rule-based Time Series Complexity Scoring Technique to Support Design of Adaptive Forecasting DSS

Evidence from forecasting research gives reason to believe that understanding time series complexity can enable design of adaptive forecasting decision support systems (FDSSs) to positively support forecasting behaviors and accuracy of outcomes. Yet, such FDSS design capabilities have not been formally explored because there exists no systematic approach to identifying series complexity. This study describes the development and validation of a rule-based complexity scoring technique (CST) that generates a complexity score for time series using 12 rules that rely on 14 features of series. The rule-based schema was developed on 74 series and validated on 52 holdback series using well-accepted forecasting methods as benchmarks. A supporting experimental validation was conducted with 14 participants who generated 336 structured judgmental forecasts for sets of series classified as simple or complex by the CST. Benchmark comparisons validated the CST by confirming, as hypothesized, that forecasting accuracy was lower for series scored by the technique as complex when compared to the accuracy of those scored as simple. The study concludes with a comprehensive framework for design of FDSS that can integrate the CST to adaptively support forecasters under varied conditions of series complexity. The framework is founded on the concepts of restrictiveness and guidance and offers specific recommendations on how these elements can be built in FDSS to support complexity

Supporting decision making process with "Ideal" software agents: what do business executives want?

According to Simon’s (1977) decision making theory, intelligence is the first and most important phase in the decision making process. With the escalation of information resources available to business executives, it is becoming imperative to explore the potential and challenges of using agent-based systems to support the intelligence phase of decision-making. This research examines UK executives’ perceptions of using agent-based support systems and the criteria for design and development of their “ideal” intelligent software agents. The study adopted an inductive approach using focus groups to generate a preliminary set of design criteria of “ideal” agents. It then followed a deductive approach using semi-structured interviews to validate and enhance the criteria. This qualitative research has generated unique insights into executives’ perceptions of the design and use of agent-based support systems. The systematic content analysis of qualitative data led to the proposal and validation of design criteria at three levels. The findings revealed the most desirable criteria for agent based support systems from the end users’ point view. The design criteria can be used not only to guide intelligent agent system design but also system evaluation

A new model for the development of information systems

The most commonly used systems specification and design techniques in commercial computing are described and compared; Information Engineering as proposed by James Martin, A Framework for Information Definition-Muitiview proposed by Wood-Harper et al, Real-world Modeling as described by Jackson, Structured Analysis and Design as In Demarco, Yourdon and Constantine and Output-Oriented Structured Requirement Definition proposed by Orr. In addition, system prototyping is discussed, including the role of prototyping in large software development projects and as a tool for the design of human-computer interfaces. Other areas described and discussed include decision support systems (DSS) and knowledge based management support systems. The context is in the design and development approaches for DSS, prototyping for DSS, expert system for DSS and the integration of DSS and information system. The design and development of human-computer interface is also discussed in relation to user Interface complexity and adaptive interfaces. Further, the important issue of user involvement and support within the development process Is discussed. Thus, weaknesses of current approaches to the system development process are identified and a new model for the development of information system is proposed. In proposing the model, data and functional analysis structured method and methodology for decision support systems (DSS) development is presented including guidelines for the development of knowledge based DSS. The new proposed model is put to test in the design, development and implementation of large integrated commercial systems including DSS. Results and discussion on the use of the model is reported with special consideration to the users' and developers' view of the model. Finally the objectives of this research program are examined in relation to what has been achieved during this program of research. The prospect of using the model for the development of information systems are concluded with references to current and future goals

Decision Support Systems: Issues and Challenges; Proceedings of an International Task Force Meeting, June 23-25, 1980

This book reports on a three-day meeting on Decision Support Systems held at IIASA. IIASA's interest in sponsoring the meeting was spurred by several factors. First, the term DSS clearly is used in a wide range of contexts; we hoped to develop a deeper understanding of the term and the new field to which it refers. Second, we felt that ongoing work in the DSS field would be enhanced by interaction between professionals who had been working on such systems and people from fields that function as "resource disciplines" for DSS. Finally we wished to bring professionals from several nations together, from the east as well as the west, to share experiences and to assess the viability of the DSS concept in different cultures. The broad objectives set for this meeting were realized in a number of ways. Virtually all the participants testified that they had gained a deeper understanding of DSS, the role it can play in asssisting managers in organizations, and the need for further development in key areas