Calls for papers
International Journal of Computer Applications in Technology
Special Issue on: "Decision Support Systems for Collaborative Design and Manufacturing"
Dr. Xuan F. Zha, National Institute of Standards and Technology, Gaithersburg, USA
Dr. Weiming Shen, National Research Council Canada, Canada
Decision Support Systems (DSS) are computer-based systems that support some or several phases of the individual, team, organisational or inter-organisational decision making process. DSS may involve many technologies drawn from multiple disciplines, including information science, cognitive science, computer science, economics, engineering, business and management science, and statistics, among others. DSS can be categorised based on the complexity of the decision problem space and group composition. The combination of the dimensions of the problem space and group compositions in distributed collaborative environments in terms of time, spatial distribution and interaction results in a set of requirements that need to be addressed in different phases of the decision-making process.
Research on DSS can be focused on both technical and organisational issues. From a technical perspective, advances in information technology and artificial intelligence have improved the support capabilities for DSS; in particular, artificial intelligence (AI)-based technologies such as knowledge-based systems, neural networks, fuzzy logic, cased-based reasoning, genetic algorithms, machine learning, data mining algorithms, intelligent agents, soft computing, and user intelligent interfaces, among others, have been recognised as significant enhancement tools for DSS. Knowledge-intensive intelligent decision support systems (KIIDSS) have now become more critical and widely applied in collaborative decision-making processes, in which the decision support is exploited from the perspective of synthesis of collaborative decision-making process modelling, knowledge management, and decision problem solving support.
Collaborative design and manufacturing essentially involves decision-making processes that require rigorous evaluation, comparison and selection of design and manufacturing solution alternatives and optimisation from a global perspective. Increasing design and manufacturing knowledge and supporting better collaborations among customers, engineers and partners to make intelligent decisions can result in higher quality products.
This special issue is aimed at reviewing the state-of-the-art research and applications, addressing the major challenges and issues of developing and applying intelligent decision support systems, in particular KIIDSS, for collaborative design and manufacturing. The guest editors invite authors to submit their original papers to this special issue. Papers of both theoretical and practical application nature are welcome. The guest editors are also interested in authoritative review of the state of the art and directions for future research in the field.Subject Coverage
The scope of the issue covers, but not limited to:
- Decision theories, including game theory, utility theory, probability theory, fuzzy set theory, Bayesian theory, among others for collaborative design and manufacturing
- Collaborative decision support models, framework and architecture, and infrastructure, including communication, coordination, integration and interoperation strategies, mechanisms, protocols, and standards for collaborative design and manufacturing
- Development and applications of intelligent and knowledge intensive decision support systems deploying specific AI based technologies, such as knowledge-based systems, neural networks, fuzzy logic, cased-based reasoning, genetic algorithms, machine learning, data mining algorithms, intelligent agents, soft computing, and user intelligent interfaces, among others, for collaborative design and manufacturing
- Decision support systems for collaborative enterprise business and management
- Decision support systems for collaborative supply chain management
- Decision support systems for collaborative product lifecycle management
Notes for Prospective Authors
Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere
All papers are refereed through a peer review process. A guide for authors, sample copies and other relevant information for submitting papers are available on the Author Guidelines page
Full paper submission due: January 31, 2008
First round review completed: April 30, 2008
Final notification: May 30, 2008
Final manuscripts due: July 31, 2008