scholarly journals Industrial Process Model Integration Using a Blackboard Model within a Pan Stage Decision Support System

2009 ◽  
Author(s):  
Roland Dodd ◽  
Andrew Chiou ◽  
Xinghuo Yu ◽  
Ross Broadfoot
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Process Model ◽  
Industrial Process ◽  
Model Integration ◽  
Blackboard Model

scholarly journals SOM BASED DECISION SUPPORT IN FAILURE MANAGEMENT

2014 ◽  
pp. 124-130
Author(s):  
Miki Sirola ◽  
Golan Lampi ◽  
Jukka Parviainen
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Nuclear Power ◽  
Process Model ◽  
Primary Circuit ◽  
Self Organizing Map ◽  
Computerized Decision Support ◽  
Knowledge Based ◽  
Management Scenario

Computerized decision support system field covers many methodologies and application areas. In this paper Self-Organizing Map (SOM) and knowledge-based techniques are used in combination to reason problematic situations in failure management. A process model that consists of individual connected process components has been developed. A primary circuit of a boiling water nuclear power plant including two branches has been composed. A failure management scenario is thoroughly analyzed and solved with the SOM based decision support system. The structure and reasoning of the Computerized Decision Support System (CDSS) is also shortly discussed. The process model is demonstrated together with the CDSS and shown to be useful. The tool helps operators decision making with various visualizations, and by giving concrete recommendations for possible control actions or other acts.

A Case Study of Decision Support System and Warehouse Management System Integration

2021 ◽  
pp. 111-138
Author(s):  
Alparslan Sari ◽  
Ismail Butun
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
System Integration ◽  
Management System ◽  
Process Model ◽  
Real Life ◽  
Warehouse Management ◽  
Warehouse Management System ◽  
Smart Things

A warehouse is an indispensable part of the logistics. A warehouse management system (WMS) is designed to improve efficiency in warehouses to increase their throughput and potential. The rise of IoT and its commercialization enabled ‘smart things' to be widely adopted by hobbyists and companies. Cheap sensors and smart devices triggered better automation opportunities. Many devices and sensors that are being deployed in the industry and warehousing are affected by this trend. A well-designed WMS is needed to connect devices and humans in a heterogenous warehouse environment. This chapter introduces a prototype of a WMS powered by a decision support system (DSS) based on real-life requirements. In order to have fast, reliable, and efficient decision making in warehousing, the importance of employing DSS in the WMS is emphasized. Warehouse-related IoT technology is briefly introduced, and its security considerations are discussed thoroughly. The main contribution of this chapter is to show how warehouse operations can be modeled in business process model notation and executed in a DSS.

Data Mining Approach to Decision Support in Social Welfare

2014 ◽  
Vol 5 (2) ◽  
pp. 39-61 ◽  
Author(s):  
Ricardo Anderson ◽  
Gunjan Mansingh
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Social Welfare ◽  
Knowledge Discovery ◽  
Support System ◽  
Process Model ◽  
Use Of Data ◽  
Data Mining Approach

Knowledge discovery and data-mining techniques have the potential to provide insights into data that can improve decision making. This paper explores the use of data mining to extract patterns from data in the domain of social welfare. It discusses the application of the Integrated Knowledge Discovery and Data Mining process model (IKDDM) a social welfare programme in Jamaica. Further, it demonstrates how the knowledge acquired from the data is used to develop a knowledge driven decision support system (DSS) in the PATH CCT programme. This system was successfully tested in the domain showing over 94% accuracy in the comparative decisions produced.

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