Bridge Management Intelligence Decision Support System

2013 ◽  
Vol 361-363 ◽  
pp. 1182-1186
Author(s):  
Juan Wang ◽  
Zhi Hong Qie ◽  
Xin Miao Wu
Keyword(s):  
Decision Support ◽  
Fault Diagnosis ◽  
Decision Support System ◽  
Knowledge Base ◽  
Decision Maker ◽  
Support System ◽  
Damage Identification ◽  
Inference Engine ◽  
Bridge Management ◽  
Model Base

In this paper a “bridge management intelligence decision support system” (BMIDSS) will be introduced. This system is based on the theory of decision support, considering the need of bridge manager and decision maker, utilizing database, model base, graphics base, knowledge base, and inference engine to realize date management, damage identification, status evaluation, fault diagnosis, remedy guidance to existing bridge.

scholarly journals Intelligent decision support method in a typical situation

2021 ◽  
Vol 11 (1) ◽  
pp. 126-136
Author(s):  
V.V. Antonov ◽  
◽  
K.A. Konev
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Base ◽  
Business Process ◽  
Support System ◽  
System Integration ◽  
General Structure ◽  
Typical Situation ◽  
Ontological Model ◽  
Decision Support Method

The article discusses a decision support method using a knowledge base. The relevance of the study of issues related to decision-making in typical situations is shown. In order to increase the effectiveness of management activities, the is-sues of system integration of the regulatory framework, ontological model and knowledge base of the intelligent sub-system of decision support within the framework of the business process are considered. In support of the proposed method, a model has been formed for replenishing the knowledge base both at the structural and analytical levels, which demonstrates the connection between the most important elements of the system: the ontological model, the knowledge base and the normative subsystem. An example of using the proposed scheme is shown. To demonstrate the model of functioning of the decision support system, an algorithm for replenishing the knowledge base is proposed and described. As a conceptual basis for the formal description of the model, operations for working with knowledge are described in the set-theoretic aspect. The principles of adaptation of the ontological model as an information object for linking with the knowledge base are considered. The conceptual diagram of the general structure of the ontological model for making decisions within the framework of the business process as a set of interrelated concepts is proposed and demonstrated. An information model of a specialized database has been developed and presented, serving as a technical basis for building a knowledge base of a decision support system in a typical situation, its main structural el-ements, the principles of their interrelation and an approach to ensuring the consistency of its internal structure are de-scribed.

scholarly journals A MODEL-DRIVEN DECISION SUPPORT SYSTEM FOR REALLOCATION OF SUPPLY TO ORDERS UNDER UNCERTAINTY IN CERAMIC COMPANIES

2015 ◽  
Vol 21 (4) ◽  
pp. 596-625 ◽  
Author(s):  
M. M. E. ALEMANY ◽  
A. A. ◽  
Andrés BOZA ◽  
Vicente S. FUERTES-MIQUEL
Keyword(s):  
Decision Support ◽  
Mathematical Programming ◽  
Decision Support System ◽  
Decision Maker ◽  
Support System ◽  
Programming Model ◽  
Decision Makers ◽  
Mathematical Programming Model ◽  
Finished Good ◽  
Model Driven

In ceramic companies, uncertainty in the tone and gage obtained in first quality units of the same finished good (FG) entails frequent discrepancies between planned homogeneous quantities and real ones. This fact can lead to a shortage situation in which certain previously committed customer orders cannot be served because there are not enough homogeneous units of a specific FG (i.e., with the same tone and gage). In this paper, a Model-Driven Decision Support System (DSS) is proposed to reassign the actual homogeneous stock and the planned homogeneous sublots to already committed orders under uncertainty by means of a mathematical programming model (SP-Model). The DSS functionalities enable ceramic decision makers to generate different solutions by changing model options. Uncertainty in the planned homogeneous quantities, and any other type of uncertainty, is managed via scenarios. The robustness of each solution is tested in planned and real situations with another DSS functionality based on another mathematical programming model (ASP-Model). With these DSS features, the ceramic decision maker can choose in a friendly fashion the orders to be served with the current homogeneous stock and the future uncertainty homogeneous supply to better achieve a balance between the maximisation of multiple objectives and robustness.

Knowledge organization and inference engine for the WVU Face Decision Support System

1988 ◽  
Vol 24 (5) ◽  
pp. 866-869
Author(s):  
R.S. Raman ◽  
N.A. Reddy ◽  
Y.V. Reddy ◽  
R.S. Nutter ◽  
S. Carrow ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Inference Engine ◽  
Knowledge Organization
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