scholarly journals DECISION SUPPORT SYSTEMS BASED ON QUALITATIVE METHODS FOR PROJECT MANAGEMENT

2017 ◽  
Vol 8 (3(1225)) ◽  
pp. 82-88 ◽  
Author(s):  
Олег Николаевич ГУЦА ◽  
Дмитрий Борисович ЕЛЬЧАНИНОВ ◽  
Андрей Павлович ПОРВАН ◽  
Софья Владимировна ЯКУБОВСКАЯ
Keyword(s):  
Decision Support ◽  
Project Management ◽  
Qualitative Methods ◽  
Decision Support Systems ◽  
Support Systems

scholarly journals Application of the integrated decision support system for scheduling of development projects

2018 ◽  
Vol 251 ◽  
pp. 05033
Author(s):  
Vitaly Berezka
Keyword(s):  
Decision Support ◽  
Project Management ◽  
Decision Support Systems ◽  
Construction Industry ◽  
System Integration ◽  
Support Systems ◽  
Development Projects ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Joint Activities

Introduction: A higher level of specialization in various disciplines and technologies typical for the participants of modern investment and construction (development) projects creates the need for the advanced project management models. Decision support systems and tools for communication and organization of joint activities are the mandatory components for the efficient project management. Purpose-designed information systems allow to computerize such project management function as scheduling. However, the optimum decision is still found on the basis of personal assessment by decision makers. At the same time, as competition in the construction industry increases, the need for decision support systems to optimize investment and construction activities in the environment of multi-objective optimization becomes evident. Methods: The findings of DSS development projects in the construction industry have been used. The studies have been based on system integration approach to engineering, method of successive concession and the procedure for a search of the satisfactory values meeting STEM criteria under given weights. Results: Principles of development and functioning, architecture and organization and process aspects of DSS for scheduling under multi-objective optimization. Discussion: Integrated DSS capable for multi-objective optimization of the schedules has been proposed.

Using Decision Support Systems for Delay Analysis: The Project Management Perception Program

1998 ◽  
Vol 29 (2) ◽  
pp. 29-38 ◽  
Author(s):  
Janet K. Yates ◽  
John H. Audi
Keyword(s):  
Decision Support ◽  
Project Management ◽  
Decision Support Systems ◽  
Support Systems ◽  
Knowledge Bases ◽  
Delay Analysis ◽  
Analysis Model ◽  
Systematic Analysis ◽  
Data Collection Process ◽  
Causes Of Delays

This paper discusses the use of decision support systems and their application in the area of project management. Not only can computers be used to provide scheduling data, but they also can provide a consistent, detailed, and systematic analysis of progress; help predict delays; provide possible causes of delays; and provide suggestions on ways to prevent further delays and corrective actions. Decision support systems can provide a well-documented analysis of project delays and help with the data collection process. In order to demonstrate the usefulness of decision support systems, a program called the Project Management Perception is also discussed. This decision support system allows incorporation of the knowledge of experts in the field of project controls into the delay analysis model. Knowledge bases can be used within the program to perform evaluations that are normally performed manually. By computerizing these traditionally manual processes, the computer system can test for multiple influences that affect the project and search for trends, exceptions, comparisons, and patterns that indicate problems.

Assigning Value to Clinical Information – A Major Limiting Factor in the Implementation of Decision-Support Systems

1996 ◽  
Vol 35 (01) ◽  
pp. 1-4 ◽  
Author(s):  
F. T. de Dombal
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Clinical Information ◽  
Limiting Factor ◽  
Major Difficulty ◽  
Computer Aided ◽  
Computer Aided Decision

AbstractThis paper deals with a major difficulty and potential limiting factor in present-day decision support - that of assigning precise value to an item (or group of items) of clinical information. Historical determinist descriptive thinking has been challenged by current concepts of uncertainty and probability, but neither view is adequate. Four equations are proposed outlining factors which affect the value of clinical information, which explain some previously puzzling observations concerning decision support. It is suggested that without accommodation of these concepts, computer-aided decision support cannot progress further, but if they can be accommodated in future programs, the implications may be profound.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

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