Overview of Decision Support Systems Applied to Construction

2016 ◽  
pp. 77-94
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Markov Decision Processes ◽  
Support Systems ◽  
Decision Processes ◽  
Rule Based ◽  
Markov Decision ◽  
Knowledge Intensive ◽  
The Cost ◽  
Rule Based Systems

The domain of construction is a very knowledge-intensive domain with so many factors involved. This implies undertaking any action requires an understanding of the different factors and how best to combine them to achieve a favourable and optimal outcome. Thus decision-making has been extensively used in the domain of construction. The aim of this chapter is to undertake a review of various decision support systems and to provide insights into their applications in the domain of construction. Specifically, the principle of cost index, sub-work chaining diagram method, linear regression and cost over-runs in time-overrun context (CCOTOV) model and Markov decision processes (MDP), ontology and rule-based systems have been reviewed. Based on the review the Markov decision processes (MDP), ontology and rule-based systems were chosen as the more suitable for the cost control case considered in this study.

Selected Mathematical Theories Underpinning Decision Models

2016 ◽  
pp. 95-121
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Support ◽  
Decision Support Systems ◽  
Markov Decision Processes ◽  
Support Systems ◽  
Decision Processes ◽  
Decision Models ◽  
Rule Based ◽  
Markov Decision

In Chapter 4, various decision support systems have been examined. The rational for Chapter 4 was to appraise the diiferent decision-support systems that have been used in construction without necessarily detailing the complexities and mathematical underpinnings. This chapter will provide the theory that underpins some selected decision support systems. These are regression models (RLM), artificial neural networks (ANN), Matrices, Markov decision processes (MDP) and the ontology rule-based decision support systems.

Rule-Based Ontology Decision Model

2016 ◽  
pp. 271-291
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Process ◽  
Support Systems ◽  
Rule Based ◽  
The Matrix ◽  
Markov Decision ◽  
Minimization Technique ◽  
Complex Decision

In chapter 7, we examined some selected case study applications of some decision support systems. Those considered were the matrix-based used in determining labour cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique and Markov decision process. As earlier discussed, our focus will be on rule-based decision support systems. This is because rule-based systems are more encompassing and can easily be employed to deal with complex decision about construction activities. Hence in this chapter, an overview of rule-based decision system will be examined.

Some Selected Case Study Applications

2016 ◽  
pp. 180-270
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Process ◽  
Support Systems ◽  
Labor Cost ◽  
The Matrix ◽  
Markov Decision ◽  
Decision Support Models ◽  
Minimization Technique

Having examined the modelling principles of underpinning based decision support systems applied to construction in Chapter 6, this chapter will now demonstrate their detail applications in construction practice. Specifically, 7 decision-support systems will be examined. The choices are based on the fact that data for use in the decision support models are available. The decision-support systems considered are the matrix-based used in determining labor cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique, Markov decision process and rule-based systems.

Engineering Mobile Group Decision Support

2011 ◽  
pp. 86-102 ◽  
Author(s):  
Reinhard Kronsteiner
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Group Decision ◽  
Support Systems ◽  
Decision Processes ◽  
Group Decision Support ◽  
Problem Space ◽  
Support Tools ◽  
Group Compositions ◽  
Collaborative Decision

This chapter investigates the potential of mobile multimedia for group decisions. Decision support systems can be categorized 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 mobile environments in terms of time, spatial distribution, and interaction will result in a set of requirements that need to be addressed in different phases of decision process. Mobility analysis of group decision processes leads to the development of appropriate mobile group decision support tools. In this chapter, we explore the different requirements for designing and implementing a collaborative decision support systems.

scholarly journals Cost and economic benefit of clinical decision support systems for cardiovascular disease prevention: a community guide systematic review

2017 ◽  
Vol 24 (3) ◽  
pp. 669-676 ◽  
Author(s):  
Verughese Jacob ◽  
Anilkrishna B Thota ◽  
Sajal K Chattopadhyay ◽  
Gibril J Njie ◽  
Krista K Proia ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Health Care ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Costs And Benefits ◽  
The Cost

Objective: This review evaluates costs and benefits associated with acquiring, implementing, and operating clinical decision support systems (CDSSs) to prevent cardiovascular disease (CVD). Materials and Methods: Methods developed for the Community Guide were used to review CDSS literature covering the period from January 1976 to October 2015. Twenty-one studies were identified for inclusion. Results: It was difficult to draw a meaningful estimate for the cost of acquiring and operating CDSSs to prevent CVD from the available studies (n = 12) due to considerable heterogeneity. Several studies (n = 11) indicated that health care costs were averted by using CDSSs but many were partial assessments that did not consider all components of health care. Four cost-benefit studies reached conflicting conclusions about the net benefit of CDSSs based on incomplete assessments of costs and benefits. Three cost-utility studies indicated inconsistent conclusions regarding cost-effectiveness based on a conservative $50,000 threshold. Discussion: Intervention costs were not negligible, but specific estimates were not derived because of the heterogeneity of implementation and reporting metrics. Expected economic benefits from averted health care cost could not be determined with confidence because many studies did not fully account for all components of health care. Conclusion: We were unable to conclude whether CDSSs for CVD prevention is either cost-beneficial or cost-effective. Several evidence gaps are identified, most prominently a lack of information about major drivers of cost and benefit, a lack of standard metrics for the cost of CDSSs, and not allowing for useful life of a CDSS that generally extends beyond one accounting period.

RuleRS: a rule-based architecture for decision support systems

2018 ◽  
Vol 26 (4) ◽  
pp. 315-344 ◽  
Author(s):  
Mohammad Badiul Islam ◽  
Guido Governatori
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Rule Based

scholarly journals Acceleration of association‐rule based markov decision processes

2009 ◽  
Vol 7 (03) ◽  
Author(s):  
Ma. de G. Garcí­a-Hernández ◽  
J. Ruiz-Pinales ◽  
A. Reyes-Ballesteros ◽  
E. Onaindí­a ◽  
J. Gabriel Aviña-Cervantes ◽  
...  
Keyword(s):  
Markov Decision Processes ◽  
Association Rule ◽  
Decision Processes ◽  
Rule Based ◽  
New Approach ◽  
Stochastic Shortest Path ◽  
Finite State ◽  
Markov Decision ◽  
Stochastic Shortest Path Problem ◽  
New Criterion

In this paper, we present a new approach for the estimation of Markov decision processes based on efficient association rule mining techniques such as Apriori. For the fastest solution of the resulting association‐rule based Markov decision process, several accelerating procedures such as asynchronous updates and prioritization using a static ordering have been applied. A new criterion for state reordering in decreasing order of maximum reward is also compared with a modified topological reordering algorithm. Experimental results obtained on a finite state and action‐space stochastic shortest path problem demonstrate the feasibility of the new approach.

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