scholarly journals Integration of Case-based and Rule-based Reasoning Through Fuzzy Inference in Decision Support Systems

2017 ◽  
Vol 103 ◽  
pp. 447-453 ◽  
Author(s):  
T.V. Avdeenko ◽  
E.S. Makarova
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Fuzzy Inference ◽  
Support Systems ◽  
Rule Based ◽  
Case Based

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.

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.

Designing Valid Humanitarian Logistics Scenario Sets

2021 ◽  
pp. 452-469
Author(s):  
Jorge Vargas-Florez ◽  
Matthieu Lauras ◽  
Tina Comes
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Inventory Management ◽  
Support Systems ◽  
Humanitarian Logistics ◽  
The Real ◽  
Quantitative Models ◽  
Location Routing ◽  
Concrete Application ◽  
Case Based

Literature about humanitarian logistics (HL) has developed a lot of innovative decision support systems during the last decades to support decisions such as location, routing, supply, or inventory management. Most of those contributions are based on quantitative models but, generally, are not used by practitioners who are not confident with. This can be explained by the fact that scenarios and datasets used to design and validate those HL models are often too simple compared to the real situations. In this chapter, a scenario-based approach based on a five-step methodology has been developed to bridge this gap by designing a set of valid scenarios able to assess disaster needs in regions subject to recurrent disasters. The contribution, usable by both scholars and practitioners, demonstrates that defining such valid scenario sets is possible for recurrent disasters. Finally, the proposal is validated on a concrete application case based on Peruvian recurrent flood and earthquake disasters.

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