Use of fuzzy relations in rule-based decision support systems for business planning problems

1988 ◽  
Vol 34 (3) ◽  
pp. 326-335 ◽  
Author(s):  
Harald Hruschka
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Business Planning ◽  
Fuzzy Relations ◽  
Rule Based ◽  
Planning Problems

scholarly journals Decision-Support Systems For Ambulatory Care, Including Pandemic Requirements: Using Mathematically Optimized Solutions

2021 ◽  
Author(s):  
Neele Leithäuser ◽  
Dennis Adelhütte ◽  
Kristin Braun ◽  
Christina Büsing ◽  
Martin Comis ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Ambulatory Care ◽  
Support Systems ◽  
Alternative Solution ◽  
Healthcare Sector ◽  
Decision Support Tools ◽  
Transport Services ◽  
Support Tools ◽  
Planning Problems

Abstract Background: The healthcare sector poses many strategic, tactic and operational planning questions. Due to the historically grown structures, planning is often locally confined and much optimization potential is foregone. Methods: We implemented optimized decision-support systems for ambulatory care for four different real-world case studies that cover a variety of aspects in terms of planning scope and decision support tools. All are based on interactive cartographic representations and are being developed in cooperation with domain experts. The planning problems that we present are the problem of positioning centers for vaccination against Covid-19 (strategical) and emergency doctors (strategical/tactical), the out-of-hours pharmacy planning problem (tactical), and the route planning of patient transport services (operational). For each problem, we describe the planning question, give an overview of the mathematical model and present the implemented decision support application. Results: Mathematical optimization can be used to model and solve these planning problems. However, in order to convince decision-makers of an alternative solution structure, mathematical solutions must be comprehensible and tangible. Appealing and interactive decision-support tools can be used in practice to convince public health experts of the benefits of an alternative solution. The more strategic the problem and the less sensitive the data, the easier it is to put a tool into practice. Conclusions: Exploring solutions interactively is rarely supported in existing planning tools. However, in order to bring new innovative tools into productive use, many hurdles must be overcome.

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.

scholarly journals Decision support systems in Slovak forestry planning: a review / Systémy na podporu rozhodovania v lesníckom plánovaní na Slovensku: prehľad

2015 ◽  
Vol 61 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Ján Tuček ◽  
Róbert Sedmák ◽  
Andrea Majlingová ◽  
Maroš Sedliak ◽  
Susete Marques
Keyword(s):  
Decision Support ◽  
Forest Management ◽  
Decision Support Systems ◽  
Support Systems ◽  
Computer Based ◽  
Management Techniques ◽  
Planning Problems ◽  
Support Decision Making ◽  
Support Research

Abstract Project COST Action FP 0804 - FORSYS summarizes European experiences in developing and applying decision support systems for forest management. This paper introduces FORSYS methodology for the classification of current forest management problems and for the description of existing decision support systems. The paper identifies the general forestry planning problems that need to be solved in Slovakia, lists the DSS tools available in Slovakia and evaluate their ability for addressing the identified problems. Finally, the research needs and gaps in this field were identified. A comparison of the situation regarding decision support in Slovakia and both in Europe and neighbouring countries (Austria, Hungary) is introduced in order to justify the identified needs. The paper is focused on the overview of models, methods and knowledge management techniques which are available in Slovakia now. We found out that the Slovak decision support research follows the state in Europe with a significant time delay and a lack of adequate instruments for addressing the contemporary planning problems exists. Consequently, there is a strong need for the development and application of computer-based tools to support decision-making problems in forest management.

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

Designing Clinical Decision Support Systems in Health Care

2011 ◽  
pp. 552-561
Author(s):  
Wullianallur Raghupathi
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Reasoning ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Rule Based ◽  
Viable Systems ◽  
Systemic Nature

Clinical decision support systems have historically focused on formal clinical reasoning. Most of the systems are rule-based and very few have become fully functional prototypes or commercially viable systems that can be deployed in real situations. The attempts to build large-scale systems without examining the intrinsic systemic nature of the clinical process have resulted in limited operational success and acceptance. The clinical function, another area of medical activity, has emerged rapidly offering potential for clinical decision support systems. This article discusses the systemic differences between clinical reasoning and clinical function and suggests that different design methodologies be used in the two domains. Clinical reasoning requires a holistic approach, such as an intelligent multiagent, incorporating the properties of softness, openness, complexity, flexibility, and generality of clinical decision support systems, while traditional rule-based approaches are sufficient for clinical function applications.

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