scholarly journals A Collaborative Decision Support System Framework for Vertical Farming Business Developments

2021 ◽  
Vol 13 (1) ◽  
pp. 34-66
Author(s):  
Francis J. Baumont De Oliveira ◽  
Scott Ferson ◽  
Ronald Dyer
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Environmental Sustainability ◽  
Financial Risk ◽  
Business Models ◽  
Partial Information ◽  
Lessons Learned ◽  
Business Decisions ◽  
Vertical Farming

The emerging industry of vertical farming (VF) faces three key challenges: standardisation, environmental sustainability, and profitability. High failure rates are costly and can stem from premature business decisions about location choice, pricing strategy, system design, and other critical issues. Improving knowledge transfer and developing adaptable economic analysis for VF is necessary for profitable business models to satisfy investors and policy makers. A review of current horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. Data from the literature alongside lessons learned from industry practitioners are centralised in the proposed DSS, using imprecise data techniques to accommodate for partial information. The DSS evaluates business sustainability using financial risk assessment. This is necessary for complex/new sectors such as VF with scarce data.

scholarly journals A Review of Information Fusion Methods for Gas Turbine Diagnostics

2019 ◽  
Vol 11 (22) ◽  
pp. 6202 ◽  
Author(s):  
Valentina Zaccaria ◽  
Moksadur Rahman ◽  
Ioanna Aslanidou ◽  
Konstantinos Kyprianidis
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Gas Turbine ◽  
Information Fusion ◽  
Support System ◽  
Probabilistic Reasoning ◽  
Decision Fusion ◽  
Lessons Learned ◽  
Diagnostic Systems ◽  
Fusion Methods

The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.

scholarly journals Tools for Risk Management of Technical Facilities Operation

2020 ◽  
Vol 5 (4) ◽  
pp. 494-500
Author(s):  
Dana Prochazkova ◽  
Jan Prochazka
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Management Plan ◽  
Life Cycles ◽  
Present Knowledge ◽  
Lessons Learned ◽  
Risk Management Plan ◽  
The Past

The article shows the results of research directed to detection of technical facilities accidents and failures sources at their operation. The research aim is to create the effective tools for management of risks so the coexistence of technical facilities with their vicinity would be ensured throughout their life cycles. The problems solution way is based on the simultaneously preferred concept, in which the safety is preferred over the reliability.  Respecting the present knowledge on technical facilities´ safety and the lessons learned from the past technical facilities accidents and  failures, the causes of which were connected with their operation, two tools are developed:  Decision Support System and Risk Management Plan that were reviewed by experts and tested in practice.

Spruce budworm decision support system: lessons learned in development and implementation

2000 ◽  
Vol 27 (1-3) ◽  
pp. 293-314 ◽  
Author(s):  
David A MacLean ◽  
Kevin B Porter ◽  
Wayne E MacKinnon ◽  
Kathy P Beaton
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Spruce Budworm ◽  
Lessons Learned

scholarly journals Individually Optimized Commercial Road Transport: A Decision Support System for Customizable Routing Problems

2019 ◽  
Vol 11 (20) ◽  
pp. 5544 ◽  
Author(s):  
Max Leyerer ◽  
Marc-Oliver Sonneberg ◽  
Maximilian Heumann ◽  
Tim Kammann ◽  
Michael H. Breitner
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Vehicle Routing ◽  
Support System ◽  
Real World ◽  
Environmental Sustainability ◽  
Route Planning ◽  
Road Transportation ◽  
Routing Problems ◽  
Routing Problem

The Vehicle Routing Problem (VRP) in its manifold variants is widely discussed in scientific literature. We investigate related optimization models and solution methods to determine the state of research for vehicle routing attributes and their combinations. Most of these approaches are idealized and focus on single problem-tailored routing applications. Addressing this research gap, we present a customizable VRP for optimized road transportation embedded into a Decision Support System (DSS). It integrates various model attributes and handles a multitude of real-world routing problems. In the context of urban logistics, practitioners of different industries and researchers are assisted in efficient route planning that allows for minimizing driving distances and reducing vehicle emissions. Based on the design science research methodology, we evaluate the DSS with computational benchmarks and real-world simulations. Results indicate that our developed DSS can compete with problem-tailored algorithms. With our solution-oriented DSS as final artifact, we contribute to an enhanced economic and environmental sustainability in urban logistic applications.

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