decision support models
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2021 ◽  
Author(s):  
Dmytro Perepolkin

The present literature review aims to perform a survey of the decision support models used in waterfowl management. Special attention is dedicated to the origins and practice of adaptive management and modern applications of agent-based models focusing on explicit acknowledgment and treatment of uncertainty in these models.


2021 ◽  
Vol 11 (3) ◽  
pp. 3700-3713

The present study was carried out in the Oued Laou watershed belonging to the Moroccan Rif. It aims at the elaboration of mathematical tariffs for cubing Aleppo pine, the morphometric and increments study. The data analyzed for this purpose were collected after a preliminary stratification of the forest based on the plantation age. The study of the region’s climate shows that the western sector of the watershed is part of the temperate humid bioclimate, and the eastern sector belongs to the warm variant subhumid. The analysis of the stand-study structure shows that the distribution of the number of stems as a function of the circumference is a distribution close to normal. Cubic rates have been constructed to estimate the volume of trees. The mean annual volume increment of Aleppo pine varies between 3.10 and 4.83 m3/ha/year from one plot to another; this small fluctuation largely reflects the homogeneity of the study area. Aleppo pine has a wide ecological and spatial plasticity: colonization of all substrates, bioclimates ranging from semi-arid to temperate humid. It’s a species that exhibits uniform behavior despite topographic and edaphic variations.


Author(s):  
Uduak Umoh ◽  
Imo Eyoh ◽  
Vadivel S. Murugesan ◽  
Abdultaofeek Abayomi ◽  
Samuel Udoh

Healthcare systems need to overcome the high mortality rate associated with cardiovascular disease and improve patients’ health by using decision support models that are both quantitative and qualitative. However, existing models emphasize mathematical procedures, which are only good for analyzing quantitative decision variables and have failed to consider several relevant qualitative decision variables which cannot be simply quantified. In solving this problem, some models such as interval type-2 fuzzy logic (IT2FL) and flower pollination algorithm (FPA) have been used in isolation. IT2FL is a simplified version of T2FL, with a reduced computation complexity and additional design degrees of freedom, but it cannot naturally achieve the rules it uses in making decisions. FPA is a bio-inspired method based on the process of pollination, executed by the flowering plants, with the ability to learn, generalize and process numerous measurable data, but it is not able to describe how it reaches its decisions. The hybrid intelligent IT2FL-FPA system can conquer the constraints of individual approaches and strengthens their robustness to cope with healthcare data. This work describes a hybrid intelligent telemedical monitoring and predictive system using IT2FL and FPA. The main objective of this paper is to find the best membership functions (MFs) parameters of the IT2FL for an optimal solution. The FPA technique was employed to find the optimal parameters of the MFs used for IT2FLSs. The authors tested two data sets for the monitoring and prediction problems, namely: cardiovascular disease patients’ clinical and real-time datasets for shock-level monitoring and prediction.


2021 ◽  
pp. 1-24
Author(s):  
C. Lisa Mahon ◽  
Shawna Pelech

Landscapes in Canada are undergoing change due to resource and land use stressors and climate stressors. Understanding the cumulative effects of these stressors is challenging because of the complexity of ecosystems, the variability of stressors, and species response to individual or multiple stressors. A key challenge within the field of cumulative effects assessment (CEA) is guidance that describes and evaluates analytical methods. In this review we discuss four broad categories of methods with current or potential use for project-based and effects-based CEA for species in terrestrial systems: (i) qualitative review, (ii) habitat supply models, (iii) empirical species–stressor models, and (iv) decision support models. We describe each method and provide examples, highlight advantages and limitations, identify how methods address key science-based CEA questions, and provide direction on when and why to use specific CEA methods. Empirical species–stressor models and decision support models are the only analytical approaches that provide answers to many science-based CEA questions including how multiple stressors combine to affect an individual species and the certainty of multiple stressor effects. We provide recommendations for using one or more methods as complementary building blocks to fill data gaps, improve understanding and communication, engage diverse partner groups, and increase the quality and credibility of the CEA. Our review supports a move toward regional scale, effects-based CEA where partner collaboration to design, implement, and analyze comprehensive assessments of multiple stressors will (i) expand our knowledge of terrestrial species response to stressors and (ii) inform best management practices for resource industries and conservation and management actions for land managers.


2021 ◽  
Author(s):  
Karl Johnson ◽  
Caitlin Biddell ◽  
Kristen Hassmiller Lich ◽  
Julie Swann ◽  
Paul Delamater ◽  
...  

AbstractBackgroundThe COVID-19 Pandemic has popularized computer-based decision-support models as a tool for decision-makers to manage their organizations. It is unclear how decision-makers have considered these models to inform COVID-19-related decisions.MethodsWe interviewed decision-makers from North Carolina across diverse organizational backgrounds to assess major decision-making processes during COVID-19, including the use of modeling as an input to inform decision-making.ResultsInterviewees were aware of models during COVID-19, with some depending upon multiple models. Models were used to compare trends in disease spread across localities, allocate scarce resources, and track disease spread within small geographic areas. Decision-makers desired models to project disease spread within subpopulations and estimate where local outbreaks could occur as well as estimate the outcomes of social distancing policies, including consequences beyond typical health-related outcomes. Challenges to the use of modeling included doubts that models could reflect nuances of human behavior, concerns about the quality of data used in models, and the limited amount of modeling at the local level.ConclusionsThroughout COVID-19, decision-makers perceived modeling as valuable for understanding disease spread within their communities and to inform organization decisions, yet there were variations in organizations’ ability and willingness to use models for these purposes.


2021 ◽  
Vol 11 (4) ◽  
pp. 1504
Author(s):  
Fatima Abderrabi ◽  
Matthieu Godichaud ◽  
Alice Yalaoui ◽  
Farouk Yalaoui ◽  
Lionel Amodeo ◽  
...  

This paper aims to study a real case of an optimization problem derived from a hospital supply chain. The present work focuses on developing operational decision support models and algorithms for production process scheduling in hospital catering. The addressed production system is considered as a flexible job shop system. The objective is to minimize the total flow time. A novel mathematical model and two metaheuristics for the production scheduling of multi-product and multi-stage food processes are developed. These methods have proven their effectiveness for the scheduling of operations of the food production processes and allowed significant improvements in the performance of the studied production system.


2021 ◽  
Vol 51 (2) ◽  
pp. 236-256 ◽  
Author(s):  
Peter F. Newton

The evolving shift in forest management objectives towards the collective consideration of volumetric yield, end-product quality and value, and ecosystem service outcomes, while accounting for the impacts of anthropogenic climate change, has resulted in innovative advancements in decision-support models used in stand density management. This review provides a synopsis of these efforts with respect to static, dynamic, and structural stand density management diagrams (SDMDs). More precisely, the scope of this review includes an ecology-based perspective of stand density management, summarization of the foundational quantitative relationships along with their utilization within the analytical structure of the SDMD, examination of SDMD compliance with underlying ecological constructs and empirical prediction expectations, exemplification of a climate-sensitive structural SDMD variant in boreal crop planning, and identification of outstanding analytical challenges and plausible future research directions for advancing the SDMD modelling approach and its utility in stand-level management planning. Collectively, this account of the conceptual basis, historical analytical evolution, ecological integrity, predictive ability, application diversity, and demonstrated utility of the various SDMD variants solidifies the prerequisite evidentiary foundation for the continued development and deployment of SDMD-based crop planning decision-support models.


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