A decision support system that links short-term silvicultural operating plans with long-term forest-level strategic plans

1993 ◽  
Vol 23 (6) ◽  
pp. 1078-1095 ◽  
Author(s):  
Robert G. Davis ◽  
David L. Martell

This paper describes a decision support system that forest managers can use to help evaluate short-term, site-specific silvicultural operating plans in terms of their potential impact on long-term, forest-level strategic objectives. The system is based upon strategic and tactical forest-level silvicultural planning models that are linked with each other and with a geographical information system. Managers can first use the strategic mathematical programming model to develop broad silvicultural strategies based on aggregate timber strata. These strategies help them to subjectively delineate specific candidate sites that might be treated during the first 10 years of a much longer planning horizon using a geographical information system and to describe potential silvicultural prescriptions for each candidate site. The tactical model identifies an annual silvicultural schedule for these candidate sites in the first 10 years, and a harvesting and regeneration schedule by 10-year periods for aggregate timber strata for the remainder of the planning horizon, that will maximize the sustainable yield of one or more timber species in the whole forest, given the candidate sites and treatments specified by the managers. The system is demonstrated on a 90 000 - ha area in northeastern Ontario.

2015 ◽  
Vol 16 (2) ◽  
pp. 542-550 ◽  
Author(s):  
M. S. Morley ◽  
D. Vitorino ◽  
K. Behzadian ◽  
R. Ugarelli ◽  
Z. Kapelan ◽  
...  

A decision support system (DSS) tool for the assessment of intervention strategies (Alternatives) in an urban water system (UWS) with an integral simulation model called ‘WaterMet2’ is presented. The DSS permits the user to identify one or more optimal Alternatives over a fixed long-term planning horizon using performance metrics mapped to the TRUST sustainability criteria. The DSS exposes lists of in-built intervention options and system performance metrics for the user to compose new Alternatives. The quantitative metrics are calculated by the WaterMet2 model, and further qualitative or user-defined metrics may be specified by the user or by external tools feeding into the DSS. A multi-criteria decision analysis approach is employed within the DSS to compare the defined Alternatives and to rank them with respect to a pre-specified weighting scheme for different Scenarios. Two rich, interactive graphical user interfaces, one desktop and one web-based, are employed to assist with guiding the end user through the stages of defining the problem, evaluating and ranking Alternatives. This mechanism provides a useful tool for decision makers to compare different strategies for the planning of UWS with respect to multiple Scenarios. The efficacy of the DSS is demonstrated on a northern European case study inspired by a real-life UWS for a mixture of quantitative and qualitative criteria. The results demonstrate how the DSS, integrated with an UWS modelling approach, can be used to assist planners in meeting their long-term, strategic-level sustainability objectives.


2021 ◽  
pp. 193229682110088
Author(s):  
Petra Augstein ◽  
Peter Heinke ◽  
Lutz Vogt ◽  
Klaus-Dieter Kohnert ◽  
Eckhard Salzsieder

Background: The increasing prevalence of type 2 diabetes mellitus (T2D) and specialist shortage has caused a healthcare gap that can be bridged by a decision support system (DSS). We investigated whether a diabetes DSS can improve long- and/or short-term glycemic control. Methods: This is a retrospective observational cohort study of the Diabetiva program, which offered a patient-tailored DSS using Karlsburger Diabetes-Management System (KADIS) once a year. Glycemic control was analyzed at baseline and after 12 months in 452 individuals with T2D. Time in range (TIR; glucose 3.9-10 mmol/L) and Q-Score, a composite metric developed for analysis of continuous glucose profiles, were short-term and HbA1c long-term measures of glycemic control. Glucose variability (GV) was also measured. Results: At baseline, one-third of patients had good short- and long-term glycemic control. Q-Score identified insufficient short-term glycemic control in 17.9% of patients with HbA1c <6.5%, mainly due to hypoglycemia. GV and hyperglycemia were responsible in patients with HbA1c >7.5% and >8%, respectively. Application of DSS at baseline improved short- and long-term glycemic control, as shown by the reduced Q-Score, GV, and HbA1c after 12 months. Multiple regression demonstrated that the total effect on GV resulted from the single effects of all influential parameters. Conclusions: DSS can improve short- and long-term glycemic control in individuals with T2D without increasing hypoglycemia. The Q-Score allows identification of individuals with insufficient glycemic control. An effective strategy for therapy optimization could be the selection of individuals with T2D most at need using the Q-Score, followed by offering patient-tailored DSS.


Author(s):  
Jean-Fabrice Lebraty ◽  
Cécile Godé

This article explores the ability of a decision support system (DSS) to improve the quality of decision making in extreme environment. This DSS is actually based on a networked information system. Academic literature commonly mentions models of fit to explore the relationship between technology and performance, reckoning users' evaluations as a relevant measurement technique for Information System (IS) success. Although effective contributions have been achieved in measurement and exploration of fit, there have been few attempts to investigate the triangulation of fit between “Task-DSS-Decision Maker” under stressful and uncertain circumstances. This article provides new insights regarding the advantages provided by networked IS for making relevant decisions. An original case study has been conducted. It is focused on a networked decision support system called Link 16 that is used during aerial missions. This case study shows that the system improves decision making on an individual basis. Our result suggest the importance of three main fit criteria – Compliance, Complementarity and Conformity – to measure DSS performance under extreme environment and display a preliminary decisional fit model.


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