scholarly journals RuttOpt — a decision support system for routing of logging trucks

2008 ◽  
Vol 38 (7) ◽  
pp. 1784-1796 ◽  
Author(s):  
Gert Andersson ◽  
Patrik Flisberg ◽  
Bertil Lidén ◽  
Mikael Rönnqvist
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Case Studies ◽  
Support System ◽  
Planning Horizon ◽  
Relevant Information ◽  
Two Phase ◽  
Optimization Routine ◽  
Tabu Search Method ◽  
Large Case

We describe the decision support system RuttOpt, which is developed for scheduling logging trucks in the forest industry. The system is made up of a number of modules. One module is the Swedish road database NVDB, which consists of detailed information of all of the roads in Sweden. This also includes a tool to compute distances between locations. A second module is an optimization routine that finds a schedule, i.e., set of routes for all trucks. This is based on a two-phase algorithm where linear programming and a standard tabu search method are used. A third module is a database storing all relevant information. At the center of the system is a user interface where information and results can be viewed on maps, Gantt schedules, and result reports. The RuttOpt system has been used in a number of case studies and we describe four of these. The case studies have been made in both forest companies and hauling companies. The cases range from 10 to 110 trucks and with a planning horizon ranging between 1 and 5 days. The results show that the system can be used to solve large case studies and that the potential savings are in the range 5%–30%.

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
Keyword(s):  
Information System ◽  
Decision Support ◽  
Decision Support System ◽  
Geographical Information System ◽  
Support System ◽  
Planning Horizon ◽  
Geographical Information ◽  
Timber Species ◽  
Short Term

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.

scholarly journals HMMER Cut-off Threshold Tool (HMMERCTTER): Supervised Classification of Superfamily Protein Sequences with a reliable Cut-off Threshold

2017 ◽  
Author(s):  
Inti Anabela Pagnuco ◽  
María Victoria Revuelta ◽  
Hernán Gabriel Bondino ◽  
Marcel Brun ◽  
Arjen ten Have
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Case Studies ◽  
Support System ◽  
Protein Sequences ◽  
High Quality ◽  
Protein Superfamilies ◽  
Similarity Principle ◽  
Training Sequences

AbstractProtein superfamilies can be divided into subfamilies of proteins with different functional characteristics. Their sequences can be classified hierarchically, which is part of sequence function assignation. Typically, there are no clear subfamily hallmarks that would allow pattern-based function assignation by which this task is mostly achieved based on the similarity principle. This is hampered by the lack of a score cut-off that is both sensitive and specific.HMMER Cut-off Threshold Tool (HMMERCTTER) adds a reliable cut-off threshold to the popular HMMER. Using a high quality superfamily phylogeny, it clusters a set of training sequences such that the cluster-specific HMMER profiles show 100% precision and recall (P&R), thereby generating a specific threshold as inclusion cut-off. Profiles and threshold are then used as classifiers to screen a target dataset. Iterative inclusion of novel sequences to groups and the corresponding HMMER profiles results in high sensitivity while specificity is maintained by imposing 100% P&R. In three presented case studies of protein superfamilies, classification of large datasets with 100% P&R was achieved with over 95% coverage. Limits and caveats are presented and explained.HMMERCTTER is a promising protein superfamily sequence classifier provided high quality training datasets are used. It provides a decision support system that aids in the difficult task of sequence function assignation in the twilight zone of sequence similarity. A package containing source code and full dataset will be deposited at Github and is available for reviewers at: https://www.dropbox.com/s/aacao6ggcak30bg/Repo.tar.gz?dl=0Author summaryThe enormous amount of genome sequences made available in the last decade provide new challenges for scientists. An important step in genome sequence processing is function assignation of the encoded protein sequences, typically based on the similarity principle: The more similar sequences are, the more likely they encode the same function. However, evolution generated many protein superfamilies that consist of various subfamilies with different functional characteristics, such as substrate specificity, optimal activity conditions or the catalyzed reaction. The classification of superfamily sequences to their respective subfamilies can be performed based on similarity but since the different subfamilies also remain similar, it requires a reliable similarity score cut-off.We present a tool that clusters training sequences and describes them in profiles that identify cluster members with higher similarity scores than non-cluster members, i.e. with 100% precision and recall. This defines a score cut-off threshold. Profiles and thresholds are then used to classify other sequences. Classified sequences are included in the profiles in order to improve sensitivity while maintaining specificity by imposing 100% precision and recall. Results on three case studies show that the tool can correctly classify complex superfamilies with over 95% coverage.HMMERCTTER is meant as a decision support system for the expert biologist rather than the computational biologist.

PRO Plus: a whole-farm fodder budgeting decision support system

2000 ◽  
Vol 40 (4) ◽  
pp. 621 ◽  
Author(s):  
M. J. McPhee ◽  
A. K. Bell ◽  
P. Graham ◽  
G. R. Griffith ◽  
G. P. Meaker
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Case Studies ◽  
Support System ◽  
Growth Rates ◽  
Grazing Management ◽  
Financial Viability ◽  
Management Decisions ◽  
Sheep Meat ◽  
Integral Component

This paper describes PRO Plus, a whole-farm fodder budgeting decision support system for beef, sheep meat and wool producers. The program predicts the pasture mass available at the end of each month for individual paddocks based on pasture growth rates, number of stock, intake and the grazing plan where producers allocate mobs weekly to paddocks. Two case studies are presented that identify how the program can be used individually or in conjunction with other programs to make management decisions. PRO Plus is an integral component of the PROGRAZE Plus course and assists producers to improve the financial viability and sustainability of their farms through better pasture and grazing management.

Generic On-Board Decision Support System Framework for Marine Operations

2019 ◽  
Author(s):  
Stian Skjong ◽  
Lars T. Kyllingstad ◽  
Karl-Johan Reite ◽  
Joakim Haugen ◽  
Jarle Ladstein ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Numerical Models ◽  
Relevant Information ◽  
Operational Planning ◽  
Efficient Operation ◽  
Hybrid Propulsion ◽  
Board Decision ◽  
Offshore Crane

Abstract In this work, we present a generic framework for designing on-board decision support systems for marine operations. We discuss different technologies and methods for obtaining and analysing data and providing relevant information to on-board personnel. In particular, we focus on combining and integrating simulators with measurements, both live and stored historical data, in on-board systems for both pre-operational planning and live situation observers that might have predictive functionalities. To exemplify, we present four case studies: The first two are concerned with development of an on-board decision support system for offshore crane operations. Here, vessel motion measurements and numerical models are combined for both on-board surveillance applications and on-board pre-operational planning applications, where the latter include historical vessel characteristics. In the third, we combine simulations, measurements and automatic control in an application aimed at triple-trawl fishing operations. Finally, we present a data-driven decision support system for energy-efficient operation of hybrid propulsion systems.

scholarly journals Decision support system for the long-term city metabolism planning problem

2015 ◽  
Vol 16 (2) ◽  
pp. 542-550 ◽  
Author(s):  
M. S. Morley ◽  
D. Vitorino ◽  
K. Behzadian ◽  
R. Ugarelli ◽  
Z. Kapelan ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
User Interfaces ◽  
Performance Metrics ◽  
Real Life ◽  
Water System ◽  
Planning Horizon ◽  
Planning Problem

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.

scholarly journals A rule-based decision support system for aiding iron deficiency management

2021 ◽  
Vol 27 (4) ◽  
pp. 146045822110660
Author(s):  
Duygu Çelik Ertuğrul ◽  
Önsen Toygar ◽  
Neda Foroutan
Keyword(s):  
Decision Support ◽  
Iron Deficiency ◽  
Decision Support System ◽  
Case Studies ◽  
Support System ◽  
Recommendation System ◽  
Experimental Studies ◽  
Deficiency Anemia ◽  
Proper Function ◽  
Tree Classifier

Iron is a vital mineral for the proper function of hemoglobin which is also a protein needed to transport oxygen in the blood. The lack of iron in human blood causes a range of serious health problems including “anemia.” In this article, the COntAneRS (Clinical ONTology-based Iron Deficiency‐ANEmia‐ Recommendation System) is proposed as a clinical decision support system to diagnose iron deficiency and manage its treatment. The applied methodologies and main technical contributions of this study are discussed in four aspects: (1) Iron Deficiency Domain Ontology (IDDOnt), (2) Semantic Web Rule Knowledgebase (SWRL), (3) Inference Engine, and (4) Physician Portal of the system. Experimental studies of the proposed system have been applied on a population of 200 people, consisting of real anemia patients and healthy individuals. First, a decision tree classifier is used to diagnose iron deficiency condition based on the patients’ demographic information and certain medical data, as well as recently measured hemoglobin CBC levels of the patients. To check the effectiveness of the system, the data of 50 anonymous patients randomly selected from 200 patients are entered manually in the IDDOnt and the system is then verified according to the inferencing results. After inferencing step, the recommendations related to appropriate iron drugs, daily consumption dose, drug consumption periods, and additional medical suggestions about drug interactions are provided by the system to the responsible physician through system ontology, SWRL rules, and web services. As a result of experimental studies, our system has provided very good accuracy (99.5%) and robust results in producing patient-suitable suggestions. In addition, the applicability of the system on the cases is discussed as case studies in this paper. The results reported from the applied case studies are promising in demonstrating the applicability, effectiveness, and efficiency of the proposed approach.

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