Model-based Decision Support System Research Articles

Implementing CRM models for improved oil recovery in large oil fields

This paper describes a model-based decision support system (DSS) for reservoir engineers who may wish to use simpler analytical methods to optimize waterfloods in large waterflood operations, as is common in many oil and gas companies. The engineers select a set of existing injector and producer wells, and a set of past time periods over which the model will be fitted. The model provides outputs which support waterflooding decisions, using only oil, water and gas production and bottom hole pressure at producing wells, and water injection at each injection well in each time period. The model relating injection and production is a set of discrete-time difference equations, from the family of capacitance-resistance models. We focus on the model fitting process, which uses the least squares approach, and leads to a linearly constrained nonlinear optimization problem with non-quadratic objective. The DSS is coded in the GAMS modeling language. A numerical example is provided, and some experience with the system is described. An Excel file with the data used is available as Supplementary Online material accompanying the article on the Springer website.

Model based decision support system of operating settings for MMAT nozzles

Abstract Droplet size, which is affected by nozzle type, nozzle setups and operation, and spray solution, is one of the most critical factors influencing spray performance, environment pollution, food safety, and must be considered as part of any application scenario. Characterizing spray nozzles can be a timely and expensive proposition if the entire operational space (all combinations of spray pressure and orifice size, what influence flow rate) is to be evaluated. This research proposes a structured, experimental design that allows for the development of computational models for droplet size based on any combination of a nozzle’s potential operational settings. The developed droplet size determination model can be used as Decision Support System (DSS) for precise selection of sprayer working parameters to adapt to local field scenarios. Five nozzle types (designs) were evaluated across their complete range of orifice size (flow rate*) and spray pressures using a response surface experimental design. Several of the models showed high level fits of the modeled to the measured data while several did not as a result of the lack of significant effect from either orifice size (flow rate*) or spray pressure. The computational models were integrated into a spreadsheet based user interface for ease of use. The proposed experimental design provides for efficient nozzle evaluations and development of computational models that allow for the determination of droplet size spectrum and spraying classification for any combination of a given nozzle’s operating settings. The proposed DSS will allow for the ready assessment and modification of a sprayers performance based on the operational settings, to ensure the application is made following recommendations in plant protection products (PPP) labels.

Efficient Design of Distribution Logistics by Using a Model-Based Decision Support System

Efficient Design of Distribution Logistics by Using a Model-Based Decision Support System

Beyond modelling: considering user-centred and post-development aspects to ensure the success of a decision support system

RIM, or ‘Ryegrass Integrated Management’, is a model-based decision support system (DSS) for weed management in broadacre cropping systems that was updated to continue aid the delivery of key recommendations to manage herbicide resistance. This article complements earlier publications by documenting the rationales that underpinned the re-development efforts. The objectives are to inform the next development cycle of RIM and its delivery, as well as its adaptation to other situations. Specifically, the article aims at providing developers and project managers with key aspects to be considered before and after (re-)developing this type of model-based agricultural DSS. Reviewers report a lack of similar efforts, with modelling aspects generally better documented than underpinning rationales, including those related to implementation. Yet, this type of initiative is necessary considering that agricultural DSS can become expensive projects, and that uptake by target audiences is typically low in spite of known pitfalls and limitations. The key elements that contributed to the thought process behind upgrade choices are thus provided, as well as practical consequences for modelling. Clearly re-asserting cost-effectiveness objectives and favouring human aspects led to: retaining the ‘what-if’ learning strategy rather than developing optimisation features; renouncing added modelling intricacies; enhancing the software accessibility; and anticipating future maintenance and distribution requirements. Strategies to maximise the impact of RIM are also discussed, particularly the need for qualified workshop facilitators, as well as transparency and evaluation to build user confidence.

Integrating expert knowledge and multilingual web crawling data in a lead qualification system

Qualifying prospects as leads to contact is a complex exercise. Sales representatives often do not have the time or resources to rationally select the best leads to call. As a result, they rely on gut feeling and arbitrary rules to qualify leads. Model-based decision support systems make this process less subjective. Standard input for such an automated lead qualification system is commercial data. Commercial data, however, tends to be expensive and of ambiguous quality due to missing information. This study proposes web crawling data in combination with expert knowledge as an alternative. Web crawling data is freely available and of higher quality as it is generated by companies themselves. Potential customers use websites as a main information source, so companies benefit from correct and complete websites. Expert knowledge, on the other hand, augments web crawling data by inserting specific information. Web data consists of text that is converted to numbers using text mining techniques that make an abstraction of the text. A field experiment was conducted to test how a decision support system based on web crawling data and expert knowledge compares to a basic decision support system within an international energy retailer. Results verify the added value of the proposed approach.

WIND-STORM: A Decision Support System for the Strategic Management of Windthrow Crises by the Forest Community

Storms are one of the most damaging agents for European forests and can cause huge and long-term economic impacts on the forest sector. Recent events and research haves contributed to a better understanding and management of destructive storms, but public authorities still lack appropriate decision-support tools for evaluating their strategic decisions in the aftermath of a storm. This paper presents a decision support system (DSS) that compares changes in the dynamics of the regional forest-based sector after storm events under various crisis management options. First, the development and implementation of a regional forest model is addressed; then, the potential application of the model-based DSS WIND-STORM is illustrated. The results of simulated scenarios reveal that this DSS type is useful for designing a cost-effective regional strategy for storm-damage management in the context of scarce public resources and that public strategies must encompass the whole forest-based sector to be efficient. Additional benefits of such a DSS is to bring together decision-makers and forest stakeholders for a common objective and therefore to enhance participatory approaches to crisis management.

A model-based decision support system for solving vehicle routing and driver scheduling problems under hours of service regulations

In principle, hours-of-service (HOS) regulations are intended to help ensure truck drivers get adequate rest and perform safe operations. The new HOS regulations, however, may lead to substantial cost increases for regional common carriers which have already been hit hard by rising fuel prices and declining shipping demands. In addition, the new HOS regulations complicate driver schedules by not only restricting the driver's consecutive driving hours, but also expanding off-duty hours. To deal with this complex challenge, we propose a model-based decision support system (DSS) that helps determine the truck driver's working hours, rest periods, and his/her assigned truck's schedules and routes under HOS regulations. As a core of this model-based DSS, we developed a mixed-integer programming model and a simulated annealing metaheuristic for solving it. This model was also integrated with a geographical information system and relational database management system to enhance interfaces between the model and its parametric data using spatial, graphical displays.

Upgrading the RIM Model for Improved Support of Integrated Weed Management Extension Efforts in Cropping Systems

RIM, or “Ryegrass Integrated Management,” is a user-friendly weed management software that integrates long-term economics. As a model-based decision support system, RIM enables users to easily build 10-year cropping scenarios and evaluate the impacts of management choices on annual rigid ryegrass populations and long-term profitability. Best used in a workshop format to enable learning through interactions, RIM can provide insights for the sustainable management of ryegrass through “what-if” scenarios in regions facing herbicide resistance issues. The upgrade of RIM is presented, with changes justified from an end-user perspective. The implementation of the model in a new, intuitive software format is presented, as well as the revision, update, and documentation of over 40 management options. Enterprises, establishment systems, and control options were redefined to represent current practices, with the notable inclusion of customizable herbicide options and techniques for weed seed control at harvest. Several examples of how RIM can be used with farmers to demonstrate the benefits of adopting recommended practices for managing or delaying the onset of herbicide resistance are presented. Originally designed for the dryland broadacre systems of the Australian southern grainbelt, RIM's underlying modeling was restructured to facilitate future updates and adaptation to other weed species and cropping regions.

Water management decision makers' evaluations of uncertainty in a decision support system: the case of WaterSim in the Decision Theater

Model-based decision support systems are increasingly used to link knowledge to action for environmental decision making. How stakeholders perceive uncertainty in models and visualisations affects their perceptions of credibility, relevance and usability of these tools. This paper presents a case study of water decision makers’ evaluations of WaterSim, a dynamic water simulation model presented in an immersive decision theatre environment. Results reveal that decision makers’ understandings of uncertainty in their evaluations of decision support systems reflect both scientific and political discourse. We conclude with recommendations for design and evaluation of decision support systems that incorporate decision makers' views.

Monitoring of intratidal lung mechanics: a Graphical User Interface for a model-based decision support system for PEEP-titration in mechanical ventilation

In mechanical ventilation, a careful setting of the ventilation parameters in accordance with the current individual state of the lung is crucial to minimize ventilator induced lung injury. Positive end-expiratory pressure (PEEP) has to be set to prevent collapse of the alveoli, however at the same time overdistension should be avoided. Classic approaches of analyzing static respiratory system mechanics fail in particular if lung injury already prevails. A new approach of analyzing dynamic respiratory system mechanics to set PEEP uses the intratidal, volume-dependent compliance which is believed to stay relatively constant during one breath only if neither atelectasis nor overdistension occurs. To test the success of this dynamic approach systematically at bedside or in an animal study, automation of the computing steps is necessary. A decision support system for optimizing PEEP in form of a Graphical User Interface (GUI) was targeted. Respiratory system mechanics were analyzed using the gliding SLICE method. The resulting shapes of the intratidal compliance-volume curve were classified into one of six categories, each associated with a PEEP-suggestion. The GUI should include a graphical representation of the results as well as a quality check to judge the reliability of the suggestion. The implementation of a user-friendly GUI was successfully realized. The agreement between modelled and measured pressure data [expressed as root-mean-square (RMS)] tested during the implementation phase with real respiratory data from two patient studies was below 0.2mbar for data taken in volume controlled mode and below 0.4mbar for data taken in pressure controlled mode except for two cases with RMS<0.6mbar. Visual inspections showed, that good and medium quality data could be reliably identified. The new GUI allows visualization of intratidal compliance-volume curves on a breath-by-breath basis. The automatic categorisation of curve shape into one of six shape-categories provides the rational decision-making model for PEEP-titration.

Model based decision support system for the heap leaching process

As one of the copper recovery processes, hydrometallurgy has gain impact due to its ability to process ore with average low grades at competitive prices compared with other metallurgical processes. Among hydrometallurgical processes the leaching stage, a process that is characterized by its significant temporal and spatial scale of operation, is critical. In spite of extensive developments in instrumentation in pyrometullurgical and concentrators plants, developments in heap leaching instrumentation has not reached the required level for a fully automated control system and furthermore for a stable operation. Computational tools, such us dynamic simulators, can help operators achieve the best performance of the heap using the available instrumentation. This paper presents the development and implementation of an integrated dynamic simulator and a decision support system (DSS) for hydrometallurgical processes with emphasis in heap leaching process. The dynamic simulator uses two dimensional models of fluid transport, transport of solutes and dissolution of copper in the leaching heap, to analyse the effects produced in the variables of interest, such as the consumption of acid, copper concentration in the PLS (Pregnant Leach Solution) and leaching times. The DSS, which is connected to the real time plant information system, gathers the information by sensors and laboratory analyses to perform an automatic on-line parameter estimation of the process and makes predictions to recommend to the operator both curing and leaching rates. Results show that the DSS achieves the identification and prediction with less than 6% of error, allowing the metallurgist to predict the leaching behaviour and take decisions with better information.

Integrating regional climatology, ecology, and agronomy for impact analysis and climate change adaptation of German agriculture: An introduction to the LandCaRe2020 project

One of the most decisive natural framework conditions of agriculture – the regional climate – is in transition. This requires considering climate change and climate change impact for decision making. Although this knowledge is uncertain and depends on future green-house gas emission, it is rapidly expanding and improving. Therefore, it is important to create flexible systems with adaptable data bases and analytical tools to integrate knowledge of future climate change in agronomy for impact studies and adaptation planning. The joint project LandCaRe (Land, Climate, and Resources) 2020 aimed at developing a conceptual framework and prototype of a model-based decision support system (DSS) based on improved process knowledge and stakeholder communication. The final product, the LandCaRe DSS should combine grid-based information on regional climate and land surface, robust climate impact models and socio-economic boundary conditions to develop spatially explicit climate impact scenarios. Emphasis was put on the integration of different knowledge from science and practice. Climate projections had to be adapted to the needs of impact modelling of agricultural and ecological processes at regional and farm scale. Impact modelling included new process knowledge, especially related to the CO2 fertilisation effect on crop rotations. A new Free Air Carbon Dioxide Enrichment Experiment (FACE) on the C4 plant maize was conducted during the project. A new agro-ecosystem model was developed which integrates soil–plant–atmosphere exchange and plant production to predict crop yield as well as water, carbon and nitrogen fluxes. Stakeholders included representatives of agricultural and environmental administrations, managers of agro-enterprises and large farms as well as organisations for regional planning and nature conservation. Their central common interests were future land use, water availability and management. Stakeholders from agriculture requested not only to assess potential impacts of regional climate change on yield but also to interpret climate impact on farm economy. This required the design of a farm-economy module linking effects of management and adaptation on crop yield with scenarios of costs and prices. Here an introduction to the project, components of the DSS and its further perspectives are presented.

Modeling and development of a decision support system for supplier selection in the process industry

This paper presents the development of a model based decision support system with a case study on solving the supplier selection problem in a chemical processing industry. For the evaluation and selection of supplier, the analytical hierarchy process (AHP) and grey relational analysis (GRA) were used. The intention of the study is to propose an appropriate platform for process industries in selecting suppliers, which was tested with an electroplating industry during the course of development. The sensitivity analysis was performed in order to improve the robustness of the results with regard to the relative importance of the evaluation criteria and the parameters of the evaluation process. Finally, a practical implementation study was carried out to reveal the procedure of the proposed system and identify the suitable supplier with detailed discussions about the benefits and limitations.

Decision Support System for emergency scheduling of raw water supply systems with multiple sources

A hydraulic model-based emergency scheduling Decision Support System (DSS) is designed to eliminate the impact of sudden contamination incidents occurring upstream in raw water supply systems with multiple sources. The DSS consists of four functional modules, including water quality prediction, system safety assessment, emergency strategy inference and scheduling optimization. The work flow of the DSS is as follows. First, the water quality variations on specific cross-sections are calculated given the pollution information. Next, a comprehensive evaluation on the safety of the current system is conducted using the outputs in the first module. This will assist in the assessment of whether the system is in danger of failure, taking both the impact of pollution and system capacity into account. If there is a severe impact of contamination on the reliability of the system, a fuzzy logic based inference module is employed to generate reasonable strategies including technical measures. Otherwise, a Genetic Algorithm (GA)-based optimization model will be used to find the least-cost scheduling plan. The proposed DSS has been applied to a coastal city in South China during a saline tide period as validation. Through scenario analysis, it is demonstrated that this DSS tool is instrumental in emergency scheduling for the water company to quickly and effectively respond to sudden contamination incidents.

Application of stochastic optimization algorithm for waste load allocation in the Nakdong River basin, Korea

Model based decision support systems are increasingly used for the purpose of water resources management. In the aspect of water quality management, deterministic water quality simulation will involve various uncertainties that may yield unrealistic results. To overcome such limitations, this study proposed an optimization algorithm incorporating the stochastic approach focusing on efficient waste load allocation. Based on Monte Carlo Simulation (MCS) uncertainty analysis method, the performance of the chance constrained linear optimization programming was validated to describe the nonlinear behavior in the river system. Applicability of the proposed optimization scheme has been tested and evaluated for the Nakdong River basin in Korea where multiple pollutant sources and a large amount of water intake facilities coexist. The proposed optimization algorithm is useful to estimate the amount of waste loads that should be removed to meet the target regulatory water quality standard considering uncertainties involved in the deterministic water quality simulation.

A model-based decision support system for critiquing mechanical ventilation treatments

A computerized system for critiquing mechanical ventilation treatments is presented that can be used as an aide to the intensivist. The presented system is based on the physiological model of the subject's respiratory system. It uses modified versions of previously developed models of adult and neonatal respiratory systems to simulate the effects of different ventilator treatments on the patient's blood gases. The physiological models that have been used for research and teaching purposes by many researchers in the field include lungs, body tissue, and the brain tissue. The lung volume is continuously time-varying and the effects of shunt in the lung, changes in cardiac output and cerebral blood flow, and the arterial transport delays are included in the system. Evaluation tests were done on adult and neonate patients with different diagnoses. In both groups combined, the differences between the arterial partial pressures of CO(2) predicted by the system and the experimental values were 1.86 ± 1.6 mmHg (mean ± SD), and the differences between the predicted arterial hemoglobin oxygen saturation values, S(aO2), and the experimental values measured by using pulse oximetry, S(pO2), were 0.032 ± 0.02 (mean ± SD). The proposed system has the potential to be used alone or in combination with other decision support systems to set ventilation parameters and optimize treatment for patients on mechanical ventilation.

The Intelligent Ventilator (INVENT) project: The role of mathematical models in translating physiological knowledge into clinical practice

This dissertation has addressed the broad hypothesis as to whether building mathematical models is useful as a tool for translating physiological knowledge into clinical practice. In doing so it describes work on the INtelligent VENTilator project (INVENT), the goal of which is to build, evaluate and integrate into clinical practice, a model-based decision support system for control of mechanical ventilation. The dissertation describes the mathematical models included in INVENT, i.e. a model of pulmonary gas exchange focusing on oxygen transport, and a model of the acid-base status of blood, interstitial fluid and tissues. These models have been validated, and applied in two other systems: ALPE, a system for measuring pulmonary gas exchange and ARTY, a system for arterialisation of the acid-base and oxygen status of peripheral venous blood. The major contributions of this work are as follows. A mathematical model has been developed which can describe pulmonary gas exchange more accurately that current clinical techniques. This model is parsimonious in that it can describe pulmonary gas exchange from measurements easily available in the clinic, along with a readily automatable variation in F(I)O(2). This technique and model have been developed into a research and commercial tool (ALPE), and evaluated both in the clinical setting and when compared to the reference multiple inert gas elimination technique (MIGET). Mathematical models have been developed of the acid- base chemistry of blood, interstitial fluid and tissues, with these models formulated using a mass-action mass-balance approach. The model of blood has been validated against literature data describing the addition and removal of CO(2), strong acid or base, and haemoglobin; and the effects of oxygenation or deoxygenation. The model has also been validated in new studies, and shown to simulate accurately and precisely the mixing of blood samples at different PCO(2) and PO(2) levels. This model of acid-base chemistry of blood has been applied in the ARTY system. ARTY has been shown to accurately and precisely calculate arterial values of acid-base and oxygen status in patients residing in the ICU, and in those with chronic lung disease. The INtelligent VENTilator (INVENT) system has been developed for optimization of mechanical ventilator settings using physiological models and utility/penalty functions, separating physiological knowledge from clinical preference. The models can be tuned to the individual patient via parameter estimation, providing patient specific advice. The INVENT team has shown prospectively that the system provides advice on F(I)O(2) which is as good as clinical practice, and retrospectively that the system provides reasonable suggestions of tidal volume, respiratory frequency and F(I)O(2). In general, this dissertation has illustrated a further example of the role of modeling in describing and understanding complex systems. The dissertation has shown that when dealing with complexity the goal of the model must be in focus if a correct balance is to be maintained between system complexity and model parameterization. The original goal of the INVENT team, i.e. to build, evaluate and integrate a DSS for control of mechanical ventilation has not as yet been completed. However, the broader hypothesis that building models generates new and interesting questions has been successfully demonstrated. The ALPE model and system has been applied in intensive care, post operative care and cardiology and is currently being evaluated in new clinical domains. ARTY has been shown to have potential benefit in eliminating the need for painful arterial punctures, and may also be useful as a screening tool. These systems illustrate the benefits of investing in models as a mechanism for translating physiological knowledge to clinical practice.

Retrospective evaluation of a decision support system for controlled mechanical ventilation

Management of mechanical ventilation in intensive care patients is complicated by conflicting clinical goals. Decision support systems (DSS) may support clinicians in finding the correct balance. The objective of this study was to evaluate a computerized model-based DSS for its advice on inspired oxygen fraction, tidal volume and respiratory frequency. The DSS was retrospectively evaluated in 16 intensive care patient cases, with physiological models fitted to the retrospective data and then used to simulate patient response to changes in therapy. Sensitivity of the DSS's advice to variations in cardiac output (CO) was evaluated. Compared to the baseline ventilator settings set as part of routine clinical care, the system suggested lower tidal volumes and inspired oxygen fraction, but higher frequency, with all suggestions and the model simulated outcome comparing well with the respiratory goals of the Acute Respiratory Distress Syndrome Network from 2000. Changes in advice with CO variation of about 20% were negligible except in cases of high oxygen consumption. Results suggest that the DSS provides clinically relevant and rational advice on therapy in agreement with current 'best practice', and that the advice is robust to variation in CO.

장외파생상품거래를 위한 유연한 의사결정지원시스템 아키텍처 설계에 관한 연구: A은행 사례

Model-based decision support system (DSS) has acted as a crucial role in strengthening the business competitiveness by providing a way of modeling and solving real-world decision problems in a quantitative and scientific manner. It is even more important for trading OTC derivatives, which requires extensive financial-engineering expertise while actively reacting to the continuously changing financial market. This paper proposes a flexible model-based DSS architecture that can support user-friendly interface for executing and analyzing the models and can adapt to the changes of financial market seamlessly. For user-friendliness, we implement the user-interfaces (UIs) using Microsoft Excel, a very widely used spreadsheet program for its great generality and extensibility. Users can utilize the analysis results of DSS or reprocess them for their special needs through the UIs in the form of familiar spreadsheets easily. For adaptiveness to the markets, the proposed architecture is constructed based on the object-oriented concepts, which enables such changes as release of a new financial product can be updated into the system without any delay at the lowest cost. We investigate the practical benefits and limitations of the proposed architecture by a case study on the construction of Model-based Trading Support System (MTSS), performed by a commercial bank in Korea.

Model-based decision support system for water quality management under hybrid uncertainty

Water quality management is inevitably complicated since it involves a number of environmental, socio-economic, technical, and political factors with dynamic and interactive features. In planning water quality management systems, uncertainties exist in many system components and may affect the system behaviours. It is thus desired that such complexities and uncertainties be effectively addressed for providing decision support for practical water quality management. The objective of this study is to develop a model-based decision support system for supporting water quality management under hybrid uncertainties, named FICMDSS, which is based on a hybrid uncertain programming (HFICP) model with fuzzy and interval coefficients. The system provides an effective tool for the decision makers in dealing with water quality management problems and formulating desired policies and strategies. The user can easily operate the system and obtain the decision support through user-friendly graphical interfaces. The HFICP model improves upon the existing inexact programming methods through incorporation of hybrid fuzzy and interval uncertainties into the optimization management processes and resulting solutions. Results of a water quality management case study indicated that the developed FICMDSS can facilitate the decision making in planning agricultural activities for water quality management in agricultural systems. Feasible decision alternatives for cropping area, amounts of manure and fertilizer application, and sizes of livestock husbandry can be generated for achieving the maximum agricultural system benefit subject to the given water-related constraints. The user can better make the decisions for water quality management under hybrid uncertainties with the help of FICMDSS.