Web-Based Decision Support System

A SDSS combines database storage technologies, geographic information systems (GIS) and decision modeling into tools which can be used to address a wide variety of decision support areas (Eklund, Kirkby, and Pollitt, 1996). Recently, various emerging technologies in computer hardware and software such as speedy microprocessors, gigabit network connections, fast internet mapping servers along with Web-based technologies like extensible markup language (XML), Web services, etc provide promising opportunities to take the traditional spatial decision support systems one step further to provide easy-to-use, round-the-clock access to spatial data and decision support over the Web. Traditional DSS and Web-based spatial DSS can be further improved by integrating expert knowledge and utilizing intelligent software components (such as expert systems and intelligent agents) to emulate the human intelligence and decision making. These kinds of decision support systems are classified as intelligent decision support systems. The objective of this chapter is to discuss the development of an intelligent web-based spatial decision support system and demonstrate it with a case study for planning snow removal operations.

A Web-based real-time decision support system, connecting nine plants to a central information server, was implemented at a large oil and gas facility information centre in the Middle East. The case study presented shows how a real-time decision support system improved profitability. The implementation included a full well accounting package, allowing operators, engineers, petroleum engineers, and accountants to direct production and well properties, access test separator results, and reconcile production with the individual well settings. Qatar Petroleum chose Matrikon's ProcessNet as their Web-based decision support system, as it allowed them to retain their existing and proven business processes while enabling these processes through open connectivity, accessibility, and easy usability. By sharing and distributing information between the business processes and providing this information in single views, efficiency was greatly improved. Introduction Decisions at any corporation are made at a level which is usually applicable, but sometimes not pertinent, to the problem at hand. This situation comes about when all the information is not available to the key decision makers. An example is that of an operator, who might adjust process parameters beyond normal operation limits to address an issue caused upstream by changed feed-stock properties. This might put the product off-spec, whereas the feed-stock was purposely changed elsewhere to improve product quality. This illustrates the need for access to applicable information at every level in the plant. The operator should be able to view the feed-stock properties in the control room, and production staff should be able to analyse process conditions based on feed-stock properties to prevent future upsets. Whether made at the correct level or not, both long-term and short-term decisions are based on available information "processed" through experience or by analysis. Decisions based on experience are in danger of being biased or conservative, while decisions made by analysis could be misguided due to bad quality of data. An objective, fast, and reliable decision-making process requires timely and quality data, distributed to the correct levels in the corporation. The core technology implemented for the needed decision support system should allow the corporation to keep existing information- gathering infrastructure (i.e., process data in various forms, real-time and historian-based) and the long-time proven business processes (i.e., accounting data), while providing access to this information to the entire corporation, if desired. The barrier need no longer exist between plant process data, sales, supply chain, accounting, and management data. The project included configuration of many different and consolidated views, often comprised of Key Performance Indicators (KPIs) specific to the user's needs and job functions. Enabled with secured Web access, engineers can review the plant's performance while away from the site. At the same time, this engineer can query the maintenance database to check the last time a compressor was serviced, and correlate that to the current run-time of the compressor and its current power consumption. Discussion The following is a brief discussion of the base technology, followed by the details of the specific deployment at Qatar Petroleum, and how the project improved the decision-making processes on site.

Background: Dealing with the consequences of Thailand’s ageing population is a significant challenge. The growing usage of Information and Communication Technology (ICT) has the potential to support healthy and active ageing. Objective: This study aimed to develop a web-based Decision Support System (DSS) and evaluate its usability. Materials and methods: Research and Development (R&D) was a study design. It was divided into two phases: the development of the web-based DSS and its usability. Results: The web-based DSS was designed under the requirements of the users and comprised seven components: 1) the login page; 2) the main page; 3) the register page; 4) active ageing measurement; 5) decision-making for older people; 6) the Occupational Therapy (OT) recommendations system; and 7) exporting information. A System Usability Score (SUS) of 80.41 in the usability test showed that the web-based DSS was very user-friendly and had excellent efficacy, efficiency, and satisfaction. Conclusion: The web-based DSS effectively enhanced ageing for older people.

A web-based GIS Decision Support System for managing and planning USDA's Conservation Reserve Program (CRP)

The growing number of crisis situations around the world in recent years has highlighted the urgent need to improve the capability of Emergency Response Systems. When an emergency happens, the most important response is to promptly evacuate victims or potential victims from dangerous areas to safe places. The use of decision support systems can not only improve the efficiency of evacuation but also reduce losses on life and property. But existing computer-based decision support systems mainly aim at simplifying the evacuation process. The evacuation plans produced by them are difficult to follow and Emergency Response Personnel (ERP) cannot readily access emergency evacuation information in real time. In this paper, we present Blue Arrow, a web-based spatially-enabled decision support system for emergency evacuation planning. Based on their specific requirements, ERP can use the Blue Arrow System to generate various evacuation plans that have intuitive turn-by-turn instructions. These evacuation plans depend not only on the spatial information, but also on the underlying road network information. The Blue Arrow System implements different evacuation plans using the different variants of CCRP algorithm that focus on optimizing various aspects of the evacuation planning process including evacuation time and capacity.

Multiple Sclerosis (MS) is a common neurological disorder in recent years. The diagnosis process of the disease starts with the accurate and precise detection of lesions from MR images. In addition, important achievements are achieved with computer aided decision support systems, which are used as an auxiliary secondary tool in the detection of MS. In this study, we present a web-based decision support system (DeepMSWeb) developed via deep learning for the detection of MS lesions on a publicly-available dataset. Mask R-CNN architecture, one of the deep learning models, is used in the infrastructure of DeepMSWeb, and the developed web application has a flexible and user-friendly interface. In addition, experimental studies are carried out with DeepMSWeb on the dataset consisting of MR images for the detection of MS lesions, and the detection accuracy of the application is supported by similarity measurement metrics. Radiologists who have experienced DeepMsWeb are confirmed that DeepMSWeb can be used as a decision support system for the detection of MS lesions. In addition, it is evaluated that DeepMs Web can be used in different screen sizes, is easy to use and is a fast as decision support tool.

A web-based open-source decision support system (DSS) was developed to facilitate real-world engagement with dam-operating agencies in the decision-making process involving atmospheric modeling, hydrologic modeling, and web technology. The development process was decoupled into the container (frontend) and the modeling framework for the content (backend), to arrive at an intelligent system that improves the productivity and independent reuse of each component. The backend framework uses the weather forecasts from Numerical Weather Prediction models, downscales to a finer resolution, and simulates hydrologic and data-based artificial neural network models to optimize operations. The frontend architecture disseminates the forecasted meteorological variables, reservoir inflow, optimized operations, and retrospective weekly assessment of forecasts and hydropower benefits. The framework is automated and operationalized over the Detroit dam (Oregon) to generate the daily optimized release decisions. However, backend scripts and frontend elements are flexible and customizable enough that the DSS can be reproduced for other dams. The optimization of reservoir operations based on weather forecasts results in significant additional hydropower benefit without compromising other objectives when compared to the conventional operations. More importantly, the platform helps visualize for the dam operator how much more ‘smarter’ operations can be if weather forecasts and open-source technology are used.

The facility location problem is a widely studied classical operations research problem. To address this problem we implement an algorithm that calculates the exact solution for a given multiple capacitated facility location problem so long as that exists. Many issues of this problem belong to the NP-hard class of algorithms and as a result the computation time is disappointing for large networks. Therefore, we present a dynamic approximation algorithm for the solution of this problem that is capable to compute an approximation solution in an acceptable time interval. The aforementioned algorithms are integrated in a web-based Decision Support System (DSS). The DSS offers the possibility to create either a random or a custom graph and to evaluate both algorithms by performing alternative scenarios for the future development of the market. Finally, the DSS can export the results of the evaluation to a Microsoft Word document for further use.

Building Information Modelling (BIM) is a revolutionary technology and process which has led to new ways of thinking and working in construction project delivery. Governments around the world are promoting BIM capacity to eliminate waste on public projects and even mandating its use as part of the reform of the public construction sector and cost-saving strategy. In the UK, the government has declared that no public projects can be accepted without using BIM by 2016. Evidence shows that BIM adoption among small and medium sized enterprises (SMEs) is currently lagging behind and they are losing out in wining publicly funded projects. There are existing sets of frameworks and matrixes developed to assist BIM implementation. However, guidance and frameworks for SMEs are lacking at the present time. This paper reports on criteria and framework as a part of an ongoing study that seeks to develop a web-based Decision Support System (DSS) to assist SMEs to broker risks and rewards of adopting BIM in project delivery. The paper presents the framework and its criteria of the proposed DSS assists SMEs to make informed decisions about whether or not use BIM to deliver building projects, according to specific criteria and queries. This includes tradeoff risks and rewards, broken down into several criteria such as BIM readiness, involvement stage, project value, funding and procurement route.

Gold Coast Water is responsible for the management of the water, recycled water and wastewater assets of the City of the Gold Coast on Australia's east coast. Excess treated recycled water is released at the Gold Coast Seaway, a man-made channel connecting the Broadwater Estuary with the Pacific Ocean, on an outgoing tide in order for the recycled water to be dispersed before the tide changes and re-enters the Broadwater estuary. Rapid population growth has placed increasing demands on the city's recycled water release system and an investigation of the capacity of the Broadwater to assimilate a greater volume of recycled water over a longer release period was undertaken in 2007. As an outcome, Gold Coast Water was granted an extension of the existing release licence from 10.5 hours per day to 13.3 hours per day from the Coombabah wastewater treatment plant (WWTP). The Seaway SmartRelease Project has been designed to optimise the release of the recycled water from the Coombabah WWTP in order to minimise the impact to the receiving estuarine water quality and maximise the cost efficiency of pumping. In order achieve this; an optimisation study that involves intensive hydrodynamic and water quality monitoring, numerical modelling and a web-based decision support system is underway. An intensive monitoring campaign provided information on water levels, currents, winds, waves, nutrients and bacterial levels within the Broadwater. This data was then used to calibrate and verify numerical models using the MIKE by DHI suite of software. The Decision Support System will then collect continually measured data such as water levels, interact with the WWTP SCADA system, run the numerical models and provide the optimal time window to release the required amount of recycled water from the WWTP within the licence specifications.

Web-based spatial decision support system for optimum route to forest fires: A case of Viphya plantations

Distributed decision support and organizational connectivity: A case study

SmartScape™: A web-based decision support system for assessing the tradeoffs among multiple ecosystem services under crop-change scenarios

Government institutions are often constrained when making decisions regarding selecting the best employees due to the unavailability of an adequate decision support system. In fact, with this system, determining the best employees can be done easily and quickly. One method that can be used is the Analytical Hierarchy Process (AHP) which supports multi-criteria selection. This web-based decision support system designed has six criteria. From the calculation results of the priority weight value for each standard, the Court Punishment criteria have the highest priority value compared to other measures. Thus the requirements for this Court Punishment will be the primary consideration in calculating the value of outstanding employees. These criteria are then used for the simulation of 10 ministry employees. The simulation results show that the designed AHP technique is proven to prepare data for high achieving employee candidates accurately.

The aim of this study was to assess the agreement in the management of periapical radiolucencies associated with root-filled teeth among dental students using and without using a new web-based clinical decision support system (CDSS) application (DentalHelp). One hundred ten dental students from two universities were chosen to evaluate 10 cases of periapical radiolucencies associated with root-filled teeth. Each case included the clinical signs and symptoms, clinical photographs, periapical radiographs, and a small-volume cone beam computed tomography (CBCT) scan. Participants were asked to select one proposed management option and to assess the degree of difficulty in making this decision. Four weeks later, students entered case information into the DentalHelp web app. The app's algorithm then autonomously generated a suggested management option for each case. Cohen's unweighted Kappa index was calculated to estimate the level of concordance between each suggested management option for each case with and without using the app. There was a significant change in the management choice by students when using the CDSS compared to when they did not use it (p<0.05). A global Kappa index of 0.21 (95% CI: 0.17-0.25) indicated a weak general agreement. Cohen's Kappa index of 0.07 (95% CI: 0.02-0.11) demonstrated poor agreement between the level of difficulty of decision-making with and without the app. The DentalHelp web app was effective in assisting dental students with clinical decision-making for periapical radiolucencies associated with root-filled teeth.