Prototype Decision Support System Research Articles

Development of Decision Support System (DSS) for surface runoff and erosion control structures planning: a case study in the Upper Citarum Watershed & towards sustainability inland waters ecosystem

The watershed ecosystem is a complex system in which there are numerous transportation and transfer mechanisms of mass and energy. To make management more effective in all national critical watersheds, there needs to be more scientific, evidence-based policymaking that is based on an understanding of the system and mechanisms of the socio-hydrological processes of the watershed. The availability of Decision Support System (DSS) technology can be the appropriate approach to this need because DSS can be an interface between scientific and practical needs (easy-to-use, easy-to-access, user-friendly). However, the availability and implementation of DSS as an important tool in the optimal design of sustainable watershed management in Indonesia are still very limited. This study aims to create a prototype DSS that practitioners and policymakers can use to identify priority areas and optimize technical solutions for controlling surface runoff and soil erosion at various scales in the internal watershed. Herein, the spatial-based numerical modeling system and process mechanism; the database and knowledge; and the Graphical User Interface (GUI) are the three main components that have been used as a framework for model-based DSS development. A distributed rainfall-runoff-erosion model (EcoHydro) is the main engine of DSS for spatially quantifying dimensions of hydrological responses, erosion rate, and sediment production according to the user’s specifications and providing design options for control measures of them. The upper Citarum river basin in West Java, which is a critical and first national priority watershed, has been selected as the case study for DSS development and to demonstrate its application. Furthermore, the resulting DSS can later be developed for use in watersheds and other nationally critical lakes.

The development of a novel decision support system for regional land use planning for brownfield land

Digital tools, particularly specialised decision support systems (DSSs), can be utilized to assist in the complex process of brownfield redevelopment. Existing brownfield DSSs typically focus on site-specific, late-stage applications, and socioeconomic factors are often overlooked. In this paper, we present a novel DSS aimed at providing support for early-stage, city region-scale brownfield land use planning and redevelopment. The proposed DSS is a prototype WebGIS application that enables land use planners and other brownfield regeneration professionals to examine a region and a set of sites during the initial planning phase for brownfield redevelopment. The DSS includes three bespoke modules comprising: (1) Land Use Potential (residential, commercial, and public open space), (2) risks posed by contamination and geotechnical hazards, (3) data pertinent to brownfield economic viability assessments. We outline a use case for this DSS, developed through comprehensive user-requirements gathering, and subsequently describe the techniques employed to construct the DSS modules and user interface. Finally, we present the results of user testing, wherein case-study stakeholders assessed the DSS. The feedback obtained during user testing aided in the identification of areas for improvement with regard to the functionality, usability, and effectiveness of the DSS in supporting decision-makers. The feedback was utilized to implement iterative improvements to the DSS and to plan future developments for the prototype DSS.

Pyrrhic victory? Towards a decision support system for simulation projects in industrial practice based on Fuzzy cognitive mapping

Simulation has become a popular methodology to solve complex non-linear problems in research and practice. Yet, particularly in industrial practice, assessing the individual applicability of simulation projects remains an intricate, time-consuming and precarious challenge. This paper models simulation applicability by tapping into expert knowledge of simulation engineers with multiple years of simulation project experience. We decompose simulation applicability into a number of interconnected concept variables, which ultimately control the likelihood of project success and failure. The system is modeled in the form of a Fuzzy Cognitive Map, in which the nodes represent positive or negative project influencers in variables and link their interconnectedness. In doing so, this paper is the first to exceed the scope of simulation success factors or systemic risk assessment by addressing the need to provide holistic decision support on the context-dependent applicability of simulation projects. We contextualize organizational, environmental and methodology-related cause-effect relationships to develop a Fuzzy Decision Support System (FDSS) prototype that can guide scholars and practitioners when planning to adopt a simulation project for industrial purposes. The approach is illustrated using three applications, where the assessment has been conducted for (1) a large, (2) a medium-sized, as well as (3) a small organization, and evaluated through sensitivity analyses and a comparative survey. Ultimately, our study contributes to existing knowledge on success and risk factors for simulation projects, extends the realms of FCM to the case of simulation projects and sets a starting point for additional research on the context-specific applicability of the simulation methodology.

Accessible satellite data decision support systems for Yurok Tribe forest management

Local decisionmakers managing forests for sequestering carbon and mitigating disturbance face increasing pressures from climate change, while needing to derive revenue from forests resources and balance diverse stakeholder concerns. Decision support systems (DSS) that use freely available satellite data are effective tools for forest management decision-making, due to the broad spatial coverage and temporal consistency of this data. However, the literature demonstrates that many DSS developed by researchers incorporating satellite data are often not utilized by decision makers. This is in part due to a lack of stakeholder engagement in DSS development. Here, we employ an intentional stakeholder engagement process to develop an accessible DSS incorporating satellite data. This process is applied with the Yurok Tribe – an Indigenous community in California – towards the goal of managing forests for carbon sequestration. We use system architecture framework (SAF) to translate stakeholder objectives into targeted analyses and the environment-vulnerability-decision-technology (EVDT) integrated modeling framework to incorporate contextual local infrastructure and land ownership information alongside satellite data analyses. Satellite data analyses classify tree cover and track Normalized Difference Vegetation Index (NDVI) over time. We compare and show that trends in our analysis agree with independent global tree cover and biomass datasets. These analyses meet the Tribe's objective for tracking forest trends in Yurok carbon sequestration projects, while also detecting undesirable conifer encroachment on culturally-important prairies traditionally managed as open landscapes. Additionally, integration of community infrastructure data alongside satellite-derived drought, forest fire, and smoke density information via EVDT meets another Yurok Tribe objective of identifying negatively impacted Tribal areas for aid allocation. User feedback sessions with participating Yurok Tribe employees assess the prototype DSS and show 80% of users providing high ratings (≥75%) for information accuracy, relevancy, and sufficiency, demonstrating that our DSS development process overcomes previous gaps to DSS use and satellite data accessibility to augment Yurok Tribe forest management decision making.

A mobile application-based decision support system for routing and decision making problems

Industry 4.0 applications and related topics open up new opportunities in problem solving for states, companies, production systems and individuals. These developments make data gathering and processing easier. Moreover, mobile devices offer more sophisticated approaches since they can gather data and process the collected data by various algorithms, which are embedded to devices via applications. Appro-priate problem related algorithms can be coded and they can be deployed to mobile devices to increase mobility and ease of use for end users. In the present paper, a prototype of mobile application-based decision support system is developed for industrial systems including routing and multi-criteria decision making problems. The developed application is comprised of several modules including registry and signup modules as well as problem solving modules. Problem solving modules can gather data from both user and outer sources such as GPS in order to solve both routing problems and multi-criteria decision-making problems. The mentioned application adopts a Simulated Annealing Algorithm to find promising routes for the users, while multi-criteria decision-making module uses both Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on the choice of the users. The proposed application is coded on Java and Python due to their strong integration capabilities with Android. According the results of the developed prototype, the proposed application offer promising results and ease of use.

Predictive Maintenance in Industry 4.0 for the SMEs: A Decision Support System Case Study Using Open-Source Software

Predictive maintenance is one of the most important topics within the Industry 4.0 paradigm. We present a prototype decision support system (DSS) that collects and processes data from many sensors and uses machine learning and artificial intelligence algorithms to report deviations from the optimal process in a timely manner and correct them to the correct parameters directly or indirectly through operator intervention or self-correction. We propose to develop the DSS using open-source R packages because using open-source software such as R for predictive maintenance is beneficial for small and medium enterprises (SMEs) as it provides an affordable, adaptable, flexible, and tunable solution. We validate the DSS through a case study to show its application to SMEs that need to maintain industrial equipment in real time by leveraging IoT technologies and predictive maintenance of industrial cooling systems. The dataset used was simulated based on the information on the indicators measured as well as their ranges collected by in-depth interviews. The results show that the software provides predictions and actionable insights using collaborative filtering. Feedback is collected from SMEs in the manufacturing sector as potential system users. Positive feedback emphasized the advantages of employing open-source predictive maintenance tools, such as R, for SMEs, including cost savings, increased accuracy, community assistance, and program customization. However, SMEs have overwhelmingly voiced comments and concerns regarding the use of open-source R in their infrastructure development and daily operations.

Development of a Decision Support System Prototype for Time and Cost Reduction in Collecting Recyclable Waste

Development of a Decision Support System Prototype for Time and Cost Reduction in Collecting Recyclable Waste

Effects of data time lag in a decision-making system using machine learning for pork price prediction

Spain is the third-largest producer of pork meat in the world, and many farms in several regions depend on the evolution of this market. However, the current pricing system is unfair, as some actors have better market information than others. In this context, historical pricing is an easy-to-find and affordable data source that can help all agents to be better informed. However, the time lag in data acquisition can affect their pricing decisions. In this paper, we study the effect that data acquisition delay has on a price prediction system using multiple prediction algorithms. We describe the integration of the best proposal into a decision support system prototype and test it in a real-case scenario. Specifically, we use public data from the most important regional pork meat markets in Spain published by the Ministry of Agriculture with a two-week delay and subscription-based data of the same markets obtained on the same day. The results show that the error difference between the best public and data subscription models is 0.6 Euro cents in favour of the data without delay. The market dimension makes these differences significant in the supply chain, giving pricing agents a better tool to negotiate market prices.

Improvement of demand fulfillment in Advanced Planning System through decentralized decision support system

In a tight supply situation, industries such as the semiconductor sector face challenges in allocating supply to customers through complex hierarchical supply chain planning systems, which involve the use of Advanced Planning Systems (APS) and human planners in updating allocation plans. This article focuses on designing, developing, and testing a prototype Decision Support System (DSS) based on a mathematical model to assist human planners and improve demand fulfillment systems in APS when demand exceeds supply. By incorporating digitalization and supporting human interventions in planning, our approach enhances these systems.The bi-objective mathematical model aims to maximize customer service levels while maintaining a maximum amount of stock available for unforeseen planning situations. We developed the proposed mathematical model into a web application decision support tool, called the Regional Customer Allocation Support Tool (ReCAST). The ReCAST prototype was applied to a semiconductor case study, demonstrating its effectiveness in supporting decision-making processes by planners in a real-world context.

Sistem Pendukung Keputusan Pemilihan Employee of The Month Menggunakan Metode SAW Pada PCJL Jambi

Employee of The Month is a program for best employee selection based on HSE criteria on PCJL – Jambi, most of the process performed manually which spending a lot of time to complete. Author conducted research as an effort to solve the problem by analyzing and designing a prototype of Decision Support System (DSS) utilized Simple Additive Weighting (SAW) method. Expected, by the design of this system will be a useful solution for the process and shorten the implementation time of the program.

Teacher Performance Evaluation Decision Support System Using Simple Additive Weighting: Case Study Mentari Intercultural School

The role of the teacher is critical in the process of developing a country's resources. In this case, efforts are required to maintain teacher quality. Evaluating teacher performance is one method of maintaining the quality of these teachers. However, many teacher performance evaluation processes are still carried out manually in Indonesian schools. The solution to this problem in this study is the creation of a teacher performance evaluation system in the form of a web-based Decision Support System prototype by applying Simple Additive Weighting. Several pieces of software, including Java, Hypertext Markup Language, PHP, and Cascading Style Sheets, are used in this study. As a result, this system contributes to the simplification of the teacher evaluation process.

A web-based integrated Bayesian decision support system for rangeland management in semi-arid watersheds

A Bayesian web-based Decision Support System (DSS) was designed and developed to assist various stakeholders in improving biophysical and socio-economic conditions in a stressed and fragile watershed by systematically evaluating current watershed characteristics, introducing feasible solutions as well as choosing the best ones. The need to deliver a flexible DSS has been the motivation to look for a decision toolbox accommodating the concerns and priorities of a wide range of stakeholders as the potential users. This assists the users to make informed decisions based on quantified potential outcomes. The DSS main components include a database management system, a model base management system, multi-attribute decision-making models, and a graphical user interface. This prototype DSS provides a brief introduction of a pilot watershed characterized with semi-arid conditions in the Mid-north of Iran and then presents the problem structure using the DPSIR approach. A set of vegetation-based solutions and their spatial distribution considering some scenario rules are provided to the DSS users. The various impacts of the proposed solutions are predicted using several models in the Bayesian network modeling context integrating qualitative and quantitative knowledge and data. The DSS developed in this study facilitates knowledge transfer and involvement of stakeholders including practitioners and local communities in the decision making process. This DSS has been designed into a web-based and open-source framework for easier and inclusive access. The DSS can be tailored to fit a broader class of environmental problems.

Using Geopandas for locating virtual stations in a free-floating bike sharing system

Free-floating bike-sharing systems can have a positive influence on the mobility of urban centers and developing efficient localization strategies is crucial to avoid crowding at peak times and increase service availability. Our study aims to efficiently resolve the location of virtual bike stations in a Latin American city through a geospatial data wrangling methodology that allows us to respond opportunely to the potential demand forecasted for the city. This approach is implemented in Python, and it uses the Geopandas and LocalSolver libraries to determine locations for the virtual bike stations that maximize the system coverage. The decision-making process is supported by a binary integer mathematical programming model, and the instances are built from intercity travel surveys that provide realistic data based on travel demand. The developed decision support system prototype provides a recommendation about where virtual bike stations should be located during peak hours and improve general availability by more than 37%. Moreover, when the system's users participate in the relocation of bicycles, the model can eliminate up to 80% of the CO2 emissions and nearly 50% of the operational costs associated with the relocation process.

Multiple sclerosis clinical decision support system based on projection to reference datasets.

Multiple sclerosis (MS) is a multifactorial disease with increasingly complicated management. Our objective is to use on-demand computational power to address the challenges of dynamically managing MS. A phase 3 clinical trial data (NCT00906399) were used to contextualize the medication efficacy of peg-interferon beta-1a vs placebo on patients with relapsing-remitting MS (RRMS). Using a set of reference patients (PORs), selected based on adequate features similar to those of an individual patient, we visualize disease activity by measuring the percentage of relapses, accumulation of new T2 lesions on MRI, and worsening EDSS during the clinical trial. We developed MS Vista, a functional prototype of clinical decision support system (CDSS), with a user-centered design and distributed infrastructure. MS Vista shows the medication efficacy of peginterferon beta-1a versus placebo for each individual patient with RRMS. In addition, MS Vista initiated the integration of a longitudinal magnetic resonance imaging (MRI) viewer and interactive dual physician-patient data display to facilitate communication. The pioneer use of PORs for each individual patient enables personalized analytics sustaining the dialog between neurologists, patients and caregivers with quantified evidence.

A PERSONALIZED TRAVEL RECOMMENDER SYSTEM USING FUZZY ANALYTIC HIERARCHY PROCESS

Information and communication technologies have a deep implication for the industry. This combination is being used extensively in an excessive variety of functions and numerous applications. On the other hand, tourism has become an extremely dynamic system. The globalization enabled by technology development and budget travel cost has greatly increased competition. Decision support system (DSS) can play an important role to organizations and people who managing tourism destinations. The main intention of this research is to apply Decision Support System (DSS) in tourism. It aims to establish a personalized travel recommender system that is interactive and can be easily shared and integrated in order to verify the concept of providing tourism decision support system interactively. Specifically, the study aims to achieve the following objectives; (1) To evaluate and measure criteria and alternatives performance; (2) To analyze the ranking of criteria and alternatives; (3) To recommend tourist in terms of islands, accommodation, activities and etc. based on budget. Evaluation module enables experts in tourism to evaluate criteria and alternatives of small islands to indicate islands’ performance. Analysis module will provide reports for performance of criteria and alternatives based on Best Non-fuzzy Performance. A risk analysis model for travel recommender system development using a fuzzy set approach is proposed and incorporated into the Fuzzy Decision Support System (FDSS).This study presents fuzzy-AHP as a proposed method to apply with decision making in social attributes by applying fuzzy approach. A web-based prototype Decision Support System (DSS) is designed and developed in order to prove the objectives.

Прототип системы поддержки принятия технологических решений для процесса ЭЛС

Прототип системы поддержки принятия технологических решений для процесса ЭЛС

A Clinical Decision Support System for Assessing the Risk of Cervical Cancer: Development and Evaluation Study.

BackgroundCervical cancer has been recognized as a preventable type of cancer. As the assessment of all the risk factors of a disease is challenging for physicians, information technology and risk assessment models have been used to estimate the degree of risk.ObjectiveThe aim of this study was to develop a clinical decision support system to assess the risk of cervical cancer.MethodsThis study was conducted in 2 phases in 2021. In the first phase of the study, 20 gynecologists completed a questionnaire to determine the essential parameters for assessing the risk of cervical cancer, and the data were analyzed using descriptive statistics. In the second phase of the study, the prototype of the clinical decision support system was developed and evaluated.ResultsThe findings revealed that the most important parameters for assessing the risk of cervical cancer consisted of general and specific parameters. In total, the 8 parameters that had the greatest impact on the risk of cervical cancer were selected. After developing the clinical decision support system, it was evaluated and the mean values of sensitivity, specificity, and accuracy were 85.81%, 93.82%, and 91.39%, respectively.ConclusionsThe clinical decision support system developed in this study can facilitate the process of identifying people who are at risk of developing cervical cancer. In addition, it can help to increase the quality of health care and reduce the costs associated with the treatment of cervical cancer.

Land Use Spatial Optimization Using Accessibility Maps to Integrate Land Use and Transport in Urban Areas

The scarcity of urban land resources requires a well-organized spatial layout of land use to better accommodate human activities, however, as a widely accepted concept, the integration of land use and transport is not given due consideration in land use spatial optimization (LUSO). This paper aims to integrate land use and transport in LUSO to support urban land use planning. Maximizing accessibility fitness, which follows the underlying logic between land use types and transport characteristics, is introduced into multi-objective land use spatial optimization (MOLUSO) modelling to address transport considerations, together with widely-used objectives such as maximizing compactness, compatibility, and suitability. The transport characteristics, in this study, are identified by driving accessibility, cycling accessibility, and walking accessibility. Accessibility maps, which quantify and visualize the spatial variances in accessibility fitness for different land use types, are developed based on the empirical results of the relationship between land use types and transport characteristics for LUSO and addressing policy issues. The 4-objective LUSO model and a corresponding non-dominated sorting genetic algorithm (NSGA-II) based optimization method constitute a prototype decision support system (DSS) for urban land use planning. Decision-makers (e.g., planning departments) can choose an ideal solution to accommodate urban development needs from a set of Pareto-optimal alternatives generated by the DSS. The approaches to creating accessibility maps and MOLUSO modelling are demonstrated by the case study of Eindhoven, the Netherlands. This study advocates limited changes to the current land use pattern in urban planning, and the LUSO emphasizes urban renewal and upgrading rather than new town planning.

A Novel Diagnostic Decision Support System for Medical Professionals: Prospective Feasibility Study

BackgroundContinuously growing medical knowledge and the increasing amount of data make it difficult for medical professionals to keep track of all new information and to place it in the context of existing information. A variety of digital technologies and artificial intelligence–based methods are currently available as persuasive tools to empower physicians in clinical decision-making and improve health care quality. A novel diagnostic decision support system (DDSS) prototype developed by Ada Health GmbH with a focus on traceability, transparency, and usability will be examined more closely in this study.ObjectiveThe aim of this study is to test the feasibility and functionality of a novel DDSS prototype, exploring its potential and performance in identifying the underlying cause of acute dyspnea in patients at the University Hospital Basel.MethodsA prospective, observational feasibility study was conducted at the emergency department (ED) and internal medicine ward of the University Hospital Basel, Switzerland. A convenience sample of 20 adult patients admitted to the ED with dyspnea as the chief complaint and a high probability of inpatient admission was selected. A study physician followed the patients admitted to the ED throughout the hospitalization without interfering with the routine clinical work. Routinely collected health-related personal data from these patients were entered into the DDSS prototype. The DDSS prototype’s resulting disease probability list was compared with the gold-standard main diagnosis provided by the treating physician.ResultsThe DDSS presented information with high clarity and had a user-friendly, novel, and transparent interface. The DDSS prototype was not perfectly suited for the ED as case entry was time-consuming (1.5-2 hours per case). It provided accurate decision support in the clinical inpatient setting (average of cases in which the correct diagnosis was the first diagnosis listed: 6/20, 30%, SD 2.10%; average of cases in which the correct diagnosis was listed as one of the top 3: 11/20, 55%, SD 2.39%; average of cases in which the correct diagnosis was listed as one of the top 5: 14/20, 70%, SD 2.26%) in patients with dyspnea as the main presenting complaint.ConclusionsThe study of the feasibility and functionality of the tool was successful, with some limitations. Used in the right place, the DDSS has the potential to support physicians in their decision-making process by showing new pathways and unintentionally ignored diagnoses. The DDSS prototype had some limitations regarding the process of data input, diagnostic accuracy, and completeness of the integrated medical knowledge. The results of this study provide a basis for the tool’s further development. In addition, future studies should be conducted with the aim to overcome the current limitations of the tool and study design.Trial RegistrationClinicalTrials.gov NCT04827342; https://clinicaltrials.gov/ct2/show/NCT04827342

A Digital Advisor Twin for Crop Nitrogen Management

Farmers and consultants face an unmanageable amount of diverse knowledge and information for crop management decisions. To determine optimal actions, decision makers require knowledge-based support. In this way, decisions can be improved and heuristics can be replaced over time. The study presents a digital knowledge base with an integrated decision support system (DSS), using the example of nutrient supply, specifically nitrogen (N), fertilization. Therefore, the requirements of farmers and crop consultants for DSS to inform fertilization decisions for winter wheat (Triticum aestivum L.) were elaborated using surveys, expert interviews, and a prototype test. Semantic knowledge was enriched by expert knowledge and combined in a web application, the Crop Portal. To map regional and personal decision making patterns and experiences, the tacit knowledge on the complex advisory problem of N fertilization is made digitally usable. For this purpose, 16 fuzzy variables were specified and formalized. Individual decision trees and their interactions with an integrative knowledge base were used to multiply the consulting reach of experts. Using three consultants and nine model farms from different soil–climate areas in Germany, the Crop Portal was tested under practical conditions and the perceived pragmatic and hedonic quality of the system was evaluated using a standardized questionnaire. The field test showed that the variation in fertilizer recommendations from the ‘digital advisor twin’ ranged from 5 kg N ha−1 to 16 kg N ha−1 when compared with the decisions of the experts in the field. The study presents the participatory development and evaluation of a rule-based DSS prototype in agricultural practice.