Design Of Decision Support System Research Articles

Dark cities or cities of light? – sunlight amenity preservation at whole-city scale using a spatio-temporal decision support approach

Public open spaces are fundamentally important for the health and well-being of citizens in densely populated cities. If not carefully planned, high-density urban development can overshadow adjacent open spaces, resulting in poor quality, dark and oppressive winter conditions. Current planning control approaches for protecting light amenity in cities are often limited to simple overshadowing impact diagrams (e.g. shadows cast on the equinox at 9 a.m., 12 p.m. and 3 p.m.). In cities transitioning from low to higher density, comprising more complex urban forms and more extreme seasonal light amenity dynamics, these static approaches are insufficient. This paper outlines the development of a spatio-temporal design decision support system for analysing and protecting the light amenity of public open spaces, applied to a capital city in Australia. The system described has two parts: Firstly, to assess the overshadowing of existing public open spaces and identify those in need of protection (Part A), and secondly to generate planning restrictions to protect designated open spaces from future deprivation of light (Part B). For Part A, we use a graphics processing unit accelerated aggregate-shadow (15 minute increments) calculation applied to a detailed city-wide 3D model generated from billions of points of aerial survey (LiDAR) data. For Part B, we use a reversed solar ray casting approach we call the ‘Subtracto-Sun’ which allows a user to specify a time range (e.g. 9 a.m.–4 p.m.) for multiple days of the year and subsequent generation of 3D maximum building height development envelopes. The output of this system was used by the local government to inform a proposed planning policy amendment for the City of Melbourne. The findings illustrate the potential for urban professionals to use the system to rapidly assess shadow impacts for existing and proposed, contextually accurate, large, complex urban environments with high levels of geometric and temporal details. The presented results are significant in that we develop and apply our spatio-temporal decision-support approach to a local government area, successfully informing planning height restriction decisions to protect daylight amenity of public open spaces in need of protection. Our method for setting development height restrictions allows for higher density to be achieved, while not increasing the overshadowing of critical open space infrastructure during designated times.

Усовершенствование ситуационной методологии разработки систем поддержки принятия решений для предприятий

The article proposes a technique for the development of a situational methodology for the development of decision support systems for organizational systems, taking into account the influence of the time factor. In the existing methodologies, insufficient attention is paid to the links between the organizational part of the model and the parametric method, with the help of which the choice of an acceptable solution is made, the assessment of the consequences of the decisions made is often ignored, and the application of these methodologies to solving practical problems is difficult. The main purpose of the article is to summarize the existing approaches to decision making based on an improved methodology for developing decision support systems, which is aimed at eliminating the identified problems. To achieve the goal, systemic and situational approaches, ontological engineering, structural analysis of systems, and methods for classifying complex objects were used. As a result, proposals were formed to improve the situational approach to creating decision support systems, including the definition of a hierarchy of decision-making process components, descriptions of transition methods between decision-making levels, with an emphasis on the level of choice of a parametric method for transforming a set of decision options into effective solution. The article concludes that the proposed methodology will simplify the process of designing decision support systems for enterprises.

Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index

In cotton, an optimal balance between vegetative and reproductive growth will lead to high yields and water-use efficiency. Remote sensing estimations of vegetation variables such as crop coefficient (Kc), Leaf Area Index (LAI), and crop height during plant development can improve irrigation management. Optical and Synthetic Aperture Radar (SAR) satellite imagery can be a useful data source since they provide synoptic cover at fixed time intervals. Furthermore, they can better capture the spatial variability in the field compared to point measurements. Since clouds limit optical observations at times, the combination with SAR can provide information during cloudy periods. This study utilized optical imagery acquired by Sentinel-2 and SAR imagery acquired by Sentinel-1 over cotton fields in Israel. The Sentinel-2-based vegetation indices that are best suited for cotton monitoring were identified, and the most robust Sentinel-2 models for Kc, LAI, and height estimation achieved R2 = 0.879, RMSE= 0.0645 (MERIS Terrestrial Chlorophyll Index, MTCI); R2 = 0.9535, RMSE= 0.8 (MTCI); and R2 = 0.8883, RMSE= 10 cm (Enhanced Vegetation Index, EVI), respectively. Additionally, a model based on the output of the SNAP Biophysical Processor LAI estimation algorithm was superior to the empirical LAI models of the best-performing vegetation indices (R2 =0.9717, RMSE=0.6). The most robust Sentinel-1 models were obtained by applying an innovative local incidence angle normalization method with R2 = 0.7913, RMSE= 0.0925; R2 = 0.6699, RMSE= 2.3; R2 = 0.6586, RMSE= 18 cm for the Kc, LAI, and height estimation, respectively. This work paves the way for future studies to design decision support systems for better irrigation management in cotton, even at the sub-plot level, by monitoring the heterogeneous development of the crop from space and adapting the irrigation accordingly to reach the target development at different growth stages during the season.

Analisa Perbandingan Keefektipan Waktu Proses Perhitungan Metode SAW dan WP Dalam Menentukan Siswa Berprestasi

<p><em>Achievement students are the achievements of every student in school, stand out among other students and sent to take part in the competitions with the name of the school becoming a student pride. The process of achieving achievements that still uses a manual system that actually less effective in student achievement. Therefore, the author designed decision support system comparative analysis between the two methods Simple Additive Weighting (SAW) and Weight Product (WP) with criteria that have been determined criteria. These two methods are compared to find out which method is more effective in the calculation process which is expected to help in determining students achievers.</em></p><p><strong>Keywords: </strong><em>SPK; SAW; WP; Achievement; School</em></p>

Decision-centered design of a clinical decision support system for acute management of pediatric congenital heart disease

Children with congenital heart disease (CHD), have fragile hemodynamics and can deteriorate due to common childhood illnesses and the natural progression of their disease. During these acute periods of deterioration, these children often present to their local emergency departments (ED) where expertise in CHD is limited, and appropriate intervention is crucial to their survival. Previous studies identified that determining the appropriate intervention for CHD patients can be difficult for ED physicians, particularly since key components of effective decision making are not being met. Although key components of effective decision making for ED physicians have been identified, they have yet to be transformed into actionable guidance. We used decision centered design (DCD) to translate key components of decision making into decision requirements and associated design concepts, that we subsequently incorporated into a prototype clinical decision support system (CDSS). Using framework analysis, transcripts from Critical Decision Method interviews of CHD experts and ED physicians were inductively coded to identify key decision requirements for ED physicians that are currently not well supported, and their associated design concepts. A design workshop was held to refine the identified key decision requirements and design concepts as well as to sketch information that would satisfy the identified requirements. These were iteratively incorporated into a prototype CDSS. Three decision requirements: (1) distinguish the patient's unique physiology based on their unique cardiac anatomy, (2) explicitly consider CHD specific differential diagnoses to allow a more structured reflection of diagnosis, and (3) select CHD appropriate interventions for each patient, were identified. These requirements along with design concepts and information needs identified through the design workshop were incorporated into the CDSS prototype. We identified key decision requirements and associated design concepts, that informed the design of a CDSS to provide actionable guidance for ED physicians when managing CHD patients. Meeting ED physicians' decision components with a CDSS requires the translation of their key decision requirements in its design. If not, we risk creating designs that interfere with clinician performance.

Digital tools for brownfield redevelopment: Stakeholder perspectives and opportunities

Brownfield redevelopment is a complex process often involving a wide range of stakeholders holding differing priorities and opinions. The use of digital systems and products for decision making, modelling, and supporting discussion has been recognised throughout literature and industry. The inclusion of stakeholder preferences is an important consideration in the design and development of impactful digital tools and decision support systems. In this study, we present findings from stakeholder consultation with professionals from the UK brownfield sector with the aim of informing the design of future digital tools and systems. Our research investigates two broad themes; digitalisation and the use of digital tools across the sector; and perceptions of key brownfield challenge areas where digital tools could help better inform decision-makers. The methodology employed for this study comprises the collection of data and information using a combination of interviews and an online questionnaire. The results from these methods were evaluated both qualitatively and quantitatively. Findings reveal a disparity in levels of digital capability between stakeholder groups including between technical stakeholder types, and that cross-discipline communication of important issues may be aided by the development of carefully designed digital tools. To this end, we present seven core principles to guide the design and implementation of future digital tools for the brownfield sector. These principles are that future digital tools should be: (1) Stakeholder driven, (2) Problem centred, (3) Visual, (4) Intuitive, (5) Interactive, (6) Interoperable, and (7) Geospatial data driven.

A DSS Framework for Advanced Traffic Signal Control System Investment Planning

This article presents ongoing research on the initial development of a decision support system (DSS) framework for integrated emergency vehicle preemption and transit priority system investment planning. A conceptual intelligent DSS framework provides a holistic framework to perform analytical assessments of integrated emergency vehicle preemption and transit priority systems. Three analytical tools are presented for incorporation into future DSS design: the first addresses the potential impact of transit travel time reduction on transit operating costs; the second addresses the potential impact of reduced emergency vehicle crashes at signalized intersections on fire and rescue operating costs; and the third integrates fuzzy sets concepts and multiattribute decision-making methods to rank order transit signal priority strategy alternatives at the intersection level.

DYNAMIC OPTIMIZATION OF PORT OPERATIONS ONBOARD A TYPICAL RORO VESSEL, AIDED BY A SMART DECISION SUPPORT SYSTEM

The principal reason behind the genesis of Roll On – Roll Off vessel design was to increase the pace of loading/unloading operations by eliminating the requirement of cargo-handling equipment thus enabling the wheeled cargo to be driven to its stowage location. The practice being followed today leads to a higher dependency of port operations, associated to any vessel, on ballast water to counter the trim/list generated due to unplanned and imbalanced stowage of cargo. Though advancements in field of ship automation have led to a development of ballast water management system, still, optimization of port operations, namely loading/unloading, is missing due to operator’s consideration of usage of ballast as the only natural resort. The earlier research focus at speeding up the ballasting process, whereas the need of the hour is to counter the generation of ballast, because time is a pre-requisite of all ballast operation which cannot be overlooked. The study involves the generation of an Eco-Friendly Decision Support System which counters the moment of similar cargo against each other in a three dimensionally modeled ship simulation, thus, reducing requirement of ballast. The result of the study indicates that vessel’s intact statical stability could be maintained without performing ballasting operations, thus enabling cargo stowage at port to save time, as well as the surrounding marine ecosystem. The flexible design of Decision Support System makes its application possible for various other vessel-types, while at the same time opening a plethora of opportunities to inculcate Artificial intelligence, Big-Data analysis and whereas other advancements, thus, shortening the gap between automation and optimization.

Stock Prediction Using Machine Learning Algorithms

In the recent times, the stock markets have emerged as one of the top investment destinations for individual and retail investors due to the lure of huge profits that are possible with stock investments compared to more traditional and conservative forms of investments such as bank deposits, real estate and gold. The stock markets unlike other forms of investment are highly dynamic due to the various variables involved in stock price determination and are complex to understand for a common investor. Individual and small-time investors have to generate a portfolio of common stocks to reduce the overall risk and generate reasonable returns on their investment. This phenomenon has given way too many individual and retail investors incurring huge losses because their decisions are based on speculation and not on sound technical grounds. While there are financial advisory firms and online tools where individual investors can get professional stock investment advice, the reliability of such investment advice in the recent past has been inconsistent and not meeting the rigor of quantitative and rational stock selection process. Many of such stock analysts and the tools mostly rely on short term technical indicators and are biased by the speculation in the market leading to huge variances in their predictions and leading to huge losses for individual investors. While the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques is widely adopted in the financial domain, integration of AI/ML techniques with fundamental variables and long-term value investing is a lacking in this domain. Some of the stock portfolio tools available in the market use AI/ML techniques but are mostly built using technical indicators which makes them only suitable for general trend predictions, intraday trading and not suitable for long term value investing due to wide variances and reliability issues. The availability of a Financial Decision Support System which can help stock investors with reliable and accurate information for selecting stocks and creating an automated portfolio with detailed quantitative analysis is lacking. A Financial Decision Support System (DSS) that can establish a relationship between the fundamental financial variables and the stock prices that can VII automatically create a portfolio of premium stocks shall be of great utility to the individual investment community. As part of this thesis, the researcher has designed and developed a Financial Decision Support System (DSS) for selecting stocks and automatically creating portfolios with minimal inputs from the individual investors. The Financial DSS is based on a System Architecture combining the advantages of Artificial Intelligence (AI), Machine learning (ML) and Mathematical models. The design and development of the Financial DSS is based on the philosophy to combine various independent models and not rely on a single stock price model to increase the accuracy and reliability of the stock selections and increase the overall Return on Investment (ROI) of the stock portfolio. The Machine learning models are used to establish the relationship between fundamental financial variables and the price of the stock, a mathematical model is developed to calculate the intrinsic value of the stock taking in to account the full lifecycle of the stock which involves various phases and a comprehensive model to analyze the financial health of the stocks. The AI/ML stock models are independently trained using historical financial data and integrated with the overall Financial DSS. Finally, the Financial DSS tool with a graphical user interface is built integrating all the three models which shall be able to run on a general-purpose desktop or laptop. To reliably validate the Financial DSS, it has been subjected to wide variety of stocks in terms of market capitalization and industry segments. The Financial DSS is validated for its short term and long-term Return on Investment (ROI) using both historical and current real-time financial data. The researcher has reported that the accuracy of the AI/ML stock price models is greater than 90% and the overall ROI of the stock portfolios created by the Financial DSS is 61% for long term investments and 11.74% for short term investments. This system has the potential to help millions of individual investors who can make their financial decisions on stocks using this system for a fraction of cost paid to corporate financial consultants and value eventually may contribute to a more efficient financial system.

Ensuring proper management of building renovation based on an optimised decision-making model: Application in schools and social housing from southern Europe.

One of the main challenges for architects and technicians is the efficient management of the built environment, in response to the growing deterioration and obsolescence in the building stock. This research introduces the design, development and application of a novel decision support system that assesses the multidisciplinary advantages or disadvantages of different intervention strategies, mainly focused on schools and social housing. The concept of the model aims to gather, interrelate and weight different renovation factors and variables, according to technical, social, energy and economic parameters, quantifying results on the impacts, consequences and benefits of each renovation strategy and providing practical outcomes in the design, construction, management and maintenance stages. This study uses schools and multi-family buildings, located in southern Europe, to apply and test the system iteratively in both building typologies, serving to adjust the calculation model and demonstrate its operation and replicability. The results are classified according to different intensity levels with their corresponding design alternatives along with a graphical output of results for decision-making. This model is expected to contribute to policy-making by introducing new theoretical and practical renovation guidelines, with a rational adjustment of proposals and ensuring effective and feasible action strategies in the built environment.

EMF: Humanoid robots design scheme for child with autism

This research explores the humanoid facial design through Eyes-Mouth Features (EMF) as an indication of human embodied consciousness design. The study will be focused on portraying the practical model based on the syntax context focusing on Autism Spectrum Disorder (ASD) based design development. This design integrates the value of appealing sensations that can trigger the feelings and impressions of the child with ASD. It is critical to developing an effective decision support system for designers to deal with challenges relating to consumers' psychological preferences towards a humanoid facial character that appeals to human emotion. Keywords: Humanoid; Design; Autistic; Facial Appealing eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI7%20(Special%20Issue).3833

How are ontologies implemented to represent clinical practice guidelines in clinical decision support systems: protocol for a systematic review

BackgroundClinical practice guidelines are statements which are based on the best available evidence, and their goal is to improve the quality of patient care. Integrating clinical practice guidelines into computer systems can help physicians reduce medical errors and help them to have the best possible practice. Guideline-based clinical decision support systems play a significant role in supporting physicians in their decisions. Meantime, system errors are the most critical concerns in designing decision support systems that can affect their performance and efficacy. A well-developed ontology can be helpful in this matter. The proposed systematic review will specify the methods, components, language of rules, and evaluation methods of current ontology-driven guideline-based clinical decision support systems.MethodsThis review will identify literature through searching MEDLINE (via Ovid), PubMed, EMBASE, Cochrane Library, CINAHL, ScienceDirect, IEEEXplore, and ACM Digital Library. Gray literature, reference lists, and citing articles of the included studies will be searched. The quality of the included studies will be assessed by the mixed methods appraisal tool (MMAT-version 2018). At least two independent reviewers will perform the screening, quality assessment, and data extraction. A third reviewer will resolve any disagreements. Proper data analysis will be performed based on the type of system and ontology engineering evaluation data.DiscussionThe study will provide evidence regarding applying ontologies in guideline-based clinical decision support systems. The findings of this systematic review will be a guide for decision support system designers and developers, technologists, system providers, policymakers, and stakeholders. Ontology builders can use the information in this review to build well-structured ontologies for personalized medicine.Systematic review registrationPROSPERO CRD42018106501

APPLYING MULTIDIMENSIONAL CANONICAL PARTITIONING (MCP) APPROACH TO DESIGN URBAN DECISION-SUPPORT SYSTEM

The design of decision-support systems remains a challenge because no method has yet been approved, although several approaches have been proposed. Multidimensional Canonical Partitioning (MCP) is a supply-driven approach to design decision-support systems, previously proposed. In this approach, decision systems are designed from Entity/Relationship (E/R) schema. The schema provided as input is reduced to a flat relation, if it is not already the case, by using universal relations assumption. The design approach uses two main algorithms. The first is a greedy type heuristic algorithm, and the second a matching algorithm. The approach has six steps. In this paper, we implement algorithms used in the design approach. We then propose a graphical interface as a tool for implementing the approach. The tool, called PaCaM, is intended to be used by decision-support systems designers. We after all apply the design approach on urban transactional systems.

Design of an Assistant Decision Support System for Sports Training Based on Association Rules

In order to quantitatively evaluate the effect of sports training, it is necessary to track the dynamic characteristics of sports by using sports training aided decision support system. When the existing sports training assistant decision support system extracts the features of decision association rules, some redundant features will appear when establishing the global association rules, which increases the amount and difficulty of data calculation and affects the effect of assistant decision support. On this basis, the data fusion of assistant decision support information is carried out, and the optimal assistant decision support scheme is obtained according to the fusion results. The experimental results show that the design system is superior to the existing auxiliary decision-making system in motion recognition rate, motion result accuracy rate, and decision-making accuracy rate, which can provide users with auxiliary decision-making support for sports training and has good practical application effect.

Combined Expert and User-Driven Usability Assessment of Trauma Decision Support Systems Improves User-Centered Design

BackgroundTrauma clinical decision support systems improve adherence with evidence-based practice but suffer from poor usability and the lack of a user-centered design. The objective of this study was to compare the effectiveness of user and expert-driven usability testing methods to detect usability issues in a rib fracture clinical decision support system and identify guiding principles for trauma clinical decision support systems. MethodsA user-driven and expert-driven usability investigation was conducted using a clinical decision support system developed for patients with rib fractures. The user-driven usability evaluation was as follows: 10 clinicians were selected for simulation-based usability testing using snowball sampling, and each clinician completed 3 simulations using a video-conferencing platform. End-users participated in a novel team-based approach that simulated realistic clinical workflows. The expert-driven heuristic evaluation was as follows: 2 usability experts conducted a heuristic evaluation of the clinical decision support system using 10 common usability heuristics. Usability issues were identified, cataloged, and ranked for severity using a 4-level ordinal scale. Thematic analysis was utilized to categorize the identified usability issues. ResultsSeventy-nine usability issues were identified; 63% were identified by experts and 48% by end-users. Notably, 58% of severe usability issues were identified by experts alone. Only 11% of issues were identified by both methods. Five themes were identified that could guide the design of clinical decision support systems—transparency, functionality and integration into workflow, automated and noninterruptive, flexibility, and layout and appearance. Themes were preferentially identified by different methods. ConclusionWe found that a dual-method usability evaluation involving usability experts and end-users drastically improved detection of usability issues over single-method alone. We identified 5 themes to guide trauma clinical decision support system design. Performing usability testing via a remote video-conferencing platform facilitated multi-site involvement despite a global pandemic.

Decision Support Tool for the Agri-Food Sector Using Data Annotated by Ontology and Bayesian Network

The scientific literature is a valuable source of information for developing predictive models to design decision support systems. However, scientific data are heterogeneously structured expressed using different vocabularies. This study developed a generic workflow that combines ontology, databases and computer calculation tools based on the theory of belief functions and Bayesian networks. The ontology paradigm is used to help integrate data from heterogeneous sources. Bayesian network is estimated using the integrated data taking into account their reliability. The proposed method is unique in the sense that it proposes an annotation and reasoning tool dedicated to systematic analysis of the literature, which takes into account expert knowledge of the domain at several levels: ontology definition, reliability criteria and dependence relations between variables in the BN. The workflow is assessed successfully by applying it to a complex food engineering process: skimmed milk microfiltration. It represents an original contribution to the state of the art in this application domain.

Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging

In this article, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an electrical vehicle (EV) charging facility for human-driven connected vehicles (CVs) and AVs. The charging request by the human-driven EV becomes irrational when it demands more energy and charging period than its actual need. Therefore, the scheduling policy of each EVSE must be adaptively accumulated the irrational charging request to satisfy the charging demand of both CVs and autonomous vehicles (AVs). To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO. Thus, we devise a rational reward maximization problem to adapt the irrational behavior by CVs in a data-informed manner. We propose a novel risk adversarial multiagent learning system (RAMALS) for CAV-CI to solve the formulated RDSS problem. In RAMALS, the DSO acts as a centralized risk adversarial agent (RAA) for informing the laxity risk to each EVSE. Subsequently, each EVSE plays the role of a self-learner agent to adaptively schedule its own EV sessions by coping advice from RAA. The experiment results show that the proposed RAMALS affords around 46.6% improvement in charging rate, about 28.6% improvement in the EVSE’s active charging time, and at least 33.3% more energy utilization, as compared to a currently deployed ACN EVSE system, and other baselines.

AHP-SAW and AHP-WP Sensitivity Test in DSS Design for Housing Location Selection

The Decision Support System (DSS) is implemented by CV. IKS assists the director as a Decision Maker in determining the location of housing developments. In the case of determining the location of the subsidized housing project that was carried out in 2020, the DSS developed with two methods, namely DSS1 with the AHP – WP method and DSS2 with the AHP – SAW method, gives different outputs. The purpose of this paper is to conduct a sensitivity test to determine the MADM method most suitable for completing the locating determination of subsidized housing projects in CV. IKS. The sensitivity test method was chosen because this method can detect every ranking change in each MADM method. The more sensitive the value obtained from the change in ranking suggests the MADM method is the best in displaying the order of the ranked alternatives. It can be seen from the sensitivity test results that the change value of the AHP-WP method is 3.178%, and the change value of the AHP-SAW method is -19.274%. It concluded that the AHP-WP method was considered to be the most relevant in addressing the problem of determining the location of housing developments in the CV. IKS.Keywords: sensitivity test, MADM, AHP-SAW method, AHP-WP method, housing location determination

Ageing Design of Urban Park Landscape Based on Computer Virtual Simulation Technology.

Greenery are the parks in the urban areas and it becomes progressively most significant as cities grow more crowded. In the end, urban parks contribute to the population's healthiness and welfare by providing opportunities intended for physical and social activity, leisure, and relaxation. In order to construct “Digital Land,” computer and system software and hardware were used. Computer simulations are used to examine the value of a city garden's landscape design. Digital models and multimedia performances are being built using computer-aided design (CAD), this underlines the need for digitizing data for landscape design. A region's landscapes can only be accurately assessed if sufficient information is available on the elements that influence the people's awareness of landscape quality, as well as the kind, method, and effective rate of each of them. We use an LVQANN (artificial neural network) to forecast the landscape aesthetic assessment of urban parks and priorities the model's significant factors in this research. User viewpoint and artificial neural network modelling were utilized in conjunction to assess the aesthetic quality of the urban park's environment. This was done for two reasons. The design of urban parks decision support system is known as MATLAB software's multilayer perceptions model, which gives the ability to anticipate landscape visual significance in innovative parks. In this study, the ANN LVQ model is used to execute an ageing design of an urban park landscape based on a computer virtual simulation application. An example land is selected as input and area linked to sunny spot, top view, and so on is fixed. The ANN tool box is used to develop this application in MATLAB 2018b software. The following approach is used to create the ideal urban park landscape model. An accuracy of 89.23 percent, a sensitivity of 87.34 percent, and a recall of 78.93 percent were achieved, outperforming the approach and competing with the current model.

Developing a prescriptive decision support system for shop floor control

PurposeThe reported study aims at connecting the two crucial aspects of manufacturing of future, i.e. advanced analytics and digital simulation, with an objective to facilitate real-time control of manufacturing operations. The work puts forward a framework for designing prescriptive decision support system for a multi-machine manufacturing environment.Design/methodology/approachThe schema of the decision support system design begins with the development of a simulation model for a manufacturing shop floor. The developed model facilitates prediction followed by prescription. As a connecting link between prediction and prescription mechanism, heuristics for intervention have been proposed. Sequential design and simulation-based demonstration of activities that span from development of a multi-machine shop floor model; a prediction mechanism and a scheme of intervention that ultimately leads to prescription generation are the highlights of the current work.FindingsThe study reveals that the effect of intervention on the observed predictors varies from one another. For a machine under observation, subject to same intervention scheme, while two of the predictive measures namely penalty and desirability stabilize after a certain point, a third measure, i.e. complexity, shows either an increase or decrease in percent change. The work objectively establishes that intervention plans have to be evaluated for every machine as well as for every environmental variable and emphasizes the need for dynamic evaluation and control mechanism.Originality/valueThe proposed prescriptive control mechanism has been demonstrated through a case of a high pressure die casting (HPDC) manufacturer.