Decision Support Approach Research Articles

Enhancing smart city assessment: an advanced MCDM approach for urban performance evaluation

Urban centers in the 21st century face significant challenges impacting future urban living. The numerous indicators and inherent complexity involved in assessing urban performance in smart cities necessitate the application of multi-criteria evaluation methods, which are frequently associated with fuzziness and uncertainties. Thus, this study introduces an innovative decision support approach that integrates complex spherical fuzzy set to enhance decision-making for smart city evaluation. By incorporating a consensus measurement mechanism for judgments, the reliability of results is improved. The methodology assesses smart city performance by calculating a level index using criteria of resilience, sustainability, and urban livability, along with eighteen sub-criteria for comprehensive benchmarking. Results highlight pollution control, energy and environmental resources, community well-being, eco-friendly transport, and social cohesion as critical factors for smart city performance. Singapore demonstrates high-level performance due to its integration of advanced technologies and community-focused strategies. The evaluated results obtained via the developed approach offer valuable insights for urban planners aiming to promote sustainable, resilient, and livable urban development.

A two-stage approach for modeling, performance evaluation, and decision support of disassembly processes

ABSTRACT This paper tackles an issue of disassembly lot-sizing within the framework of stochastic batch demand for multi-products featuring a two-level structure. The suggested methodology relies on a batch deterministic and stochastic Petri net as a modeling tool to address such problems in the context of stochastic processes. The primary objective is to determine the optimal sequencing of disassembly for each product type to meet stochastic batch demands of root items across multiple criteria. A novel mathematical model is introduced to minimize the cumulative costs associated with disassembly operations and extra costs, as well as holding costs for diverse products. In a subsequent stage, a decision support tool, namely the preference ranking organization method for enrichment evaluation method, is put forward for prioritizing different scenarios. For assessing the efficiency of the suggested approach, a real case study from the manufacturing industry is simulated. The simulation results prove the suggested approach’s capability to adapt to dynamic demands for leaf items throughout the planning horizon.

Digital twin-driven safety management and decision support approach for port operations and logistics

Ports are of great significance in processing cargo containers and facilitating global marine logistics. Nevertheless, the susceptibility of the container shipping network for hazardous cargo is likely to intensify in the event of a significant disruption at a major port, such as adverse weather conditions, inadequate management practices, or unforeseen catastrophes. Such situations require the deployment of port protection emergency response and prevention in advance. This study proposes a digital twin (DT) model that employs extensive and trajectory data within containers to comprehensively analyze the occurrence of hazardous cargo failures within the port storage process. The virtual models of physical entities in the port are created through a data-driven approach, and the behavior of these entities in a port environment with big data is then simulated. A combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) algorithm is employed to provide predictions for the service layer of the DT system. The predicted correlation coefficients of temperature and humidity in the container reach 0.9855 and 0.9181, respectively. The developed system driven by DT models integrated with a CNN and the LSTM algorithm can more effectively assist the safety manager in achieving prevention in port operations. This study enables marine authorities and decision-makers to optimize emergency procedures, thereby reducing the probability of accidents in port operations and logistics.

Cognitive activity analysis of Parkinson's patients using artificial intelligence techniques.

The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models. Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-support approach that doctors can benefit from. A dataset was created by asking 40 PD and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying drawings of individuals using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out (LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitting scenarios. To further improve the performances of classifiers, Principal Component Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly. The highest accuracy among machine learning based classifiers was obtained as 90% with SVM classifier using non-template drawings with PCA application. The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimizes environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals. We plan to work on more comprehensive data in the future.

Edge-platforms based decision-support approach for solar panels inspection with YOLOv8 deep neural-network

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>This paper presents an innovative AI-based method for autonomous inspection, designed to enhance energy production efficiency by optimizing cleaning strategies for soiled photovoltaic panels, using advanced artificial intelligence algorithms to analyze panel conditions and environmental factors in real-time, allowing for targeted cleaning interventions. Based on the advanced YOLOv8 deep learning algorithm and computer vision approach, the proposed method offers distinct advantages in real-time detection and classification of various types of soiling and dust accumulation compared on solar panels to traditional methods, and underwent satisfactory testing across diverse scenarios. The NVIDIA Jetson Nano, the Raspberry Pi4 embedded devices, and the Raspberry Pi4 combined with NCS2 accelerator are used for implementing our approach. A comparison aims to provide a detailed exploration of the most suitable embedded platform for deploying our advanced system was discussed. This comparison considers processing speed and accuracy, energy consumption, and overall performance in executing the computationally intensive tasks. The results demonstrate that our model achieves high accuracy in detecting soiling and enhancing the model's detection speed. With an average precision of 99.5%, this approach ensures accurate fault identification, underscoring the effectiveness of computer vision using deep learning algorithms for detection tasks across a wide range of scenarios.</p></div></div></div>

Pre- and post-surgery: advancements in artificial intelligence and machine learning models for enhancing patient management in infective endocarditis.

Infective endocarditis (IE) is a severe infection of the inner lining of the heart, known as the endocardium. It is characterized by a range of symptoms and has a complicated pattern of occurrence, leading to a significant number of deaths. IE poses significant diagnostic and treatment difficulties. This evaluation examines the utilization of artificial intelligence (AI) and machine learning (ML) models in addressing information extraction (IE) management. It focuses on the most recent advancements and possible applications. Through this paper, we observe that AI/ML can significantly enhance and outperform traditional diagnostic methods leading to more accurate risk stratification, personalized therapies as well and real-time monitoring facilities. For example, early postsurgical mortality prediction models like SYSUPMIE achieved 'very good' area under the curve (AUROC) values exceeding 0.81. Additionally, AI/ML has improved diagnostic accuracy for prosthetic valve endocarditis, with PET-ML models increasing sensitivity from 59% to 72% when integrated into ESC criteria and reaching a high specificity of 83%. Furthermore, inflammatory biomarkers such as IL-15 and CCL4 have been identified as predictive markers, showing 91% accuracy in forecasting mortality, and identifying high-risk patients with specific CRP, IL-15, and CCL4 levels. Even simpler ML models, like Naïve Bayes, demonstrated an excellent accuracy of 92.30% in death rate prediction following valvular surgery for IE patients. Furthermore, this review provides a vital assessment of the advantages and disadvantages of such AI/ML models, such as better-quality decision support approaches like adaptive response systems on one hand, and data privacy threats or ethical concerns on the other hand. In conclusion, Al and ML must continue, through multi-centric and validated research, to advance cardiovascular medicine, and overcome implementation challenges to boost patient outcomes and healthcare delivery.

Efficient Workflow Analysis to Address Paper Persistence in Tuberculin Testing.

Despite widespread adoption and maturity, paper persistence endures in many Electronic Health Record (EHR) systems, particularly for complex workflows involving multiple steps from different stakeholders separated in time. In our health system, Latent Tuberculosis Infection (LTBI) testing was one such workflow where a Tuberculin Skin Test (TST) must be administered and then correctly read 48-72 hours later and documented. This paper discusses a low-resource workflow analysis and clinical decision support approach to replace a paper workflow and garner the benefits of the EHR for clearer documentation and retrieval of LTBI results. Our approach resulted in a significant increase in completed TST documentation, 57% (24/42) to 95% (18/19), P < 0.003. Human-centered design practices such as work system analysis and formative usability testing are feasible with limited resources and improve the likelihood of success of electronic workflows by designing solutions that fit existing clinical workflows and automating processes wherever possible.

Understanding the Dairy Sector in Slovenia: A Modeling Approach for Policy Evaluation and Decision Support

This study investigates the dairy sector in Slovenia, focusing on farm heterogeneity, efficiency in resource utilization, and policy implementations. Through a modeling approach, we explore the differences among dairy farms, considering factors such as herd size, farm management, natural conditions, and production potential. Based on 32 typical dairy farms, representing the entire dairy sector, composed of 6400 dairy farms, the analysis was performed using the farm model (SiTFarm). We emphasize the importance of accurate assessments, given the variability of policy impacts across farm types. While medium-to-large, specialized farms dominate milk production, smaller farms, particularly in less favored areas, hold social and environmental importance despite facing competitive challenges. Addressing environmental sustainability could involve promoting practices that improve milk yield and include grazing, as this tends to lower greenhouse gas emissions per kilogram of milk (−5%). Dairy farms contribute about one-third of the generated revenue in Slovene agriculture, of which a good half goes to farms located in less favored areas. They manage a good quarter of permanent grassland in Slovenia, and it is certainly the sector that can achieve the highest return on these areas. In 75% of the farms, the gross margin is higher than 1756 EUR/ha and using best practices they exceed 3400 EUR/ha. The model results indicate that the average hourly rate on dairy farms during the observed period falls within the range of EUR 7.3 to 17.4 of gross margin for most farms, with the top-performing ones exceeding 24 EUR/h. However, due to the significant reliance on budgetary payments (on average, 58% of the gross margin), the implementation of the common agricultural policy strategic plan generally leads to a deterioration in the economic indicators of dairy farms. This impact is particularly pronounced on medium-sized and larger farms, increasing the effect on income due to the substantial fixed costs involved. Our findings underscore the interplay between policy interventions, farm characteristics, and sectoral outcomes, offering valuable insights for policy-makers and stakeholders.

Leafiness-LiDAR index and NDVI for identification of temporal patterns in super-intensive almond orchards as response to different management strategies

The use of super-intensive orchards is a growing trend in fruit production. The present study aims to improve management of these cropping systems by focusing on how agronomic decisions impact orchard dynamics in the short to medium term and by providing a decision-support approach based on stable temporal patterns from previous seasons. A multitemporal study using remote sensing and LiDAR was conducted in a commercial almond orchard over four growing seasons (2019–2022) to determine the optimal timing of image acquisition for variable pre-harvest treatments. A model-based clustering (mclust) was applied to optimal Sentinel-2 NDVI maps and apparent soil electrical conductivity (ECa) data, interpolated to the pixel centroids of Sentinel-2 image grids, to delineate potential management zones (PMZs). The leafiness-LiDAR index (LLI), a leaf area index (LAI) estimator, was obtained as ground truth after summer pruning and before harvesting, showing a significant influence of fertigation and pruning on the LAI, with summer pruning particularly influencing orchard dynamics. The optimal time for NDVI mapping was found to be two months after summer pruning in productive years and two weeks after in unproductive years. The delineated PMZs were consistent across seasons and corresponded to significant LAI differences. This method could contribute to improving resource management and sustainability in super-intensive commercial orchards.

Provision and evaluation of explanations within an automated planning-based approach to solving the multimorbidity problem

The multimorbidity problem involves the identification and mitigation of adverse interactions that occur when multiple computer interpretable guidelines are applied concurrently to develop a treatment plan for a patient diagnosed with multiple diseases. Solving this problem requires decision support approaches which are difficult to comprehend for physicians. As such, the rationale for treatment plans generated by these approaches needs to be provided. Objective:To develop an explainability component for an automated planning-based approach to the multimorbidity problem, and to assess the fidelity and interpretability of generated explanations using a clinical case study. Methods:The explainability component leverages the task-network model for representing computer interpretable guidelines. It generates post-hoc explanations composed of three aspects that answer why specific clinical actions are in a treatment plan, why specific revisions were applied, and how factors like medication cost, patient’s adherence, etc. influence the selection of specific actions. The explainability component is implemented as part of MitPlan, where we revised our planning-based approach to support explainability. We developed an evaluation instrument based on the system causability scale and other vetted surveys to evaluate the fidelity and interpretability of its explanations using a two dimensional comparison study design. Results:The explainability component was implemented for MitPlan and tested in the context of a clinical case study. The fidelity and interpretability of the generated explanations were assessed using a physician-focused evaluation study involving 21 participants from two different specialties and two levels of experience. Results show that explanations provided by the explainability component in MitPlan are of acceptable fidelity and interpretability, and that the clinical justification of the actions in a treatment plan is important to physicians. Conclusion:We created an explainability component that enriches an automated planning-based approach to solving the multimorbidity problem with meaningful explanations for actions in a treatment plan. This component relies on the task-network model to represent computer interpretable guidelines and as such can be ported to other approaches that also use the task-network model representation. Our evaluation study demonstrated that explanations that support a physician’s understanding of the clinical reasons for the actions in a treatment plan are useful and important.

New results on the InterCriteria Analysis

The Intercriteria Analysis (ICA) is an decision support approach based on the apparatus of intuitionistic fuzzy sets and the relations between the evaluations of a set of given objects evaluated against a set of fixed criteria. In the present paper we discuss three new procedures for evaluation of ICA when used over objects containing elements of uncertainty.

10 years of decision‐making for biodiversity conservation actions: A systematic literature review

AbstractDecision science emphasizes necessary elements required for robust decision‐making. By incorporating decision science principles, frameworks, and tools, it has been demonstrated that decision‐makers can increase the chances of achieving conservation aims. Setting measurable objectives, clearly documenting assumptions about the impact of available actions on a specific threat or problem, explicitly considering constraints, exploring and characterizing uncertainty, and structured deliberation on trade‐offs have been identified as key elements of successful decision‐making. We quantify the extent to which these five elements were utilized in published examples of decision making in conservation in both academic and conservation practice between 2009 and 2018. We found that less than 50% of identified examples included all five elements, with differences in the degree of decision science applied across five commonly used decision support approaches: adaptive management (AM), systematic conservation planning (SCP), structured decision making (SDM), multi‐criteria decision analysis, and cost‐effectiveness analysis. Example applications that utilized the SDM framework were limited in numbers but used on average more than 50% of the five key elements we considered. Although SCP and AM constituted the majority of examples, they were more prevalent in academic studies rather than management applications. SCP and AM examples were widespread in protected area planning, threat abatement, and restoration. Strong geographic bias exists in documented conservation activities that deploy all five decision science elements.

UK net-zero policy design – from optimisation to robustness

The need to deal with the deep uncertainty and system complexity associated to Net-Zero pathways, especially those relying on emergent greenhouse gas removal (GGR) technologies, has resulted in a growing body of literature on alternative decision-support approaches. Exploratory modelling, and specifically Robust Decision Making (RDM), are potential approaches capable of addressing these challenges: by exploring a wide range of conceivable futures, they explicitly embrace deep uncertainties while seeking to reduce system vulnerabilities. However, though RDM methods have been well documented, there is little insight as to how such approach might be integrated into Net-Zero policy design processes. By means of a workshop (n=17) and interviews (n=13) with the UK climate policy and energy modelling communities, this contribution provides insights into the role and potential of RDM in explicitly dealing with the deep uncertainties that pervade in the establishment of a 60–100 MtCO2 UK GGR sector within three decades. The consultation process revealed that there is an appetite from the decision-making and analytical communities in integrating exploratory modelling concepts into UK policy design processes. It is recommended that to bridge the gap between theoretical RDM constructs and their broader adoption, the analytical process should include a broader set of disciplines and expertise. Specifically for the modelling community, this work suggests that in-use computational models should be adapted, rather than new tools developed. Key challenges also arise from the time and resources required, suggesting small scale place-based pilots could promote the acceptability and foster the adoption of the RDM methodology.

Improving search and rescue planning and resource allocation through case-based and concept-based retrieval

The need for effective and efficient search and rescue operations is more important than ever as the frequency and severity of disasters increase due to the escalating effects of climate change. Recognizing the value of personal knowledge and past experiences of experts, in this paper, we present findings of an investigation of how past knowledge and experts’ experiences can be effectively integrated with current search and rescue practices to improve rescue planning and resource allocation. A special focus is on investigating and demonstrating the potential associated with integrating knowledge graphs and case-based reasoning as a viable approach for search and rescue decision support. As part of our investigation, we have implemented a demonstrator system using a Norwegian search and rescue dataset and case-based and concept-based similarity retrieval. The main contribution of the paper is insight into how case-based and concept-based retrieval services can be designed to improve the effectiveness of search and rescue planning. To evaluate the validity of ranked cases in terms of how they align with the existing knowledge and insights of search and rescue experts, we use evaluation measures such as precision and recall. In our evaluation, we observed that attributes, such as the rescue operation type, have high precision, while the precision associated with the objects involved is relatively low. Central findings from our evaluation process are that knowledge-based creation, as well as case- and concept-based similarity retrieval services, can be beneficial in optimizing search and rescue planning time and allocating appropriate resources according to search and rescue incident descriptions.

A simulation-based approach for decision-support in healthcare processes

This article explores the application of simulation-driven decision support systems in the context of healthcare processes. The findings obtained from the review of related research suggest that simulation models have been widely used in the healthcare sector as valuable tools to aid decision-making aimed at the improvement of healthcare process management. However, the analyzed works lack sufficient evidence regarding the utilization of systematic approaches in the development of their proposals. We have conducted a research effort in this field, resulting in the design of a conceptual framework that guides in the systematic development of decision support systems centered around simulation models. The framework includes a specific methodology for developing simulation models, with simulation modeling experimentation serving as a valuable technique for assessing the impact of potential changes on the performance of the process. To illustrate the applicability and practical value of the proposed framework, we have developed an application case within the context of the Urgent Healthcare for Women's (UHW) process in a public hospital in Andalusia, Spain.

Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET.

The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET. We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients. The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%). Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.

From Data to Action: A Graph-Based Approach for Decision Support in Civil Protection Operations Planning

In the face of increasing frequency and severity of crises such as natural disasters, pandemics, and geopolitical conflicts, civil protection organizations are crucial for recovery and support for affected populations. However, the efficiency and effectiveness of these organizations in crisis response are often hindered by the manual generation of operation plans, characterized by cognitive overload and limited analytic overview. Existing systems focus on detecting crises or post-crisis analysis, overlooking proactive planning. We present GRETA, a graph-based operational planning approach utilizing semantic historical data for better decision support. GRETA uses Operational Scenario Patterns to model operations, mapping them onto a knowledge graph in JSON-LD format, thus creating a structured representation of past data to improve future crisis response planning. We tested GRETA with Germany's Federal Agency for Technical Relief, analyzing over 157,450 historic operations from 2012-2022. Results show GRETA enhances plan efficiency, accuracy, and comprehensiveness, aiding especially inexperienced planners.

Model-driven decision support: A community-based meta-implementation strategy to predict population impact

PurposeStandard tools for public health decision making such as data dashboards, trial repositories, and intervention briefs may be necessary but insufficient for guiding community leaders in optimizing local public health strategy. Predictive modeling decision support tools may be the missing link that allows community level decision makers to confidently direct funding and other resources to interventions and implementation strategies that will improve upon the status quo. MethodsWe describe a community-based model-driven decision support (MDDS) approach that requires community engagement, local data, and predictive modeling tools (agent-based modeling in our case studies) to improve decision-making on implementing strategies to address complex public health problems such as overdose deaths. We refer to our approach as a meta-implementation strategy as it provides guidance to a community on what intervention combinations and their required implementation strategies are needed to achieve desired outcomes. We use standard implementation measures including the Stages of Implementation Completion to assess adoption of this meta-implementation approach. ResultsUsing two case studies, we illustrate how MDDS can be used to support decision making related to HIV prevention and reductions in overdose deaths at the city and county level. Even when community acceptance seems high, data acquisition and diffuse responsibility for implementing specific strategies recommended by modeling are barriers to adoption. ConclusionsMDDS has the capacity to improve community decision makers use of scientific knowledge by providing projections of the impact of intervention strategies under various scenarios. Further research is necessary to assess its effectiveness and the best strategies to implement it.

A Two-Stage Sustainable Supplier Selection Model Considering Disruption Risk

The global spread of the pandemic has changed many aspects of life and placed the supply chain at risk of disruption. To solve the problem of supplier selection under the risk of supply chain disruption, in this paper, we propose a two-stage evaluation model to address the issue of supplier selection in the context where a pandemic requires a lockdown. First, we incorporate the lead time into the epidemic model that predicts the evolution of the pandemic to identify suppliers that have a high risk of disruption caused by the pandemic’s evolution. Second, we propose a best–worst method combined with regret theory to rank candidate suppliers. Our model provides a dynamic link between the pandemic’s evolution and supplier selection, and it allows selecting suppliers according to various criteria while avoiding supply chain disruptions due to inappropriate supplier selection. We validate the proposed model on a real case study with epidemic data from China. This paper is the first to consider the impact of lockdowns during the pandemic on supplier selection. We develop a novel MCDM model BWM-RT for supplier selection; our model can be an effective decision support approach to help decision makers better cope with the risk of supply chain disruptions.

Enhancing sustainable supply chain readiness to adopt blockchain: A decision support approach for barriers analysis

Blockchain technology (BT) enhances the capacity to monitor products consistently, fostering supply chain responsiveness to a wide range of societal and environmental issues. Although BT is known as an innovative tool, there exist potential operational and organizational challenges affecting BT adoption. This study proposes a decision support approach to leverage risk management to analyze potential barriers associated with BT adoption in sustainable supply chains (SSCs). This approach is developed to model how the economic, social, and environmental-related barriers (e.g., energy consumption) and their corresponding risk factors are interrelated. To model the causal relationships (CRs) among the barriers identified through the literature review, the fuzzy cognitive map advanced by Z-number theory is embedded in the proposed approach. Then, a hybrid learning algorithm is employed to determine the criticality of the barriers. As the reliability of information affects the accuracy of decision-making, the Z-number theory applies uncertainty and reliability simultaneously in specifying the values of risk factors and the weights of the CRs. Taking advantage of the learning algorithm and Z-number theory, the findings show a reliable and unbiased ranking compared to the failure mode and effect analysis. This helps managers develop more efficient mitigation strategies to deal with critical barriers. The results of the study also imply that adoption costs, extra audits, and regulatory uncertainty are the critical barriers affecting SSC readiness.