Decision Support Framework Research Articles

A Machine Learning Based Decision Support Framework For Big Data Pipeline Modeling And Design

The data warehousing process requires an architectural revolution to settle big data challenges and address new data sources such as social networks, recommendation systems, smart cities, and the web to extract value from shared data. In this respect, the pipeline modeling community for the acquisition, storage, and processing of data for analysis purposes is enacting a wide range of technological solutions that present significant challenges and difficulties. More specifically, the choice of the most appropriate tool for the user's specific business needs and the interoperability between the different tools have become a primary challenges. From this perspective, we propose in this paper a new interactive framework based on machine learning techniques (ML) to assist experts in the process of modeling a customized pipeline for data warehousing. More precisely, we elaborate first (i) an analysis of the experts’ requirements and the characteristics of the data to be processed, then (ii) we propose the most appropriate architecture to their requirements from a multitude of specific architectures instantiated from a generic one, by using (iii) several ML methods to predict the most suitable tool for each phase and task within the architecture. Additionally, our framework is validated through two real-world use cases and user feedback.

Integrating maintenance and energy problems through a Digital Twin-based decision support framework under the guidance of Asset Management

In a world where more than one third of the global energy consumption is directed to the industry sector, manufacturing companies must overhaul their business processes to move towards the improvement of sustainable performance. This work aims to contribute to this direction by integrating maintenance and energy problems through a Digital Twin-based decision support framework under the guiding role of Asset Management. The framework is meant as guideline for manufacturing companies to manage the integrated decision-making process and the related information flows and functional blocks. Maintenance or energy policies should consider both maintenance and energy objectives, handling the complexity and knowledge required for such an integration. A focus is given to the maintenance policy setting at the current release of the framework. This latter is tested and validated by an initial proof in a laboratory setting. In the future, it should be evaluated in its benefits within an industrial case.

A decision support framework to evaluate the main factors affecting the selection of sustainable materials in construction projects

A decision support framework to evaluate the main factors affecting the selection of sustainable materials in construction projects

An Explainable Decision Support Framework for Differential Diagnosis between Mild COVID-19 and Other Similar Influenzas

An Explainable Decision Support Framework for Differential Diagnosis between Mild COVID-19 and Other Similar Influenzas

A decision-support framework for evaluating supplier performance in the trial production stage of new car model development: a hybrid multi-criteria decision-making approach

A decision-support framework for evaluating supplier performance in the trial production stage of new car model development: a hybrid multi-criteria decision-making approach

Optimizing Earthquake Response with Fermatean Probabilistic Hesitant Fuzzy Sets: A Decision Support Framework

Optimizing Earthquake Response with Fermatean Probabilistic Hesitant Fuzzy Sets: A Decision Support Framework

IDEAL LOCATION SELECTION FOR CONTACTLESS PARCEL PICK-UP POINTS

The post-Covid era has witnessed the adoption of various new habits in our daily lives, particularly in relation to the ubiquitous e-commerce platforms that have become essential for urban populations. The surge in e-commerce activities and the intensified volume in delivery of packages during the pandemic sparked innovative ideas. This study explores one such creative concept: parcel pick-up points. We conducted a pioneering research endeavor to determine the optimal locations for these pick-up points in Istanbul, Turkey. Our methodology employed a novel hybrid approach, combining the Spherical Fuzzy Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The evaluation of criteria importance was facilitated by a literature review and experienced high-level managers in the cargo industry, who determined the criteria weights using the Spherical Fuzzy AHP method. Subsequently, the TOPSIS method was employed to identify the most ideal locations, leading to the selection of Kadikoy, Umraniye, and Atasehir. This study provides valuable insights into the selection of the optimal locations for parcel pick-up points in Istanbul, Turkey, which can inform policymakers, e-commerce companies, and logistics stakeholders. The proposed hybrid approach demonstrates the integration of modern smart technologies with fuzzy decision-making techniques, offering a robust framework for decision support in the field of e-commerce logistics. Future research can further explore the implementation and effectiveness of these pick-up points to enhance the efficiency and convenience of last-mile deliveries in urban areas.

Enhancing Urban Development with Picture Fuzzy Sets: A Strategic Decision Support Framework

Enhancing Urban Development with Picture Fuzzy Sets: A Strategic Decision Support Framework

A Scoping Review of Urban Planning Decision Support Tools and Processes That Account for the Health, Environment, and Economic Benefits of Trees and Greenspace.

Compelling evidence shows that trees and greenspaces positively impact human well-being and the environment and offer economic benefits. Nevertheless, there exists a knowledge gap regarding the extent to which this evidence is efficiently incorporated into existing urban planning decision-making processes. This scoping review identified the extent to which urban planning decision-making frameworks, models, and tools consider the health, environmental, and economic benefits of trees and greenspace. Out of 28 reviewed studies, 11 (39%) reported on frameworks, models, and tools that take into account the health, environmental, and economic dimensions of trees and greenspace. Additionally, seven studies provided comprehensive coverage of at least one of the three key dimensions. However, none of the decision support frameworks, models, or tools comprehensively integrated all three dimensions, with only two tools (7%) scoring above 50% (five or more out of nine) in terms of comprehensiveness. This review highlights the urgent need to incorporate the true economic and monetary values of the health and environmental benefits of trees and greenspace to inform urban development decision making.

Integrated Agent-Based Simulation and Game Theory Decision Support Framework for Cash Flow and Payment Management in Construction Projects

Effective cash flow management has become crucial for projects and stakeholders given the wide payment-related problems and financial risks encountered in the construction industry worldwide. Previous studies mostly addressed cash flow and payments from the perspective of a specific stakeholder, resulting in an imbalanced cash flow management culture that is further intensified by the power asymmetry of the top-down payment decision-making process. This research proposes an adaptive decision support framework for evaluating and negotiating payment options in construction projects while incorporating the individual and collective financial roles of stakeholders. The framework is comprised of three modules for data acquisition, payment simulation, analysis, and negotiation, as well as decision support. It integrates agent-based simulation, data envelopment analysis, and game theory for a multi-level study of project performance while capturing the driving forces of stakeholders in payment negotiations. A case study project is used to demonstrate the framework implementation under varying payment conditions and interest rates. The results provide quantitative profiles of stakeholders to identify incurred charges, balanced payment conditions, and suitable compensation. Finally, the framework can be utilized by stakeholders and jurisdictions to move towards enhanced contractual arrangements that alleviate economic and financial risks with the informed collaboration of its entities.

Regional bioethanol supply chain optimization with the integration of GIS-MCDM method and quantile-based scenario analysis

This study presents a novel decision-support framework for the bioethanol supply chain network planning and management under uncertainties. Under the holistic framework, the most suitable sites for biorefineries are first screened out by adopting a GIS-based multi-criteria decision-making approach. Then, a mixed-integer linear programming model combined with quantile-based scenario analysis is developed to determine the strategic planning (i.e. locations and size of biorefineries) and tactical management (i.e. biomass purchasing, feedstock transportation, bioethanol production, and product delivery) under uncertainties. The model can effectively search for reliable solutions under uncertainties and achieve tradeoff solutions with the consideration of decision makers’ risk tolerance. The proposed framework is demonstrated through a case study in China. It is suggested to build seven biorefineries with a capacity of 100 million liters in Zhumadian city. Utilizing 41% of local agricultural residues could satisfy the bioethanol requirement in the transportation sector under the E20 policy. However, the estimated production cost of bioethanol in Zhumadian is very high, about 1.11 $/L, which makes it lose cost advantage in the fuel market. Thus, currently, effective subsidies, mandatory energy substitution policies, along other environmental regulatory measures are desired to promote the bioethanol industry development.

A decision support framework to evaluate the sustainability performance of urban road transportation.

This study proposes a decision support framework (DSF) based on two data envelopment analysis (DEA) models in order to evaluate the urban road transportation of countries for sustainable performance management during different years. The first model considers different years independently while the second model, which is a type of network model, takes into account all the years integrated. A multi-objective programming model under two types of uncertainties is then developed to solve the proposed DEA models based on a revised multi-choice goal programming (GP) approach. The efficiency scores are measured based on the data related to several major European countries and the factors including the level of freight and passenger transportation, level of greenhouse gas emissions, level of energy consumption, and road accidents which are addressed as the main evaluation factors. Eventually, the two proposed models are compared in terms of interpretation and final achievements. The results reveal that the efficiency scores of countries are different under deterministic/uncertain conditions and according to the structure of the evaluation model. Furthermore, efficiency changes are not necessarily the same as productivity changes. The high interpretability (up to 99.6%) of the models demonstrates the reliability of DSF for decision-making stakeholders in the transport sector. Furthermore, a set of managerial analyses is conducted based on different parameters of the performance evaluation measures for these countries including the productivity changes during the period under consideration, resilience of the countries, detection of the benchmark countries, ranking of different countries, and detection of the patterns for improving the transportation system.

Visioning channel evolution for river management: Toward a functional decision support framework

AbstractImprovements in simulating and communicating the evolutionary trajectory of river morphology in response to environmental forcing over multi‐decadal timeframes would foreshadow the development of “foresight competency” in river management, whereby resource managers could strategically plan toward the most preferred of several plausible futures. Of the six steps in foresight competency, visioning, which involves translating scientific forecasts into a format useable by resource managers via a user‐friendly and interactive decision support tool that supports transparent decision‐making, is the least well developed. The approach requires converting forecasting model outputs into metrics of channel evolution that highlight transitions either within or between channel morphology states. Here, seven process‐based state transition metrics are proposed covering channel planform, morphological stability, corridor belt width, floodplain connectivity, bank erosion rate, bedform habitat diversity, and ecohydraulic diversity. To aid decision support, the metrics are converted into graphical indicators that are intuitive for management use and assembled into several prototype dashboard‐style graphical user interfaces designed to facilitate interactivity. A proof‐of‐concept illustration is provided and priorities in development toward a fully operational decision support tool are discussed. Such developments are critical in ensuring the practical relevance of geomorphology.

“All about the value?” Decisional needs of breast reconstruction for breast cancer patients in the Chinese context: A mixed-methods study

ObjectiveTo explore breast cancer (BC) patients' participation in breast reconstruction (BR) decision-making and specific decisional needs, especially the manifestations and causes of decisional conflicts, in China. MethodsA mixed-methods study was conducted using triangulation of data from interviews and a questionnaire survey with health care professionals (HCPs) and BC patients with BR decision-making experience at 5 Beijing centers. The Ottawa Decision Support Framework guided (ODSF) the qualitative and quantitative data analyses. ResultsA total of 82.53% of Chinese BC patients would consider BR. Seven themes captured patients' BR decisional needs per the ODSF: inadequate support/resources (100%, 58.82%) and knowledge (75%, 52.94%) were most frequently cited. Health beliefs (unclear values) reflected Chinese characteristics. Patients had inadequate knowledge (M=19.99/50, SD=8.67) but positive BR attitudes (M=59.48/95, SD=10.45). ConclusionsBR decisions for Chinese BC patients are complex and often accompanied by decisional conflicts. Inadequate knowledge and inadequate support and resources contribute to these conflicts, emphasizing the need for culturally tailored information and support to promote SDM. Practice implicationsHCPs need specialized training in SDM to guide patients in decision-making. It is essential to provide relevant resources and support that are culturally and clinically appropriate for Chinese patients.

Decision support for personalized therapy in implantable medical devices: A digital twin approach

Implantable Medical Devices (IMDs) offer timely therapeutic interventions for life-threatening conditions without disrupting patients’ daily activities. Given the substantial variability in individual health conditions, customizing device therapies to suit specific patients and their evolving needs is crucial. Although current customization relies on modifying device parameter settings, no universally accepted clinical guidelines exist for this form of customization, particularly for patients with complex or rare conditions. To address this gap, we introduce a decision support framework that leverages expert system methodologies for personalized therapy in IMDs. This comprehensive expert system encompasses a dynamic digital twin of the patient, built using physiological data obtained from the IMD and additional wearable devices. The digital twin captures disease mechanisms and patient-specific physiological parameters, while a periodically trained Reinforcement Learning (RL) agent and specialized algorithms for the ongoing maintenance of the digital twin complete the expert system. Together, these elements facilitate periodic, informed therapy customization based on up-to-date patient conditions. Our approach includes novel algorithms for feature extraction, physiological modeling, and the maintenance of the digital twin. We substantiate the efficacy and safety of our digital-twin-based decision support framework through a case study focused on Implantable Cardioverter Defibrillators (ICDs). Experimental results on virtual patients confirm that our integrated framework surpasses traditional customization strategies in both therapeutic effectiveness and patient safety considerations.

Optimizing retailer prepayment installment in inventory systems with link-to-order discounts and power demand patterns of price and time: An effective decision support framework

Installment in the prepayment mechanism has emerged as a significant decision for many companies due to rising capital costs and increasing pressures from suppliers for various products. To motivate the companies about the prepayment mechanism, suppliers allow the frequency of installment of advance payment as a decision variable for the company and the per unit acquisition cost as a step-down function in exchange for an order-based prepayment regulation allowing the stepwise decreasing prepayment portion and duration as well. This paper investigates the optimal joint decision-making of a company’s prepayment, pricing, and stocking policies to maximize profits in the context of an order-based prepayment and discount regulation. A realistic approach is adopted in the study by considering the clients’ demand as a multiplicatively separable power function that accounts for both the price and the storage time of products. Theoretical expressions are derived to check when multiple or single installments are more profitable for the company. To achieve the maximum profit, a solution approach is introduced based on all the derived theoretical outcomes. The results of three numerical studies are documented and then the effects of order-based prepayment and discount regulation on optimal policies and maximum profit are further studied. The results of the study indicate that the profitability of the company can be enhanced through an increase in the frequency of installments, particularly in situations where the rate of interest charged is high. Finally, to draw up effective guidelines, a sensitivity investigation is conducted for all cost parameters.

Abstract A059: The role of conversational chatbots in enhancing shared decision making for African American men with early stage prostate cancer

Abstract Introduction : Enhancing shared decision making (SDM) for African American (AA) men with early stage prostate cancer using conversational chatbots (CCs) aims to improve communication, provide information, and empower them in their treatment decision making process. SDM is a collaborative approach that involves patients and healthcare providers working together based on medical evidence and patient preferences. However, AA men face significant disparities in prostate cancer (PCa) outcomes, including higher incidence and mortality rates compared to other racial/ethnic groups in the United States. Limited access to recommended PCa information and inadequate information during SDM conversations contribute to the challenges AA men face when making treatment decisions. The purpose of this study was to develop a conversational chatbot (CC) to provide PCa information in real time thus, enhancing SDM for AA men with PCa. Methods: The Ottawa Decision Support Framework and the International Patient Decision Aids Standards provided guidance and framework for the development of the CC. Our participatory research approach involved a diverse Community-Based Participatory Research Advisory Board (CBPRAB) consisting of twelve members. The CBPRAB included AA PCa survivors, AA men, caregivers, advocates, community leaders, and cancer care providers. Through training in research methods and focus group facilitation, CBPRAB members actively contributed their valuable insights, ensuring a collaborative and inclusive research process. Results: Five stakeholder focus groups (n = 44) comprising AA men, PCa survivors, spouses, healthcare professionals, and advocates provided valuable input for the development of the CC. In total, 819 questions were generated through interviews conducted during the focus groups. These questions were either inferred from the discussions or explicitly provided by the participants. The answers to these questions were developed from evidence based sources, such as the American Cancer Society and National Comprehensive Cancer Network Guidelines. An Android-based smartphone application that allows users to submit questions via speech was created. The application matches the user's question within the data set of 819 questions and provides answers in both voice and text formats. Conclusion : The Android-based smartphone application was alpha tested on four stakeholder focus groups (n=35). The CC developed in the smartphone application demonstrated the capacity to provide real time responses to user questions sourced from an 819-question database derived from our stakeholder focus groups. By offering evidence based information and directing users to additional resources if the question is not in the database, the CC can improve accessibility to medical information, thus enhancing the SDM process regarding early stage prostate cancer. Citation Format: Angela D. Adams. The role of conversational chatbots in enhancing shared decision making for African American men with early stage prostate cancer [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A059.

A Qualitative Exploration of Stakeholders' Preferences for Early-Stage Rectal Cancer Treatment.

As treatment options for patients with rectal cancer evolve, patients with early-stage rectal cancer may have a treatment choice between surgery and a trial of nonoperative management. Patients must consider the treatments' clinical tradeoffs alongside their personal goals and preferences. Shared decision-making (SDM) between patients and clinicians can improve decision quality when patients are faced with preference-sensitive care options. We interviewed 28 stakeholders (13 clinicians and 15 patients) to understand their perspectives on early-stage rectal cancer treatment decision-making. Clinicians included surgeons, medical oncologists, and radiation oncologists who treat rectal cancer. Adult patients included those diagnosed with early-stage rectal cancer in the past 5 years, recruited from an institutional database. A semi-structured interview guide was developed based on a well-established decision support framework and reviewed by the research team and stakeholders. Interviews were conducted between January 2022 and January 2023. Transcripts were coded by 2 raters and analyzed using thematic analysis. Both clinicians and patients recognized the importance of SDM to support high-quality decisions about the treatment of early-stage rectal cancer. Barriers to SDM included variable clinician motivation due to lack of training or perception of patients' desires or abilities to engage, as well as time-constrained encounters. A decision aid could help facilitate SDM for early-stage rectal cancer by providing standardized, evidence-based information about treatment options that align with clinicians' and patients' decision needs.

Multi-criteria decision support framework for climate change-sensitive thermal comfort evaluation in European buildings

This paper proposes a novel multi-criteria decision support framework for climate change-sensitive evaluation of building thermal comfort performance in European buildings. The proposed framework considers various comfort categories based on building use and condition, comfort models based on building operation, sensitivity to climate change, key performance indicators, and their thresholds. The proposed framework is then implemented using a passive house-certified high-performance timber dwelling near Brussels as a case study. The thermal comfort in the free-running reference timber dwelling is assessed using a whole-building energy performance simulation model with a static threshold of 26°C for the bedrooms and an adaptive threshold for the other occupied zones. The analysis found an increase in indoor overheating degree by 1.7°C and ambient warmness degree by 6.5°C during heat waves from the current scenario to the end of the century scenario. However, the reference timber dwelling effectively suppressed the impacts of climate change with varying degrees of success towards the end of the century. The proposed framework is intended to support decision-makers in effectively evaluating performance during the early stages of building design. The study highlights the need for further research on building performance assessment techniques and guidelines in a changing climate.

Disease-Modifying Therapies for Sickle Cell Disease: Decisional Needs and Supports Among Adolescents and Young Adults

Introduction: Adolescents and young adults (AYAs) with sickle cell disease (SCD) often experience significant challenges in disease management. Hydroxyurea and several newer disease-modifying therapies have demonstrated health benefits; however, uptake of these therapies remains suboptimal. Shared decision-making is one promising solution to addressing barriers in uptake and to improve health outcomes for AYAs with SCD. A thorough understanding of AYA's decisional needs can guide the development of tailored decisional supports and promote shared decision-making. Informed by the Ottawa Decision Support Framework, this study aimed to examine decisional needs and supports reported by AYAs with SCD, their caregivers, and SCD healthcare providers. Methods: Semi-structured qualitative interviews were conducted with AYAs ages 15-25 years with SCD ( n=14) and caregivers of AYAs with SCD ( n=11) receiving care at two hospitals in the mid-Atlantic region of the US. Qualitative data were also obtained through online crowdsourcing with SCD providers across the US ( n=40). Most AYAs (93%) identified as non-Hispanic Black ( M age=21 years, 57% male) and had SS genotype (79%). Most caregivers were female (80%) and all identified as non-Hispanic Black (100%). About two-thirds of healthcare providers were female (65%) and non-Hispanic White (65.0%), with an average of 14.8 years in practice ( range=2-57 years). Thematic and descriptive content analyses were used to summarize perspectives on decisional needs and supports regarding disease-modifying therapies. Adequate inter-rater reliability (Cohen's Kappas > .80) and thematic saturation were achieved. Results: AYAs and caregivers reported needing six areas of support when deciding on disease-modifying therapies: resolving decisional conflicts, gaining knowledge, clarifying expectations, garnering supports and resources, navigating complexities in the decision, and addressing other personal concerns. Families reported receiving supports from providers that facilitated their decision-making by helping them: gain knowledge, clarify expectations, address other personal concerns, build rapport with providers, and resolve unreceptiveness or ambivalence to making a decision. Providers reported offering additional supports targeting: inadequate experience in implementing therapies (e.g., pill swallowing), inadequate health and social services and financial assistance, and inadequate instrumental support (e.g., transportation). Conclusions: This research highlights the needs AYAs and caregivers identify when making decisions about disease-modifying therapies for SCD and the decision supports offered by healthcare providers. Decisional needs reported by families generally corresponded to supports offered by providers. Providers also highlighted additional supports that may be helpful for families (e.g., instrumental supports, such as financial, emotional, and skillset assistance). Findings will inform the development of a shared decision-making intervention for AYAs with SCD, their caregivers, and healthcare providers. Future research is needed to examine which decision supports may be most impactful to promote shared decision-making for disease-modifying therapies.