Decision-making Model Research Articles

Markov models for clinical decision-making in radiation oncology: A systematic review.

The intrinsic stochasticity of patients' response to treatment is a major consideration for clinical decision-making in radiation therapy. Markov models are powerful tools to capture this stochasticity and render effective treatment decisions. This paper provides an overview of the Markov models for clinical decision analysis in radiation oncology. A comprehensive literature search was conducted within MEDLINE using PubMed, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only studies published from 2000 to 2023 were considered. Selected publications were summarized in two categories: (i) studies that compare two (or more) fixed treatment policies using Monte Carlo simulation and (ii) studies that seek an optimal treatment policy through Markov Decision Processes (MDPs). Relevant to the scope of this study, 61 publications were selected for detailed review. The majority of these publications (n = 56) focused on comparative analysis of two or more fixed treatment policies using Monte Carlo simulation. Classifications based on cancer site, utility measures and the type of sensitivity analysis are presented. Five publications considered MDPs with the aim of computing an optimal treatment policy; a detailed statement of the analysis and results is provided for each work. As an extension of Markov model-based simulation analysis, MDP offers a flexible framework to identify an optimal treatment policy among a possibly large set of treatment policies. However, the applications of MDPs to oncological decision-making have been understudied, and the full capacity of this framework to render complex optimal treatment decisions warrants further consideration.

Make-or-Buy Policy Decision in Maintenance Planning for Mobility: A Multi-Criteria Approach

The ongoing technical innovation is fully involving transportation sector, converting the usual mass-transit system toward a sustainable mobility. Make-or-buy decision are usually adopted to assess different solutions in terms of costs-benefits to put in place strategic choices regarding in-house production or from an external supplier. This can also be reflected on maintenance operations, thus replicating a similar approach to transport companies involved. This paper aims to present a decision-making model by means of a multi-criteria analysis to lead make-or-buy choices adapted to maintenance. A brief introduction into the actual mobility context is provided, evaluating global and national trends with respect to the mobility solutions offered. Then, a focus is set on maintenance approaches in mobility sector and the need of a make-or-buy decision process is considered. The decision-making path is developed through a multi-criteria framework based on eigenvector weighing assessment, where different Key Performance Indicators (KPIs) are identified and exploited to assess the maintenance approach at stake. Results allow to compare the solution offered by the different scenarios considered. In particular, for the case study of interest a −35% decrease in maintenance specific cost and −44% in cost variability were found. Reliability of the fleet was kept at an acceptable level compared to the reference in-house maintenance (≥90%) while an increase in the Mean Time Between Failure was observed. For the purposes of a small company, the method can address the choice of outsourcing maintenance as the best. Finally, a general trend is then extrapolated from the analysis performed, in order to constitute a decision guideline.

A Review of Recent Developments on Modeling Low Impact Development (LID) Technique

Urban areas are more susceptible to flooding and water body contamination due to the detrimental effects of urbanization. As a result, a sustainable urban drainage system, also known as low impact development (LID) technique, is required. Although this technique can be extensively applied, the planning and design processes are multi-dimensional, multi-variable, and site-specific, which must consider various local conditions and factors. Consequently, these processes can be very complicated and time-consuming for professionals, necessitating support from computer modeling. This study intends to thoroughly explore the idea of LID modeling, various available computer models, and other tools for its optimization and decision-making processes. The most recent trustworthy journal publications that addressed the subjects under discussion were reviewed. This paper used the descriptive and comparative approaches as the analytical methods. According to the findings of the review, Storm Water Management Model (SWMM) is the computer model in LID modeling that is most frequently employed. This model is a fundamental package for dynamic urban rainfall-runoff modeling, and it has the benefits of being lightweight, simple to use, and an intuitive user interface. Besides, this model is public domain (free to use), open source, and interoperable with many hydro modeling applications. A specific LID editor module is also included in this model for modeling different LID units. To acquire the best LID planning and design from multiple criteria and alternatives, it is also necessary to use metaheuristic algorithms as an optimization model and a multi-criteria decision-making (MCDM) model in addition to the rainfall-runoff model. The authors believe combining the hydrologic and hydraulics models integrated with geographical information systems (GIS), metaheuristic algorithms, and MCDM is the most comprehensive and appropriate method for LID modeling in urban watersheds.

Coupling intention and actions of vehicle–pedestrian interaction: A virtual reality experiment study

The interactions between vehicles and pedestrians are complex due to their interdependence and coupling. Understanding these interactions is crucial for the development of autonomous vehicles, as it enables accurate prediction of pedestrian crossing intentions, more reasonable decision-making, and human-like motion planning at unsignalized intersections. Previous studies have devoted considerable effort to analyzing vehicle and pedestrian behavior and developing models to forecast pedestrian crossing intentions. However, these studies have two limitations. First, they mainly focus on investigating variables that explain pedestrian crossing behavior rather than predicting pedestrian crossing intentions. Moreover, some factors such as age, sensation seeking and social value orientation, used to establish decision-making models in these studies are not easily accessible in real-world scenarios. In this paper, we explored the critical factors influencing the decision-making processes of human drivers and pedestrians respectively by using virtual reality technology. To do this, we considered available kinematic variables and analyzed the internal relationship between motion parameters and pedestrian behavior. The analysis results indicate that longitudinal distance and vehicle acceleration are the most influential factors in pedestrian decision-making, while pedestrian speed and longitudinal distance also play a crucial role in determining whether the vehicle yields or not. Furthermore, a mathematical relationship between a pedestrian’s intention and kinematic variables is established for the first time, which can help dynamically assess when pedestrians desire to cross. Finally, the results obtained in driver-yielding behavior analysis provide valuable insights for autonomous vehicle decision-making and motion planning.

A multi-criteria decision-making model for heavy construction equipment replacement in Saudi Arabia

Purpose This paper aims to propose a novel model that addresses the limitations of current practices, through considering quantitative and qualitative criteria in the decision-making process for equipment replacement. Design/methodology/approach Literature review and consultation with professionals in the heavy construction industry was conducted to identify the criteria influencing the replacement of construction machines. A questionnaire survey using analytic hierarchy process and multi-attribute utility theory was used to rank these criteria and establish their utility scores. Sensitivity analysis was performed to assess how adjustments in the weights of main criteria would impact equipment replacement decisions. Findings The identified criteria were classified into three categories: economic, technical and socioenvironmental, encompassing a total of 15 criteria. The findings indicated that salvage value/meeting payback period/maximizing profitability held the highest importance in the replacement process, followed by considerations like high repair and maintenance cost; working condition and economic conditions. Safety and social benefits scored the least among all criteria and categories. Research limitations/implications This study focuses on earth-moving equipment and involves experts from the Eastern Province of Saudi Arabia. The model introduces a novel methodology to aid decision-makers, particularly contractors and project managers, in determining when to replace heavy construction equipment, which results in resource efficiency and time saving. Originality/value The model integrates expertise and knowledge from experts to establish criteria for replacing construction equipment. This research aims to improve the functionality of the decision-making process regarding the acquisition or replacement of equipment throughout its lifespan.

Empowering Unskilled Production Systems Consultants through On-the-Job Training Support: A Digital Triplet Approach

This study aims to experimentally confirm whether knowledge that has been challenging to transfer through traditional on-the-job training (OJT) can be effectively transferred by introducing a formalized OJT approach that describes the improvement process knowledge of skilled production systems consultants, facilitating imitation by unskilled consultants. We adopted the Digital Triplet (D3) concept, an extension of the authors’ digital twin framework to intelligent activities, aligning with our study objectives. Recognizing the difficulty and inadequacy of knowledge transfer in production systems consulting OJT, we propose an OJT support method integrating a decision-making modeling approach for skilled consultants’ processes based on the Generalized Production Systems Consulting Process Model (GCPM) from prior literature into traditional OJT methods involving self-learning and direct instruction. This method enables the construction of a domain-specific GCPM, formalizing the improvement process flow implemented by skilled consultants and linking it to production improvement expertise and tools. In a case study focused on energy-saving improvement, we constructed and tested a domain-specific GCPM’s efficacy in facilitating the transfer of difficult-to-transfer knowledge. The results indicate that domain-specific GCPM facilitates such knowledge transfer, including specialized improvement, knowledge utilization, rationale, and adaptation to specific cases.

A Clinical Focus on Shared Decision Making in Clinical Practice When Providing Dysarthria and Dysphagia Services to Individuals With Amyotrophic Lateral Sclerosis

Purpose: Traditional health care decision-making models center on clinicians making decisions for patients/caregivers based on the best available research evidence. However, this diminishes patient/caregiver involvement in their care and hinders the ability to align care plans with patient values and preferences. Shared decision making is a potentially beneficial process to implement with individuals with amyotrophic lateral sclerosis (ALS) to provide more holistic, patient-centered dysarthria and dysphagia treatment. Shared decision making promotes active involvement by patients/caregivers by informing them of potential treatment options, understanding their values and preferences, and aligning their desires with treatment options to determine the most optimal individualized care plan. Conclusions: The benefits and barriers to incorporating shared decision making within ALS multidisciplinary clinics are discussed in this clinical focus article. Furthermore, a fictional case study example of how to apply shared decision making to dysarthria and dysphagia management of individuals with ALS is provided.

Stealth Aircraft Penetration Trajectory Planning in 3D Complex Dynamic Based on Radar Valley Radius and Turning Maneuver

Based on the quasi-six-degree-of-freedom flight dynamic equations, considering the changes in the elevation angle caused by an increase in the rolling angle during maneuvering turns, which leads to a rise in the radar cross-section. A computational model for the radar detection probability of aircraft in complex environments was constructed. By comprehensively considering flight parameters such as turning angle, rolling angle, Mach number, and radar power factor, this study quantitatively analyzed the influence of these factors on the radar detection probability. It revealed the variation patterns of radar detection probability under different flight conditions. The results provide theoretical support for the Radar Valley Radius and Turning Maneuver Method (RVR-TM) based on decision trees, and lay the foundation for the development of subsequent intelligent decision-making models. To further optimize the trajectory selection of aircraft in complex environments, this study combines theoretical analysis with reinforcement learning algorithms to establish an intelligent decision-making model. This model is trained using the Proximal Policy Optimization (PPO) algorithm, and through precisely defining the state space and reward functions, it accomplishes intelligent trajectory planning for stealth aircraft under radar threat scenarios.

A data-driven robust decision-making model for configuring a resilient and responsive relief supply chain under mixed uncertainty

A data-driven robust decision-making model for configuring a resilient and responsive relief supply chain under mixed uncertainty

A Decision-Making Model Proposal for the Use of Renewable Energy Technologies in Buildings in Turkey

With the increasing need for energy, issues related to using energy efficiently in buildings and employing renewable energy technologies are gaining significance. The building production process is intricate, involving numerous stakeholders, multiple decisions, and a combination of qualitative and quantitative data. This process necessitates decision making based on specific requirements. The objective of this study is to identify effective criteria in decision making concerning the use of renewable energy technologies (RETs) in buildings in Turkey. It aims to highlight the importance of these criteria and compare them, and it also aims to define a recommendation decision-making model for the widespread adoption of renewable energy technologies. This study employed qualitative and quantitative research methods. Based on information gathered from the literature, the main criteria and sub-criteria for RET utilization were determined through in-depth interviews with an expert group, including individuals influencing the building design process (architects, engineers, consultants, employers, and users). A recommendation model was developed using the analytic hierarchy process method to highlight the significance of the identified criteria, compare the criteria and technologies, and facilitate the selection of the most-appropriate technology. This study demonstrates that the decision-making model can be utilized in determining RET-related criteria in the building production process, establishing their weights, and make informed decisions regarding the appropriate technology.

Energy optimization of the smart residential electrical grid considering demand management approaches

-Many researchers have investigated the optimal energy scheduling to meet the demand regarding sustainable development issues and the economic and technical indices. This study presents the day-ahead operation of a smart residential distribution electrical grid as a bi-stage-multiple-criteria decision-making modeling. The proposed energy optimization is implemented based on optimal demand management in upper-stage and multiple-criteria decision-making in lower-stage. The multiple-criteria problem is modeled from the viewpoint of the grid's operator to optimize energy consumption costs, power losses and demand side comfort. The optimal demand management in upper-stage is coordinated considering price traffic in the upstream grid. The load shifting approach and load interruption approach are presented as optimal demand management for residential consumers. The operation of demand management by using load shifting and load interruption approaches is done via deferrable loads and clippable loads in smart residential homes, respectively. The proposed energy optimization in both stages by improved sunflower optimization (IFSO) is handled, and the TOPSIS method is proposed for the best trade-off of the multiple-criteria decision-making. The day-ahead energy operation is applied on the 33-bus distribution network to investigate the effectiveness of the residential distribution electrical grid considering obtained results from mathematical modeling and demand management approaches.

Scope of Practice: Shared Decision-Making For Patient-Centered Care

Shared decision-making (SDM) represents a collaborative model of healthcare decision-making, contrasting the traditional unidirectional approach where physicians solely determine medical choices. SDM engages patients in evaluating options based on individual goals and values. In various clinical scenarios, SDM proves indispensable, enhancing patient satisfaction, treatment adherence, and reducing the likelihood of blaming physicians for adverse outcomes. Illustrated by the BRAN questions in the UK, SDM serves as a tool to explore benefits, risks, alternatives, and the option of doing nothing. Notwithstanding its benefits, SDM encounters implementation challenges, including time constraints and varying patient priorities. Elderly patients may face cognitive barriers, exacerbated by limited clinical trials catering to diverse geriatric needs. Nevertheless, SDM offers significant advantages, fostering strong patient-physician relationships, aligning with patient-centered care principles, and yielding positive outcomes. Physicians must integrate SDM consciously into clinical practice, recognizing its potential to improve patient satisfaction, quality of life, and overall healthcare outcomes. Bangladesh J Medicine 2024; 35: 122-123

Research on Stakeholder Behavior Strategy in Green PPP Project Financing

In the context of green development, while promoting green industry and sustainable development, green PPP faces the challenge of financing difficulties. Based on the evolutionary game theory of bounded rationality and taking government, social capital and financial institutions as the game players, this paper constructs an evolutionary game model for the behavioral decision-making of stakeholders in green PPP project financing. Using MATLAB software, we simulate and analyze the key factors affecting the behavioral strategies of each game player. The results indicate that: (1) The additional income obtained from participation in green PPP projects is a key factor affecting the decision-making of social capital and financial institutions, so it is necessary to improve the reasonable return mechanism of green PPP projects and attach importance to the construction of reputation mechanism; (2) Government subsidies can effectively encourage social capital and financial institutions to participate in green PPP projects, but the amount of subsidies should be within a reasonable range; (3) The government's reward and punishment intensity is a significant factor affecting the decision-making of social capital behavior. To this end, it is imperative for the government to enhance the performance evaluation system for green PPP projects and establish a balanced reward and punishment mechanism. This paper puts forward some suggestions on the decision-making of stakeholders in the financing process of green PPP projects, and provides references for the policy formulation of encouraging social capital and financial institutions to participate in green PPP projects.

Assessing School District Decision-Making: In-Person Schooling and COVID-19 Transmission

Abstract Recent controversies have highlighted the importance of local school district governance, but little empirical evidence exists evaluating the quality of district policymakers or policies. In this paper, we take a novel approach to assessing school district decision-making. We posit a model of rational decision-making under uncertainty that emphasizes districts learning over time. We test the predictions from the model using data on a set of highly visible and consequential decisions facing school district leaders—the choice of learning mode during the 2020-21 school year. We find that district behavior is consistent with a Bayesian learning process in several key respects. Districts respond on the margin to health risks: all else equal, a marginal increase in new cases reduces the probability that a district offers in-person instruction the next week. This negative response is magnified when the district was in-person the prior week and attenuates in magnitude over the school year, suggesting districts learn from experience about the effect of in-person learning on disease transmission in schools. We also find evidence that districts are influenced by the learning mode decisions of peer districts, but not their peers' experiences with in-person instruction and disease transmission, which implies that some important frictions exist.

Capturing key interdependences among supply chain disruptions and mitigation strategies to enhance firm performance

PurposeThis paper aims to capture the complex interdependences between supply chain disruptions (SCDs), SC risk mitigation strategies and firm performance in the context of disruptive events to enhance resilience for medium-sized and large firms coping with complex supply chain networks. The roles of digitalization, insurance and government support have also been addressed as potential strategies to counteract the impacts of disruptions on supply chains.Design/methodology/approachThis study is based on an empirical investigation in an FMCG company – using a hybrid causal mapping technique based on the frameworks of interpretive structural modeling (ISM) and Bayesian networks (BN) – of 11 levels of relationships between SCDs (in supply, production, logistics, demand and finance), SC risk mitigation strategies (flexibility, efficiency, agility and responsiveness), insurance, government support, information and knowledge sharing, digitalization and finally the key firm performance measures (continuity, quality and financial performance).FindingsThe results of the empirical investigation reveal and describe: (1) the nature and probabilistic quantification of the lower-level relationships among the four SCDs, among the mitigation strategies and the three firm performance measures; (2) the nature and probabilistic quantification of the higher-level relationships among the impacts of SCDs, SC risk mitigation strategies and firm performance and (3) how to model and quantify the complex interdependences in single firms and their supply chains.Originality/valueOur results can support managers in developing more effective decision-making models to assess and manage unfavorable events and cascade effects among different functions and processes in the context of risks and disruptions.

Bias in team decision-making for advanced heart failure therapies: model application

ABSTRACT Bias in advanced heart failure therapy allocation results in inequitable outcomes for minoritized populations. The purpose of this study was to examine how bias is introduced during group decision-making with an interprofessional team using Breathett’s Model of Heart Failure Decision-Making. This was a secondary qualitative descriptive analysis from a study focused on bias in advanced heart failure therapy allocation. Team meetings were recorded and transcribed from four heart failure centers. Breathett’s Model was applied both deductively and inductively to transcripts (n = 12). Bias was identified during discussions about patient characteristics, clinical fragility, and prior clinical decision-making. Some patients were labeled as “good citizens” or as adherent/non-adherent while others benefited from strong advocacy from interprofessional team members. Social determinants of health also impacted therapy allocation. Interprofessional collaboration with advanced heart failure therapy allocation may be enhanced with the inclusion of patient advocates and limit of clinical decision-making using subjective data.

Energy-aware dynamic rescheduling of flexible manufacturing system using edge-cloud collaborative decision-making method

ABSTRACT With the development of cloud computing technology, energy-aware scheduling based on cloud computing is an essential means to achieve energy saving and carbon reduction in manufacturing systems. Due to various production disturbances in flexible production, the scheduling based on cloud computing has data security problems and poor real-time performance. How to use real-time information to improve the security and responsiveness of cloud scheduling is a research gap. This paper establishes a cloud-edge collaborative dynamic flexible job-shop energy-aware rescheduling decision-making model. According to the characteristics of dynamic production in a flexible manufacturing system, create a learning model for cloud scheduling with Deep Q Network (DQN) and send the training model to the edge for scheduling decisions. Based on real-time production interference data, edge scheduling model real-time update scheduling scheme. To improve the robustness of the cloud scheduling model based on DQN, Dynamic scheduling data at the edge will be uploaded to update the cloud model. In addition, the effectiveness and practicability of the model are verified in an extrusion workshop. The experimental results show that this method can improve the comprehensive objective evaluation of minimum energy consumption and completion time by 3.6% −49.3% compared with the traditional scheduling rules.

When should one be open-minded?

AbstractIt is widely believed among philosophers and educated people that it is virtuous to be open-minded. Instead of thinking of open-mindedness as universally or unconditionally epistemically valuable, I argue that it is vital to explicate the conditions that must obtain if open-mindedness is to be epistemically valuable. This paper critically evaluates open-mindedness given certain realistic cognitive limitations. I present and analyse a simple mathematical model of open-minded decision-making that incorporates these limitations. The results are mixed. The bad news is that the circumstances where open-mindedness is epistemically valuable may be more restricted than previously thought especially if individuals are incapable of properly evaluating the trustworthiness of sources or the content of received information. The good news is that, if individuals are mildly competent in evaluating the trustworthiness of sources and the content of received information, then there are many circumstances where open-mindedness is epistemically valuable.

A quantum group decision model for meteorological disaster emergency response based on D-S evidence theory and Choquet integral

In addressing complex and dynamic meteorological disaster decision-making environment, the traditional multi-attribute group decision-making domain model is often unable to effectively deal with the correlation between attributes and the mutual influence of group opinions. To overcome this challenge, this paper proposes a novel quantum framework for group decision-making, which is used to deal with the emergency situation of meteorological disaster. The model initially characterizes attribute correlations using 2-additive Choquet integrals and employs Dempster-Shafer evidence theory to both integrate information and ascertain attribute weights, and decision makers' weights are calculated based on grey relative correlation. On this basis, a quantum-like Bayesian network is developed to capture the interference among decision-makers' opinions. The alternatives are ranked by quantum probabilities computed based on Bayesian principle. Finally, a case study on meteorological disaster emergency scenario assessment is conducted to validate the proposed model's effectiveness and superiority. Additionally, its stability and practicality are confirmed through sensitivity analysis and comparative analysis.

Adopting smart retrofits: a decision-making model and benchmarking criteria

PurposeEnergy consumption in existing office buildings has been growing in parallel with the rise in occupant energy demand. As a result, many building owners have given smart retrofits (SRs) a higher priority. However, the utilisation of suitable SRs from a range of SRs has become a challenging task. The purpose of this paper is to develop a decision-making model to select the most suitable SRs for conventional office buildings and form a set of benchmarks for assessing the performance of SRs.Design/methodology/approachA qualitative approach with six case studies was used. Content analysis was carried out using NVivo to explore the factors considered for the selection of SR techniques. A decision-making model for selecting SRs in Sri Lankan office buildings was proposed. SR performance benchmarks were developed by referring to established standards and studies done in tropical office buildings.FindingsOut of 18 identified SRs from literature, fan cycling, ventilation control and LED luminaires have been recognised as commonly used SRs in Sri Lankan office buildings. Analysis showed that HVAC retrofits saved more energy, while lighting retrofits could be easily implemented in existing buildings. The proposed decision-making model can explore further improvements to enhance the performance of SRs.Originality/valueThe selection of SRs is a comprehensive decision-making process. Metrics were established to benchmark the performance of SRs. The proposed model offers a tool for building owners and facility managers to optimise facility operations.