Decision-making Model Research Articles

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.

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.

RETRACTED: Lu et al. Implementation of Stochastic Analysis in Corporate Decision-Making Models. Mathematics 2021, 9, 1041

The journal retracts the article entitled “Implementation of Stochastic Analysis in Corporate Decision-Making Models” [...]

Are we ready for metaverse adoption in the service industry? Theoretically exploring the barriers to successful adoption

The Metaverse has emerged as a convergence of technologies and conceptions with the potential to reshape how we interact, participate, learn, and entertain ourselves in the digital age. It has gained substantial attention recently due to its immersive, interconnected, and virtual environment for meeting evolving customer and market demands. As technology advances, firms implementing the Metaverse may gain a competitive edge and create new revenue streams. However, the service sectors still face challenges in adopting and successfully implementing Metaverse. Therefore, we attempted to establish a theoretical foundation to understand the challenges of Metaverse adoption in the service sector and to develop an integrated framework to analyse the barriers using the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Decision-making trial and evaluation laboratory (F-DEMATEL) techniques. To achieve this, we first identify barriers from extensive literature and categorise them into six broad categories based on experts’ opinions. Then, we ranked each category and the barriers constituting them based on their criticality through FAHP. The Security and Privacy category has the highest weight among the six categories of barriers, followed by Governance and Standardisation, Infrastructure, User Behaviour and psychology, Feasibility, and Organisational culture and Stakeholder commitment. Further, we developed the causal-effect relationships among the six categories and analysed those categories through the degree of prominence and relationships. We found that Security and Privacy is one of the most influencing barriers. Then, we analysed our proposed decision-making model under different scenarios through sensitivity analysis. This study offers valuable insights into the relationships among the barriers and how they can impact progress towards achieving specific goals or objectives.

A consensus reaching model for expert behavior-driven adjustment of expert weights based on picture fuzzy sets

This article proposes an expert-driven consensus and decision-making model that comprehensively considers expert behavior in Multi-criteria decision-making (MCDM) scenarios. Under the premise that experts are willing to adjust their viewpoints, the framework strives to reach group consensus to the utmost degree feasible. To tackle experts’ uncertainty during the evaluation process, this article employs the rejection degree in the picture fuzzy sets (PFS) to signify the level of ignorance while they deliver their evaluation opinions. Due to the diversity of expert views, reaching a group consensus is difficult in reality. Therefore, this article additionally presents a strategy for adjusting the weights of experts who did not reach consensus. This approach upholds data integrity and guarantees the precision of the ultimate decision. Finally, this article confirms the efficiency of the aforementioned model by means of a case study on selecting the optimal carbon reduction alternative for Chinese power plants.

A multiview deep learning-based prediction pipeline augmented with confident learning can improve performance in determining knee arthroplasty candidates.

Preoperative prudent patient selection plays a crucial role in knee osteoarthritis management but faces challenges in appropriate referrals such as total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA)and nonoperative intervention. Deep learning (DL) techniques can build prediction models for treatment decision-making. The aim is to develop and evaluate a knee arthroplasty prediction pipeline using three-view X-rays to determine the suitable candidates for TKA, UKAor are not arthroplasty candidates. A study was conducted using three-view (anterior-posterior, lateraland patellar) X-rays and surgical data of patients undergoing TKA, UKAor nonarthroplasty interventions from sites A and B. Data from site A were used to derive and validate models. Data from site B were used as external test set. A DL pipeline combining YOLOv3 and ResNet-18 with confident learning (CL) was developed. Multiview Convolutional Neural Network, EfficientNet-b4, ResNet-101and the proposed model without CLwere also trained and tested. The models were evaluated using metrics such as area under the receiver operating characteristic curve (AUC), accuracy, precision, specificity, sensitivityand F1 score. The data set comprised a total of 1779 knees. Of which 1645 knees were from site A as a derivation set and an internal validation cohort. The external validation cohort consisted of 134 knees. The internal validation cohort demonstrated superior performance for the proposed model augmented with CL, achieving an AUC of 0.94 and an accuracy of 85.9%. External validation further confirmed the model's generalisation, with an AUC of 0.93 and an accuracy of 82.1%. Comparative analysis with other neural network models showed the proposed model's superiority. The proposed DL pipeline, integrating YOLOv3, ResNet-18and CL, provides accurate predictions for knee arthroplasty candidates based on three-view X-rays. This prediction model could be useful in performing decision making for the type of arthroplasty procedure in an automated fashion. Level III, diagnostic study.

A miscellaneous model for comprehensive attractiveness assessment of chemical plants: Integrating chemical hazards, domino effects and plant traits

The growing threat of intentional attacks on major petrochemical plants underscores the urgent need for robust security risk assessments (SRA). Such assessments are widely acknowledged as crucial for risk mitigation and are currently a focus of active research. This study introduces a methodology for assessing the attractiveness of the petrochemical sector to intentional attacks, considering chemical hazards, domino effects, and plant traits. Within this framework, we have identified 12 risk factors that capture the multifaceted nature of targeting incentives, relating to the enterprise, location, population, and other key features. We have developed a hybrid decision-making model that analyzes the interrelationships among these factors and determines their weights, integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP). The applicability of this method is demonstrated through a detailed case study. The results indicate that our method can effectively rank the attractiveness of chemical plants within a specific region, serving as a preliminary screening phase in the SRA. The findings of the attractiveness assessment may assist in identifying potential targets among numerous existing plants and support the allocation of security resources and the formulation of defensive measures.

Social network group decision-making model considering interactions between trust relationships and opinion evolution

PurposeSocial network group decision-making (SNGDM) has rapidly developed because of the impact of social relationships on decision-making behavior. However, not only do social relationships affect decision-making behavior, but decision-making behavior also affects social relationships. Such complicated interactions are rarely considered in current research. To bridge this gap, this study proposes an SNGDM model that considers the interaction between social trust relationships and opinion evolution.Design/methodology/approachFirst, the trust propagation and aggregation operators are improved to obtain a complete social trust relationship among decision-makers (DMs). Second, the evolution of preference information under the influence of trust relationships is measured, and the development of trust relationships during consensus interactions is predicted. Finally, the iteration of consensus interactions is simulated using an opinion dynamics model. A case study is used to verify the feasibility of the proposed model.FindingsThe proposed model can predict consensus achievement based on a group’s initial trust relationship and preference information and effectively captures the dynamic characteristics of opinion evolution in social networks.Originality/valueThis study proposes an SNGDM model that considers the interaction of trust and opinion. The proposed model improves trust propagation and aggregation operators, determines improved preference information based on the existing trust relationships and predicts the evolution of trust relationships in the consensus process. The dynamic interaction between the two accelerates DMs to reach a consensus.

Minimisation of the Energy Expenditure of Electric Vehicles in Municipal Service Companies, Taking into Account the Uncertainty of Charging Point Operation

This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging points. The uncertainty of the charging points’ operation was presented as the probability of the occurrence of an emergency situation hindering a point’s operation, e.g., a breakdown or lack of energy supply. The problem is how to calculate the driving routes of electric vehicles so that they will arrive at charging points at times at which there is a minimal probability of breakdowns. The second aspect of this problem to be solved is that the designated routes are supposed to ensure the minimum energy expenditure that is needed for the vehicles to complete the tasks assigned. The developed method is based on two heuristic algorithms, i.e., the ant algorithm and genetic algorithms. These algorithms work in a hybrid combination, i.e., the ant algorithm generates the initial population for the genetic algorithm. An important element of this method is the decision-making model for defining the driving routes of electric vehicles with various restrictions, e.g., their battery capacity or the permissible risk of charging point breakdown along the routes of the vehicles. The criterion function of the model was defined as the minimisation of the energy expenditure needed by the vehicles to perform their transport tasks. The method was verified against real-life data, and its effectiveness was confirmed. The authors presented a method of calibrating the developed optimisation algorithms. Theoretical distributions of the probability of charging point failure were determined based on the Statistica 13 program, while a graphical implementation of the method was carried out using the PTV Visum 23 software.

An integrated model for road freight transport firm selection in third-party logistics using T-spherical Fuzzy sets

The efficient selection of road freight transport (RFT) firms plays a critical role in constructing well-operating logistics systems for large-scale third-party logistics (3PL) providers. However, the decision-making process for such selection is often complex and uncertain due to various influential criteria and dynamic industry nature. To address this challenge, a novel decision-making model that integrates Delphi, CRiteria Importance Through Intercriteria Correlation (CRITIC) and Combined compromise ranking solution (CoCoSo) methods based on T-Spherical Fuzzy sets has been proposed in this paper. The model integrates expert opinions with an extensive literature review to identify the influential criteria and their corresponding weights. Subsequently, it employs a comprehensive evaluation approach to rank the RFT firms based on their performance. The model accommodates uncertainties and subjectivity by utilizing T-Spherical Fuzzy Numbers, offering robustness and transparency in decision-making. A case study involving 15 evaluation criteria and 12 RFT firms were selected to demonstrate the applicability and aptness of the proposed model. Flexibility and Integrability to different transport modes emerged as the two most essential criteria, whereas RFT firm A2 emerged as the best alternative with the highest performance score of 3.0861, followed by RFT firm A7 with a score value of 3.0499.

Consequence analysis of a small-scale hydrogen leakage from the overhead hydrogen piping based on machine learning and physical modeling

Leakage from the overhead hydrogen piping (OHP) must be contained because of the wide flammability range and low minimum ignition energy of hydrogen; rapid leakage detection and appropriate emergency responses are necessary to ensure safe OHP operation. Consequence analysis after leakage detection is useful for hazardous-area estimation and assists operators in emergency-response planning. However, consequence analysis and leakage-detection methods have not been combined so far, and an integrated method has not yet been developed. This study aimed to develop a method based on machine learning (ML), physical modeling, and consequence analyses for rapid and appropriate decision-making after hydrogen leakage. Initially, an OHP model was constructed using physical modeling; this is a method that models target systems based on fundamental physical equations. Subsequently, an ML model for leakage detection was constructed based on various pressure or flow-rate profiles obtained from the OHP model. Regression models were constructed to estimate the leakage diameter. In addition, consequence analyses were conducted to estimate the effects of hydrogen explosions and fires using the leakage diameters predicted by the regression model. We confirmed that the ML model could distinguish between leakage and non-leakage conditions, and the estimated consequences could be used to visualize the risk level of hazardous areas near the leakage points in the case studies. The results of the case studies demonstrated the effectiveness of the proposed method for rapid decision making when hydrogen leaks from OHP. This method enables improved emergency responses in post-leakage situations.

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 probabilistic 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.

Cubic picture fuzzy topological data analysis with integrating blockchain and the metaverse for uncertain supply chain management

This article explores the idea of cubic picture fuzzy sets (CPFSs) for developing blockchain-driven solutions to meet uncertain supply chain management (SCM) and contemporary urban transportation (CUT) challenges. For this objective, the concept of topological structures on CPFSs is explored and the notion of cubic picture fuzzy topology is defined with P-order and R-order. We construct CRITIC-COPRAS method based on cubic picture fuzzy topological data analysis and cubic picture fuzzy fairly aggregation operator for finding solutions to SCM, CUT, electronic toll collection, fare management, micromobility solutions, and car-sharing implementations. We also examine the transformative potential of blockchain technology within the metaverse particularly in the domains of ride-sharing and peer-to-peer mobility platforms. To systematically evaluate and prioritize these integration strategies, we employ a robust multi-criteria decision-making (MCDM) model. This comprehensive assessment covers eight criteria including, transportation efficiency, user experience, technological feasibility, and regulatory compliance. Moreover, it offers promising solutions for revenue management and data security in electronic toll collection and public transportation fare management.

A quantitative framework to support the decision between traditional, selective, and hybrid assembly

High-technological products often consist of several components, each with varying feature values that are critical to the assembly’s quality. As traditional manufacturing involves random selection of components, strict tolerance ranges are typically set to limit their feature variability, increasing scrap rates and lowering material efficiency. By smartly combining components based on their measured features, selective and hybrid assembly allow for more relaxed tolerances and more efficient use of raw materials without affecting the final product’s quality. However, the increased logistical complexity involves additional operational costs, complicating the choice for the best strategy.This study presents a decision-making model for selecting from traditional, selective, and hybrid assembly. The model is formulated by first defining the cost functions for the corresponding expected assembly cost, and then analytically determining the optimal choice boundaries between the three methods based on the involved process parameters. Estimation methods for the instance-specific process parameters are then discussed before validating the model with a fully elaborated case study.Results confirm the need for a quantitative framework to support an informed choice as this strongly depends on the involved parameter values. This study aims to pave the way towards more sustainable manufacturing by supporting practitioners to identify the conditions whereby intelligent assembly methods, in addition to allowing for scrap reduction and increased material efficiency, also represent the most cost-effective option.

Foreword for the special issue: “Decision-making models for common goods in environmental, health, and logistics contexts”

We present the scientific results of the call for papers in SEPS entitled by Decision-Making Models for Common Goods in Environmental, Health, and Logistics Contexts. We describe the original domain of our academic initiative as guest editors in Socio-economic Planning Sciences (SEPS). We present the collected papers in SEPS. Interestingly, our collection of papers includes different methodologies from multi-criteria decision making, game theory, operations research, micro-economics, computational models and insurance mathematics. We have received more than forty submissions from Asia, Europe and America. However, fifteen papers were accepted for publication in SEPS. The latter shows how widely investigated is the problem of common goods and how still controversial is the academic discussion.

Fuzzy Inference Systems for Discretionary Lane Changing Decisions: Model Improvements and Research Challenges

Lane Changing Decision Model (LCDM) is a critical component in semi- and fully-automated driving systems. Recent research has found that Fuzzy Inference System (FIS) is a promising approach to implementing LCDMs. To improve the FIS’s performance, this research reviewed the challenges in the development of a FIS model to make the yes,no decisions in discretionary lane changes. The FIS model was revised to bring its fuzzy inference rules more consistent with the fuzzy membership functions, and its composition and defuzzification methods more in line with the classical fuzzy logic theory. An equitable test data set with approximately equal number of yes,no data points was assembled from the same NGSIM data used in the past research. The test results proved that: (1) A LCDM’s performance was dependent on how the yes,no decisions in the test data set were manually labeled; and (2) Separating the fuzzy inference rules into a yes group and a no group and compute the results separately yielded potentially better decision accuracy. Furthermore, The Genetic Expression Programming Model (GEPM) performed better than the improved FIS-based model. The findings lead the authors to suggest two possible research directions: (1) Add the subject vehicle’s speed as an input to the LCDM and redesign the decision-making model; and (2) construct models for congested and uncongested traffic separately. The authors further suggested the use of instrumented vehicles to collect a set of high-fidelity lane changing data in the naturalistic driving environment. (word count: 246)

A Multi-Criteria Decision-Making Model to Determine the Optimal Share of Variable Renewable Energy Sources in a Wholesale Electricity Market

In recent years, the development of renewable energy technologies has gotten increasing attention due to the realization of cleaner production and high energy efficiency. Therefore, the proportion of these resources in electricity production has experienced a dramatic increase. However, with the features of intermittent nature and merit order effects, it is challenging to effectively optimize the integration of renewable energy sources (RES) into the electricity markets. To deal with the complex problem of selecting the proportionate share of non-dispatchable RES for participation in the market, this paper establishes a multi-criteria decision-making (MCDM) framework, which combines the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW). Five criteria are defined and the corresponding mathematical model is solved by the proposed AHP-SAW method. In this way, the optimal share of renewable resources for participation in the wholesale market can be determined based on the desired criteria. Also, to compare the results obtained by the AHP-SAW method, the criteria weights are determined by Shannon entropy, and the problem is solved by the TOPSIS method. To assert the effectiveness of the proposed framework, two case studies are done. According to the results, by increasing the RES penetration level, the average price of the market decreases mildly while price volatility increases significantly remarkably. Also, the amount of forward contracts is decreased when the RES share increases. Moreover, the results show that a high amount of RES is not necessarily desirable and depends on the type of renewable resources and policymakers' priorities. The results for systems 1 and 2 indicate that the maximum allowable amount of RES are 85.359 and 38.24MW, respectively.