Decision-making Strategies Research Articles

Analysis of The Structural Embeddedness of a Scientific Research Laboratory’s Supplier Network

Objective: The objective of this research is to map a scientific research laboratory’s supplier network using Ucinet software and analyze its structural network. Theoretical Reference: Supply chain management is currently becoming increasingly important in companies as a decision-making strategy. The software tool based in Social Network Analysis (SNA) Ucinet allows modeling of a supply network based on its actors. By analyzing the supplier network, it is possible to identify opportunities for improvement that may not have been seen in previous diagnoses. Method: Data were collected using a questionnaire. Ucinet software and its integrated module NetDraw were used for data analysis, which allowed the data to be visualized in graphical format, to model the network and obtain structural indicators. Results: From the analysis of the supply network, improvements for the management of the laboratory’s logistics purchasing process were suggested. Implications of the research: The management of the laboratory’s logistics purchasing process helps researchers and managers and adjust its sizing parameters to the results obtained. Originality/Value: Ability to model the supplier network and obtain structural indicators, contributing to a better clarification of the exchange relationships between the authors.

Digital Transformation and Business Model Innovation: Moderating Role of Digital Leadership

Purpose: The manufacturing sector in Ghana has become increasingly important to the country's economic growth, driven by digital transformation and innovation. This research aims to explore the complexities of digital transformation within Ghanaian manufacturing companies, with a specific focus on the moderating role of digital leadership. Design/Methodology/Approach: This conceptual paper presents a framework that integrates socio-technical systems theory and the upper echelon theory. This framework offers a detailed understanding of how digital transformation, digital leadership, organizational adaptation, and business model innovation interact within the Ghanaian manufacturing sector. Findings: The Ghanaian manufacturing sector, a significant contributor to the country's GDP, has seen substantial growth, largely due to small and medium-sized enterprises (SMEs). However, many industries within the sector face challenges in embracing the digital age, including inadequate technological infrastructure and limited access to funding. The Ghanaian government has responded with policies to enhance digital capabilities and innovation, positioning the country for digital readiness. Originality: This paper contributes to the field by providing practical strategies for leadership development and decision-making within manufacturing organizations. It also advances theoretical perspectives and offers empirical insights specific to the Ghanaian context. Overall, this paper serves as a valuable resource for manufacturing leaders, policymakers, and researchers navigating digital transformation, innovation, and leadership in Ghana's manufacturing sector, with broader implications for emerging economies globally.

A latent class analysis of biosecurity attitudes and decision-making strategies of swine producers in the United States

The 2018 African swine fever (ASF) outbreak highlighted the importance of biosecurity in food production systems. Despite the significant economic impacts, the sociopsychological consequences on decision-making have been overlooked. Previous studies have focused on algebraic models and simulation-based models without considering the complex psychological and social factors that influence farmers' biosecurity behaviors and decision-making processes. This study aims to classify livestock producers into distinct subgroups based on their attitudes towards biosecurity. We conducted a survey presenting producers with three scenarios to assess their willingness to report suspected ASF cases, trust in government agencies, risk perception, biosecurity knowledge, willingness to purchase livestock insurance, motivation to invest in biosecurity, readiness to report suspected infections, and intention to contact a veterinarian. Using latent class analysis, we identified three distinct classes: Biosecurity Sceptics, Biosecurity Compliant, and Biosecurity Ultra-Compliant. Our results show that producer characteristics significantly influence biosecurity attitudes and class membership, with small-scale producers less likely to adopt ultra-compliant biosecurity practices. Attending at least one eradication program encouraged biosecurity compliance. This research informs the design of targeted food policy and risk communication strategies that account for attitudes of livestock producers to encourage biosecurity adoption and reduce the likelihood of Tier 1 disease incursion.

Low-Carbon Supply Chain Decision-Making and CSR Strategy Evolution Analysis Considering Heterogeneous Consumer Preferences

Decision-making regarding the low-carbon supply chain, considering corporate social responsibility (CSR) and the heterogeneous preferences of consumers, has become an urgent topic to be explored. This paper explores the decision-making problem of a low-carbon supply chain considering the heterogeneous preferences of consumers under different CSR situations, analyzes the influence of important parameters on each equilibrium solution, compares the size relationship of each equilibrium solution under different CSR situations, and verifies the conclusions obtained through numerical simulation. Then, based on the obtained equilibrium solution, a CSR evolutionary game model of the low-carbon supply chain is constructed, and the evolutionary stability strategies of the two sides on the CSR game are solved. Finally, the evolutionary trajectory of the game system is intuitively presented using a simulation method, and the influence of the main parameters on the evolutionary trends of the two sides is analyzed. The findings are as follows: (1) When both manufacturers and retailers undertake CSR, the retail price and wholesale price are their lowest, while carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility are the highest. (2) When a company undertakes CSR, carbon emission reduction, total market demand, manufacturer utility, retailer utility, and supply chain total utility all increase with the increase in the CSR degree of the company and the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers. (3) The evolutionary stability strategy for both manufacturers and retailers is to undertake CSR. In addition, the initial proportion of manufacturers and retailers that undertake CSR, the low-carbon preference of low-carbon consumers, and the increase in the ratio of the potential scale of low-carbon consumers to the potential scale of ordinary consumers can encourage both members of the supply chain to undertake CSR.

Sequential three-way group decision-making for double hierarchy hesitant fuzzy linguistic term set

Group decision-making (GDM) characterized by complexity and uncertainty is an essential part of various life scenarios. Most existing researches lack tools to fuse information quickly and interpret decision results for partially formed decisions. This limitation is particularly noticeable when there is a need to improve the efficiency of GDM. To address this issue, a novel multi-level sequential three-way decision for group decision-making (S3W-GDM) method is constructed from the perspective of granular computing. This method simultaneously considers the vagueness, hesitation, and variation of GDM problems under double hierarchy hesitant fuzzy linguistic term sets (DHHFLTS) environment. First, for fusing information efficiently, a novel multi-level expert information fusion method is proposed, and the concepts of expert decision table and the extraction/aggregation of decision-leveled information based on the multi-level granularity are defined. Second, the neighborhood theory, outranking relation and regret theory (RT) are utilized to redesign the calculations of conditional probability and relative loss function. Then, the granular structure of DHHFLTS based on the sequential three-way decision (S3WD) is defined to improve the decision-making efficiency, and the decision-making strategy and interpretation of each decision-level are proposed. Furthermore, the algorithm of S3W-GDM is given. Finally, an illustrative example of diagnosis is presented, and the comparative and sensitivity analysis with other methods are performed to verify the efficiency and rationality of the proposed method.

Severe Eosinophilic Asthma or Eosinophilic Granulomatosis With Polyangiitis: Potential Biomarkers for Novel Diagnostic Strategies

BackgroundSevere Eosinophilic Asthma (SEA) may be the prodromal phase of Eosinophilic Granulomatosis with Polyangiitis (EGPA). Nevertheless, few studies have tried to recognize EGPA in the early stages of the disease. ObjectivesTo identify a panel of clinical and biological markers to detect which severe asthmatic patient might be considered in a prodromal phase of EGPA and crafting a strategy for diagnostic decision-making. Methods30 patients with EGPA and 49 with SEA were enrolled. A complete pulmonary, ear, nose and Throat (ENT) and rheumatologic assessment were made. Blood (eosinophil count, eosinophilic cationic protein-ECP, IL5, IL4, total-IgE, IgG4, anti-neutrophil cytoplasmic antibody (ANCA), sputum (eosinophils count, periostin, IL8 and GMCSF) and nasal smear (eosinophilia) biomarkers were assessed. Asthma Control Test, Short Form-36, SinoNasalOutcome Test-22, and Asthma Quality of Life Questionnaire were also used. ResultsSEA patients had poorer asthma control (p<0.001) and higher level of sputum eosinophils (p<0.002) while EGPA patients reported higher levels of blood eosinophils in the past. Sputum GMCSF was the only biomarker significantly increased in EGPA patients compared with SEA (p<0.0001). Among SEA patients, those with some suggestive but not diagnostic criteria of EGPA, particularly tissue eosinophilic infiltrates, presented higher levels of sputum GMCSF (p<0.0005), blood and sputum eosinophils (p<0.0006, p<0.011) in comparison with the other patients. ConclusionSputum GMCSF and eosinophils might be useful biomarkers to support early diagnosis and treatment choices in SEA patients, suspected of having EGPA

Dynamic positioning system of ships with RBF neural network compensator

Abstract In Dynamic Positioning, traditional control methods lack stability, while neural networks and reinforcement learning have become research hotspots. By learning decision-making strategies from environmental observation data, we can adapt to environmental changes in real time and achieve optimal control effects. We also design a reward function to evaluate control performance. Dynamic inversion control is a control strategy that transforms nonlinear systems into linear systems. To address the issue of ship dynamic positioning, we propose a novel controller design method by combining RBF neural networks with dynamic inversion control. Its effectiveness has been verified through simulation experiments, demonstrating its potential and value in practical applications. This method will help improve the positioning accuracy of ships and optimize control performance.

An Intelligent Strategy Decision Method for Collaborative Jamming Based on Hierarchical Multi-Agent Reinforcement Learning

An Intelligent Strategy Decision Method for Collaborative Jamming Based on Hierarchical Multi-Agent Reinforcement Learning

Faster, more accurate, more confident? An exploratory experiment on soccer referees' yellow card decision-making.

This study aimed to examine how soccer referees make decisions about issuing yellow cards for fouls. The research involved 60 male participants, divided into expert (n=30) and novice (n=30) groups based on their experience and qualifications as referees. They took part in a 2×2×2 mixed-design experiment. The study looked at Decision-Making Style (DMS: Analytical Decision-Making [ADM] vs. Intuitive Decision-Making [IDM]), Video Type (yellow card foul vs. non-yellow card foul), and Referee Level (expert vs. novice) as independent variables. The dependent variables were accuracy rate (ACC), discrimination index (D), self-confidence index (C), and overconfidence index (OC). The findings showed that Analytical Decision-Making (ADM) led to higher accuracy compared to Intuitive Decision-Making (IDM). Expert referees demonstrated better accuracy than novice referees. There was also an interaction between Decision-Making Style and Referee Level, showing differences in the effectiveness of ADM and IDM between expert and novice referees. Additionally, the study revealed that both expert and novice referees showed overconfidence, with experts demonstrating significantly higher overconfidence, particularly during IDM. In conclusion, the research highlighted the complexity of referees' decision-making in high-pressure situations and emphasized the potential benefits of employing Analytical Decision-Making strategies. The study contributed to understanding cognitive biases in sports officiating and suggested the need for targeted training programs to help referees improve their performance and reduce overconfidence in challenging situations.

Increased risky choice during forced abstinence from fentanyl on the cued rat gambling task.

The use of illicit opioids has arguably never been more risky; street drug potency can be dangerously high, is often unknown to the consumer, and results in multiple daily fatalities worldwide. Furthermore, substance use disorder (SUD) is associated with increased maladaptive, risky decisions in laboratory-based gambling tasks. Animal studies can help determine whether this decision-making deficit is a cause or consequence of drug use. However, most experiments have only assessed psychostimulant drugs. To assess differences in decision-making strategies both before, during, and after self-administration of fentanyl in male and female Long Evans rats. Male and female Long Evans rats were trained to perform the rat gambling task (rGT), loosely based on the Iowa Gambling Task (IGT) used clinically, and/or self-administer fentanyl. We used the cued version of the rGT, in which sound and light stimuli signal sugar pellet rewards, as cocaine self-administration has the greatest effects on decision making in this task variant. After training on the cued rGT, female rats self-administered fentanyl more readily, an effect that was most apparent in optimal decision-makers. Contrary to previous reports using cocaine self-administration, decision-making was unaffected during fentanyl self-administration training in either sex. However, risky decision-making increased throughout forced abstinence from fentanyl in males. These findings complement those from human subjects, in whom preference for uncertain outcomes increased before relapse. These data highlight an abstinence-induced change in cognition that is unique to opiates as compared to psychostimulants, and which may critically contribute to the maintenance of addiction and relapse.

USV formation navigation decision-making through hybrid deep reinforcement learning using self-attention mechanism

To address the challenging of balancing Unmanned Surface Vessel (USV) autonomous collision avoidance and formation maintenance in uncertain environments, a formation construction and navigation decision-making strategy based on Hybrid Deep Reinforcement Learning (HDRL) is proposed in this study. The novelty of this study is that: (1) A HDRL training approach is proposed, incorporating diverse DRL for virtual leader and followers, thereby significantly enhancing the decision-making adaptability of USVs formation. (2) The multi-head attention mechanism of decentralized Critic strategy is used to enhance the attentional focus of the HDRL algorithm towards different agents, thereby effectively improving convergence speed. (3) The method employs a meticulously designed hybrid reward function and incorporates the Optimal Reciprocal Collision Avoidance (ORCA) for speed selection, thereby providing optimal speed recommendations based on the current situation. It is worth emphasizing that the method exhibits exceptional performance in terms of success rate, navigation time, average reward value, and other relevant indicators within the simulation. Finally, the method is validated through the utilization of real-time navigation data in the Panama Canal, thereby substantiating the potential engineering applicability.

Improving islanded distribution system stability with adaptive decision-making framework

Abstract In an integrated distribution system incorporating distributed generation (DG), various technical challenges must be addressed when the grid becomes disconnected and transforms into an islanded system. The main focus in such circumstances revolves around ensuring the stability of the islanded network. This study presents an advanced decision-making framework for supporting islanded networks by integrating metaheuristic Black Widow Optimization (BWO) and the rate of change of the voltage stability index (RoCVSI). The Rate of Change of the Voltage Stability Index (RoCVSI) detects instability in islanded networks by continuously monitoring rapid changes in the voltage stability margin. Upon identifying potential instability, an advanced decision-making strategy utilizing the Black Widow Optimization (BWO) algorithm is deployed. BWO generates multiple load-shedding scenarios and evaluates their impact on system stability, iteratively refining the solutions through processes similar to selection and cannibalism in black widow spiders. The optimal load-shedding strategy is then implemented to selectively shed specific loads, thereby reducing demand and enhancing island stability. The proposed scheme’s effectiveness for islanded network stability is assessed by extensively analyzing the IEEE 33-bus system. The efficiency of the proposed approach is confirmed through a comparative analysis, with results indicating the better efficiency of the proposed method in the islanded network.

Wait or Pass? Promoting intersection’s cooperation via identifying vehicle’s social behavior

Lack of communication between road users can reduce traffic efficiency and cause safety issues like traffic accidents. Researchers are exploring how intelligent vehicles should communicate with the environment, other vehicles, and road users. This study explores the impact of social information communication on traffic safety and efficiency at intersections through vehicle-to-vehicle (V2V) communication. The research examines how these factors influence drivers’ decision-making and cooperative behavior by incorporating social value orientation (SVO) and driving agent identity into V2V systems and automated vehicle (AV) decision-support systems. An experimental platform simulating intersection conflict scenarios was developed, and three studies involving 334 participants were conducted. The findings reveal that providing drivers with social information about opposing vehicles significantly promotes cooperative behavior and safer driving strategies. Specifically, the waiting rate for people facing proself vehicles (Mean = 0.22) is significantly higher than when facing prosocial vehicles (Mean = 0.79). When SVO is unknown, the waiting rate is around 0.5. Participants behaved more waiting when confronted with an AV than human-driven vehicles. With AV recommendations based on SVO, participants’ final waiting rate increases as the recommended waiting rate increases. The optimal recommended waiting rate for AV is most acceptable when it matches the average waiting rate of the other vehicle. This research underscores the importance of integrating social information into V2V communication to improve road safety, aiding in designing automated decision-making strategies for AV and enhancing user satisfaction.

Design and analysis of a concentrated solar power-based system with hydrogen production for a resilient community

This paper investigates how to improve the utilization of solar energy in concentrated solar power plants for multi-tasked operation, including hydrogen production. In this regard, an integrated energy system with multiple power generation stages is developed to utilize heat from concentrated solar collector with molten salt thermal energy storage system. The two-stage steam Rankine cycle and organic Rankine cycle are used as power generation cycles in order to utilize heat from the molten salt at different temperature levels, which improves source utilization for power generation. Continuous energy supply is considered a crucial challenge for renewables, which can be achieved by diversifying the source or adding energy storage. The current study uses two different energy storage methods to ensure the continuous energy supply to achieve self-sufficiency as well for the community. Hydrogen energy subsystem is the second energy storage within the integrated system, which is the secondary energy storage that is used if heat is not sufficient to drive the power generation cycles. In order to make decisions for a better source utilization, ensure continuous power supply, and facilitate the operations of the components and energy storage subsystems according to the loads and source availability, an operational decision-making strategy is developed and applied within the spreadsheet model. In order to test the proposed system and the developed algorithm, a time-dependent analysis is carried out where hourly community load and hourly source data are employed. The analyses are carried out using the first and second laws of thermodynamics, primarily through energy and exergy aspects. The power cycle can generate power from heat within the temperature range of 160°C and 500°C, in between 6.12% and 37.2% of heat to power energy conversion efficiency. The overall energy and exergy efficiencies of the integrated system are found to be 31.29% and19.71% by considering the average meteorological data.

Economic and risk based optimal energy management of a multi-carrier energy system with water electrolyzing and steam methane reform technologies for hydrogen production

This paper proposes stochastic programming to optimal economic scheduling problems of a sustainable integrated energy system with renewable resources including power, natural gas, and hydrogen carriers. Hydrogen is considered a byproduct of electrical power and natural gas carriers using water electrolysis and steam methane reform process technologies. The main objective is to minimize the operation and emission costs while addressing uncertainties of electric/heating/cooling demands, electricity price, and renewable generation using scenario-based stochastic programming. To achieve a risk-hedging strategy, downside risk constraints are involved to minimize the risky scenarios portfolio. The whole problem is modeled as mixed-integer linear programming in GAMS optimization software. The analysis revealed that the expected operation cost without DRC is $ 577.52 and the expected risk-in-cost value for risky scenarios is $ 470.7. However, a conservative decision for reaching zero risk is costly and increases the expected cost up to $ 2901.1, which is not economically viable. A modest decision-making strategy compromises the cost and risk values where for decreasing the risk-in-cost to $ 288.7 (36.8%), the expected cost increased by to $1010.39 (75.95%). Moreover, the risk-averse decision-maker lowers the power sold to the grid, increases the use of water electrolyzing instead of the SMR process to reduce the environmental charges, and tries to increase flexibility with the help of hydrogen and battery storage.

A two-way coupled CHANS model for flood emergency management, with a focus on temporary flood defences

This study presents a novel Coupled Human And Natural Systems (CHANS) modelling framework that integrates a hydrodynamic model with an agent-based model at the memory level within a multi-GPU computing environment. This two-way coupled model captures real-time interactions between human activities and flood dynamics, with a focus on the deployment of temporary flood defences during the 2015 Desmond flood in Carlisle, UK. The findings reveal that temporary defences can significantly reduce flood inundation by 30% with early warnings and 15% through real-time decision-making, leading to financial savings of £30 million and £15 million, respectively. The study further explores the decision-making process for effective emergency flood management, emphasising the importance of early warnings and resources optimisation. The new CHANS model provides a valuable tool for testing and optimising emergency flood management strategies, highlighting the necessity of directly incorporating human activities into flood risk management.

Neuromechanical proficiency in elite performance decision-making: An event-related potential (ERP) analysis

This research focuses intensively on the neural mechanisms of action selection responses in elite athletes during high-stakes decision-making. It emphasizes the neuromechanical dimensions of these processes, contrasting the responses of expert and novice players within a theoretical framework of neural information processing in high-performance contexts. Utilizing advanced EEG technologies, including event-related potential (ERP) analysis, the study captures a comprehensive view of both behavioral and neurophysiological data. The central aim is to unravel the intricate neural underpinnings that distinguish elite athletes in their decision-making strategies. Key findings highlight: (1) Enhanced accuracy and swifter reaction times in elite athletes during the action selection phase; (2) Significant neurophysiological differences, marked by pronounced N1 peak amplitudes with prolonged latencies, reduced P2 peak amplitudes with stable latencies, decreased P3 peak amplitudes with reduced latencies, and increased average PSW amplitudes. These discoveries significantly advance our understanding of the neural foundations of expert decision-making in high-performance sports. This study not only sheds light on the cognitive and neural dynamics of elite sports performance but also provides a foundation for developing training and performance enhancement techniques in various high-stakes domains.

Ship autonomous collision avoidance decision from the perspective of navigation practice

Navigation safety is an eternal theme for ships, and it is an inherent strategic requirement for both transportation powers and maritime powers. With the development of intelligent ships, how to make intelligent decision-making results consistent with maritime practice is the key to navigation safety. This paper constructs a complex water area collision avoidance decision-making model that conforms to maritime practice, providing autonomous collision avoidance decision-making schemes for manned or unmanned ships. Firstly, based on the nonlinear velocity obstacle method, dynamic constraints such as ship maneuverability, target ship motion uncertainty, Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), and good seamanship are fully considered, and a safe collision avoidance action plan is solved in the velocity domain; Then, by introducing an overshoot coefficient to improve the line of sight method, a feasible collision avoidance decision plan is derived by searching for a resumption navigation plan based on the safe avoidance action plan; Finally, collision avoidance decision plan is optimized to achieve autonomous collision avoidance decision-making for ships. To verify the effectiveness of the proposed model, a series of case studies, which focus on different encounter situations are designed and executed, the results indicate that the outcome of the model consistently aligns with real-world scenarios, which is in line with maritime practice.

Role of Artificial Intelligence and Big Data in Sustainable Entrepreneurship

There is a pressing necessity to shift our economy, society, and culture to systems and actions that promote ecological sustainability. This radical transformation necessitates an equally radical transformation of resource utilization and decision-making strategies. Sustainable entrepreneurship (SE) is frequently touted as the solution to the triple-bottom-line challenges that businesses encounter; however, there are tangible constraints on its potential. SE is currently in the first phase of implementing technological frontier tools that provide empirical guidance throughout the entrepreneurial decision-making process. The potential for artificial intelligence (AI) to inform decision-making is advanced by Big Data (BD), which also establishes pathways to attain desired outcomes. The interactions between AI, BD, and SE have been generally under-studied thus far. The absence of work that consolidates and synthesizes this literature is the primary focus of this conceptual paper. We propose that AI and BD are capable of rapidly contributing to the continued sustainable development of the weak form, but they also hold significant potential for attaining the strong sustainability ideal. We present two proposals for the integration of AI and BD to inform and facilitate SE. Finally, we outline potential areas for future research. The core of human cosmology and ethics has always been the definition of his uniqueness. He ceased to be the species situated at the center of the universe, accompanied by the sun and stars, with the arrival of Copernicus and Galileo. He ceased to be the species that was created and specially endowed by God with soul and reason with the arrival of Darwin. With Freud, he ceased to be the species whose behavior could potentially be regulated by the rational mind. He has ceased to be the species that is uniquely capable of complex, intelligent manipulation of his environment as we begin to produce mechanisms that think and learn.

A Consumer Trust Assessment Model for Online Shopping Based on Fuzzy Fusion Decision-Making

With the rapid development of e-commerce, online shopping has become an indispensable part of people's daily lives. However, consumers often face trust issues during online shopping, such as product quality and seller integrity, which directly impact their shopping experience and purchasing decisions. Therefore, accurately assessing consumer trust has become a crucial task. This study first constructs a consumer trust assessment system, analyzing and selecting key factors related to consumer trust, and establishes a model for assessing consumer trust for online shopping. Subsequently, we propose an assessment method based on text mining and deep learning sentiment analysis techniques to extract consumer sentiment information from specified consumer reviews. Furthermore, through fuzzy decision-making fusion strategy, we integrate sentiment information from the dimensions of quality assurance, reliability, and responsiveness to enhance the accuracy of the assessment.