In manufacturing, the essential product characteristics are often created through multiple stages. Coupling product data obtained through inspection and controllers based on decision models with ...prediction capabilities enables quality control loops, enhancing both feedback and feedforward mechanisms. This paper proposes a methodology to merge the formulation of feedback and feedforward quality control loops into a performance evaluation model for multi-stage manufacturing systems. This approach evaluates quality control loop impacts system-wide, aiding in configuring and reconfiguring quality gates. A case study illustrates how allocating inspection technologies and efficient decision models improves overall system performance through effective feedback and feedforward control loops.
Companies and organizations often use manuals and guidelines to communicate and execute operational decisions. Decision Model and Notation (DMN) models can be used to model and automate these ...decisions. Modeling a decision from a textual source, however, is a time intensive and complex activity hence a need for shorter modeling times. This paper studies how NLP deep learning techniques can extract decision models from text faster. In this paper, we study and evaluate an automatic sentence classifier and a decision dependency extractor using NLP deep learning models (BERT and Bi-LSTM-CRF). A large labeled and tagged dataset was collected from real use cases to train these models. We conclude that BERT can be used for the (semi)-automatic extraction of decision models from text.
•First time the use of deep learning is investigated to extract DMN models from text.•Deep learning can be used to classify sentences describing logic or dependencies.•Deep learning can be used to extract decision dependencies from sentences.•First labeled and tagged dataset is made available for decision model extraction.•First decision tool extraction from text is made available to everyone.
This paper considers a green closed-supply chain consisting of a manufacturer and a retailer. A Stackelberg game model of centralized decision-making and decentralized decision-making with ...manufacturer’s fairness concern was constructed based on the consideration of retailer’s sales effort. The decision-making of supply chain members under the above two situations and their reasons are analyzed in depth. According to the model, a green closed-loop supply chain with profit sharing contract coordination fairness is designed. Finally, the correctness of the model is verified by numerical simulation. We generate our findings from three aspects, as follows: when the manufacturer has fair concern behavior, it is not conducive to the environmental performance of green products, resulting in waste of resources, but also forcing retailers to reduce sales efforts and increase the retail price of products. Finally, the benefits of green closed-loop supply chain are seriously damaged. The profit-sharing contract could improve the relationship between members of the supply chain to achieve sustainable economic and environmental development.
•The manufacturer’s fairness concern behavior is considered in the green closed loop supply chain.•The effects of fairness concern on retailer’s sales effort, product green degree, recycling rate and product pricing decisions were analyzed.•A profit-sharing contract is employed to realized the pareto improvement of green closed-loop supply chain with fairness concern behavior.•The research will help inspire enterprises to promote green production, and guide consumers to green consumption.
•Integrated Decision Model: The research introduces a decision model for optimizing the operation of cascade hydropower stations, balancing power generation and ecological considerations.•Jinsha ...River Case Study: The model’s application in the lower Jinsha River uncovers significant conflicts between energy production and ecological preservation objectives.•Comprehensive Decision-Making: The study underscores the necessity of a holistic decision-making strategy that integrates multiple objectives and utilizes advanced algorithms to promote sustainable river management and align economic and ecological interests.
The establishment and functioning of cascade hydropower stations have significantly altered the natural state of rivers, leading to increasingly severe ecological impacts downstream. To mitigate the adverse effects of cascade reservoir impoundment on river ecosystems and achieve the multi-objective goals of hydropower development and environment protection, this study presents an integrated decision model for optimizing the operation of cascade hydropower stations, utilizing the lower Jinsha River as a case illustration. The proposed model comprises three main components: ecological flow calculation, establishment of a multi-objective optimization model, and scheme evaluation. The ecological flow threshold for the river was determined using the Tennant method. The multi-objective optimization model was formulated with objectives including power generation, ecological flow shortage, and navigation disruption days. The NSGA-II algorithm was employed to resolve the optimization framework. The improved TOPSIS method was adopted for evaluating the operation schemes. The case study conducted in the downstream region of the Jinsha River revealed significant conflicts between the objectives of power generation and ecological conservation. Moreover, different typical years exerted a considerable influence on determining the optimal operation scheme. The proposed integrated decision model provides a scientific framework for guiding the water resource dispatching of cascade hydropower stations and achieving the harmonization of economic and ecological benefits. Overall, this research contributes to the academic field by addressing the complex difficulties related to optimizing the operation of cascade hydropower stations while considering ecological concerns. The findings underscore the importance of adopting a comprehensive decision-making approach that integrates multiple objectives and utilizes advanced algorithms for sustainable river management.
Waste incineration power projects (WIPP) may face suspicion and resistance due to potential environmental and health risks. Information behavior has become a crucial way for residents to manage the ...risks. By integrating the risk information seeking and processing (RISP) model with the protective action decision model (PADM), the study develops a four-stage model that incorporates information acquisition and perceived benefits to understand the determinants of residents’ information behaviors regarding WIPP. A total of 1726 respondents were interviewed. The results indicate that 77.5 % respondents frequently use social media. Of the twenty-five hypothesized paths, twenty-one were found to be significant. Information acquisition from social media plays a larger predictive role than that from official media. Perceived risks, benefits, and knowledge positively predict information sharing, systematic processing, and information seeking, respectively. Perceived knowledge has the strongest direct influence on information insufficiency (β = 0.24, p < 0.001) with the explained variance of systematic processing being the highest (R2 = 0.51). Interesting, the relationship between information insufficiency and information sharing, as well as between relevant channel beliefs and information sharing, are negative. Theoretical insights into extending the PADM and the RISP model are provided, as well as managerial implications for risk communication about WIPP.
The research framework for residents’ information behaviors regarding waste incineration power projects. Display omitted
•This study integrates the RISP model and the PADM to present a four-stage research model of information behaviors.•Information acquisition from social media plays a larger predictive role than that from official media.•The more residents are aware of the project, the more they realize it provides more benefits and poses fewer risks.•This study provides managerial implications for risk communication about WIPP.
Poor air quality is the largest environmental health risk in England. In the West Midlands, UK, ∼2.9 million people are affected by air pollution with an average loss in life expectancy of up to 6 ...months. The 2021 Environment Act established a legal framework for local authorities in England to develop regional air quality plans, generating a policy need for predictive environmental impact assessment tools. In this context, we developed a novel Air Quality Lifecourse Assessment Tool (AQ-LAT) to estimate electoral ward-level impacts of PM2.5 and NO2 exposure on outcomes of interest to local authorities, namely morbidity (asthma, coronary heart disease (CHD), stroke, lung cancer), mortality, and associated healthcare costs. We apply the Tool to assess the health economic burden of air pollutant exposure and estimate benefits that would be generated by meeting WHO 2021 Global Air Quality Guidelines (AQGs) (annual average concentrations) for NO2 (10 μg/m3) and PM2.5 (5 μg/m3) in the West Midlands Combined Authority Area.
All West Midlands residents live in areas which exceed WHO AQGs, with 2070 deaths, 2070 asthma diagnoses, 770 CHD diagnoses, 170 lung cancers and 650 strokes attributable to air pollution exposure annually. Reducing PM2.5 and NO2 concentrations to WHO AQGs would save 10,700 lives reducing regional mortality by 1.8%, gaining 92,000 quality-adjusted life years (QALYs), and preventing 20,500 asthma, 7400 CHD, 1400 lung cancer, and 5700 stroke diagnoses, with economic benefits of £3.2 billion over 20 years. Significantly, we estimate 30% of QALY gains relate to reduced disease burden. The AQ-LAT has major potential to be replicated across local authorities in England and applied to inform regional investment decisions.
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•Local government has an essential role in delivering air quality policy actions.•We developed an open-access health impact tool for regional policy appraisal.•We use the tool to estimate health impacts of NO2 and PM2.5 in West Midlands, UK.•All areas of the West Midlands exceed WHO Global Air Quality Guidelines.•Achieving WHO Guidelines would reduce regional mortality by up to 2%.
Racial stereotypes are commonly activated by informational cues that are detectable in people's faces. Here, we used a sequential priming task to examine whether and how the salience of emotion ...(angry/scowling vs. happy/smiling expressions) or apparent race (Black vs. White) information in male face primes shapes racially biased weapon identification (gun vs. tool) decisions. In two experiments (N
= 546) using two different manipulations of facial information salience, racial bias in weapon identification was weaker when the salience of emotion expression versus race was heightened. Using diffusion decision modeling, we tested competing accounts of the cognitive mechanism by which the salience of facial information moderates this behavioral effect. Consistent support emerged for an initial bias account, whereby the decision process began closer to the "gun" response upon seeing faces of Black versus White men, and this racially biased shift in the starting position was weaker when emotion versus race information was salient. We discuss these results vis-à-vis prior empirical and theoretical work on how facial information salience moderates racial bias in decision-making.
Blockchain technology has received significant attention recently, as it offers a reliable decentralized infrastructure for all kinds of business transactions. Software-producing organizations are ...increasingly considering blockchain technology for inclusion into their software products. Selecting the best fitting blockchain platform requires the assessment of its functionality, adaptability, and compatibility to the existing software product. Novice software developers and architects are not experts in every domain, so they should either consult external experts or acquire knowledge themselves. The decision-making process gets more complicated as the number of decision-makers, alternatives, and criteria increases. Hence, a decision model is required to externalize and organize knowledge regarding the blockchain platform selection context. Recently, we designed a decision support system to use such decision models to support decision-makers with their technology selection problems in software production. In this article, we introduce a decision model for the blockchain platform selection problem. The decision model has been evaluated through three real-world case studies at three software-producing organizations. The case-study participants asserted that the approach provides significantly more insight into the blockchain platform selection process, provides a richer prioritized option list than if they had done their research independently, and reduces the time and cost of the decision-making process.
Past seasonal influenza epidemics and vaccination experience may affect individuals' decisions on whether to be vaccinated or not, decisions that may be constantly reassessed in relation to recent ...influenza related experience. To understand the potentially complex interaction between experience and decisions and whether the vaccination rate is likely to reach a critical coverage level or not, we construct an adaptive‐decision model. This model is then coupled with an influenza vaccination dynamics (SIRV) model to explore the interaction between individuals' decision‐making and an influenza epidemic. Nonlinear least squares estimation is used to obtain the best‐fit parameter values in the SIRV model based on data on new influenza‐like illness (ILI) cases in Texas. Uncertainty and sensitivity analyses are then carried out to determine the impact of key parameters of the adaptive decision‐making model on the ILI epidemic. The results showed that the necessary critical coverage rate of ILI vaccination could not be reached by voluntary vaccination. However, it could be reached in the fourth year if mass media reports improved individuals' memory of past vaccination experience. Individuals' memory of past vaccination experience, the proportion with histories of past vaccinations and the perceived cost of vaccination are important factors determining whether an ILI epidemic can be effectively controlled or not. Therefore, health authorities should guide people to improve their memory of past vaccination experience through media reports, publish timely data on annual vaccination proportions and adjust relevant measures to appropriately reduce vaccination perceived cost, in order to effectively control an ILI epidemic.
Sequential sampling models assume that people make speeded decisions by gradually accumulating noisy information until a threshold of evidence is reached. In cognitive science, one such model--the ...diffusion decision model--is now regularly used to decompose task performance into underlying processes such as the quality of information processing, response caution, and a priori bias. In the cognitive neurosciences, the diffusion decision model has recently been adopted as a quantitative tool to study the neural basis of decision making under time pressure. We present a selective overview of several recent applications and extensions of the diffusion decision model in the cognitive neurosciences.
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