Multiple attribute decision making model and application to food safety risk evaluation.
Ontology highlight
ABSTRACT:
SUBMITTER: Ma L
PROVIDER: S-EPMC5736234 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
Publications
Multiple attribute decision making model and application to food safety risk evaluation.
Ma Lihua L Chen Hong H Chen Hong H Yan Huizhe H Yang Lifeng L Wu Lifeng L
PloS one 20171219 12
Decision making for supermarket food purchase decisions are characterized by network relationships. This paper analyzed factors that influence supermarket food selection and proposes a supplier evaluation index system based on the whole process of food production. The author established the intuitive interval value fuzzy set evaluation model based on characteristics of the network relationship among decision makers, and validated for a multiple attribute decision making case study. Thus, the pro ...[more]
Similar Datasets
Project description:The intuitionistic linguistic variables are easier to describe the fuzzy information which widely exists in the real world, and Bonferroni mean can capture the interrelationship of the individual arguments. However, the traditional Bonferroni mean can only process the crisp number. In this paper, we will extend Bonferroni mean to the intuitionistic linguistic environment and propose a multiple attribute decision making method with intuitionistic linguistic information based on the extended Bonferroni mean which can consider the interrelationship of the attributes. Firstly, score function and accuracy function of intuitionistic linguistic numbers are introduced. Then, an intuitionistic linguistic Bonferroni mean (ILBM) operator and an intuitionistic linguistic weighted Bonferroni mean (ILWBM) operator are developed, and some desirable characteristics of them are studied. At the same time, some special cases with respect to the parameters p and q in Bonferroni are analyzed. Based on the ILWBM operator, the approach to multiple attribute decision making with intuitionistic linguistic information is proposed. Finally, an illustrative example is given to verify the developed approach and to demonstrate its effectiveness.
| S-EPMC4089949 | biostudies-other
Project description:In recent years, the prevalence of sensory integration disorders in children in urban areas has increased. Most existing sensory integration treatments are located in hospital-based sensory integration units; however, medical resources are extremely limited, making it difficult to guarantee the appropriate treatment time and intervention results for many children. The concept of sensory integration therapy must be disseminated widely and correctly to meet these children's needs. Although most urban communities have a high number of children's spaces, these spaces require improvement. This study proposes the incorporation of the concept of sensory integration therapy into neighborhood open spaces for children to positively impact children's sensory development. The purpose of this study is to determine the effective facility factors of an occupational therapy room, translate them into a community facility design, clarify the categories and relative importance of each design attribute, and explore the design strategies of the children's facilities in neighborhood open spaces based on the sensory integration theory. This study investigates the importance of the sensory integration treatment level. The facilities in neighborhood open spaces for children can be considered systemic structures consisting of five partitioned units with different levels of importance among the synergistic components within each unit. These structures will enable children to experience sensory stimulation during daily outdoor play and will serve as preventive and therapeutic tools.
| S-EPMC8181119 | biostudies-literature
Project description:The selection and assessment process of appropriate robots became a more complex and complicated task due to various available alternatives and conflicting attributes which must take into consideration. Also, uncertainty which exists usually in the selection process is an unavoidable component that needs to be thoughtfully measured and traditional multi-attribute decision-making approaches failed to deal precisely with it. Since almost all decisions originate from subjective ordinal preferences, handling uncertainty using linguistic variables is also not enough. Thus, the objective of the current study is to present a new extended ordinal priority approach in the neutrosophic environment for the first time to select an appropriate robot. Since neutrosophic is one of the most effective and accommodating tools for handling uncertainty, thus, this method goes to transform linguistic information into triangular neutrosophic numbers using a new presented scale. This scale was used to determine the importance degree of attributes and alternatives regarding experts' opinions. Also, the score function of the triangular neutrosophic number is used for prioritizing attributes and alternatives. The experts in our proposed method have the same degree of importance, since each expert is a person with special skills and knowledge representing mastery of a particular subject. To measure the applicability and efficiency of the proposed approach, an experimental case study has been established for the robot selection problem of a new pharmaceutical city in Egypt for the first time. The source of data in this case study is experts, interviews, and questionnaires. Also, sensitivity and comparative analysis are further made for verifying the power of the proposed approach. The outcome of this study shows that the suggested approach for robot selection is quite helpful and has a great performance under uncertainty over classical and fuzzy ordinal priority approaches. Also, the suggested approach is less consumption of time and simpler than the fuzzy ordinal priority approach. Therefore, we recommend firms and governments to apply it for increasing product quality, hence the profitability of manufacturing industries and decrease needless costs.Supplementary informationThe online version contains supplementary material available at 10.1007/s40747-022-00721-w.
| S-EPMC9051802 | biostudies-literature
Project description:In this paper, a set of Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers (qROFNs) are presented, and a multiple attribute group decision making (MAGDM) method based on these operators is proposed. First, the operational rules of qROFNs based on the Dombi t-conorm and t-norm are introduced. A q-rung orthopair fuzzy Dombi partitioned Heronian mean (qROFDPHM) operator and its weighted form are then established in accordance with these rules. To reduce the negative effect of unreasonable attribute values on the aggregation results of these operators, a q-rung orthopair fuzzy Dombi power partitioned Heronian mean operator and its weighted form are constructed by combining qROFDPHM operator with the power average operator. A method to solve MAGDM problems based on qROFNs and the constructed operators is designed. Finally, a practical example is described, and experiments and comparisons are performed to demonstrate the feasibility and effectiveness of the proposed method. The demonstration results show that the method is feasible, effective, and flexible; has satisfying expressiveness; and can consider all the interrelationships among different attributes and reduce the negative influence of biased attribute values.
| S-EPMC6804965 | biostudies-literature
Project description:Several studies have examined the neural substrates of probabilistic decision-making, but few have systematically investigated the neural representations of the two objective attributes of probabilistic rewards, that is, the reward amount and the probability. Specifically, whether there are common or distinct neural activity patterns to represent the objective attributes and their association with the neural representation of the subjective valuation remains largely underexplored. We conducted two studies (nStudy1 = 34, nStudy2 = 41) to uncover distributed neural representations of the objective attributes and subjective value as well as their association with individual probability discounting rates. The amount and probability were independently manipulated to better capture brain signals sensitive to these two attributes and were presented simultaneously in Study 1 and successively in Study 2. Both univariate and multivariate pattern analyses showed that the brain activities in the superior parietal lobule (SPL), including the postcentral gyrus, were modulated by the amount of rewards and probability in both studies. Further, representational similarity analysis revealed a similar neural representation between these two objective attributes and between the attribute and valuation. Moreover, the SPL tracked the subjective value integrated by the hyperbolic function. Probability-related brain activations in the inferior parietal lobule were associated with the variability in individual discounting rates. These findings provide novel insights into a similar neural representation of the two attributes during probabilistic decision-making and perhaps support the common neural coding of stimulus objective properties and subjective value in the field of probabilistic discounting.
| S-EPMC10619403 | biostudies-literature
Project description:Making the best choice when faced with a chain of decisions requires a person to judge both anticipated outcomes and future actions. Although economic decision-making models account for both risk and reward in single-choice contexts, there is a dearth of similar knowledge about sequential choice. Classical utility-based models assume that decision-makers select and follow an optimal predetermined strategy, regardless of the particular order in which options are presented. An alternative model involves continuously reevaluating decision utilities, without prescribing a specific future set of choices. Here, using behavioral and functional magnetic resonance imaging (fMRI) data, we studied human subjects in a sequential choice task and use these data to compare alternative decision models of valuation and strategy selection. We provide evidence that subjects adopt a model of reevaluating decision utilities, in which available strategies are continuously updated and combined in assessing action values. We validate this model by using simultaneously acquired fMRI data to show that sequential choice evokes a pattern of neural response consistent with a tracking of anticipated distribution of future reward, as expected in such a model. Thus, brain activity evoked at each decision point reflects the expected mean, variance, and skewness of possible payoffs, consistent with the idea that sequential choice evokes a prospective evaluation of both available strategies and possible outcomes.
| S-EPMC3044871 | biostudies-other
Project description:Simple perceptual tasks have laid the groundwork for understanding the neurobiology of decision-making. Here, we examined this foundation to explain how decision-making circuitry adjusts in the face of a more difficult task. We measured behavioral and physiological responses of monkeys on a two- and four-choice direction-discrimination decision task. For both tasks, firing rates in the lateral intraparietal area appeared to reflect the accumulation of evidence for or against each choice. Evidence accumulation began at a lower firing rate for the four-choice task, but reached a common level by the end of the decision process. The larger excursion suggests that the subjects required more evidence before making a choice. Furthermore, on both tasks, we observed a time-dependent rise in firing rates that may impose a deadline for deciding. These physiological observations constitute an effective strategy for handling increased task difficulty. The differences appear to explain subjects' accuracy and reaction times.
| S-EPMC2453226 | biostudies-literature
Project description:BackgroundRisk-based decision making is increasingly recognized as key to support national blood policy makers and blood operators concerning the implementation of safety interventions, especially to address emerging infectious threats and new technology opportunities. There is an urgent need for practical decision support tools, especially for low- and middle-income countries that may not have the financial or technical capability to develop risk models. WHO supported the development of such a tool for blood safety. The tool enables users to perform both a quantitative Multi-Criteria Decision Assessment and a novel step-by-step qualitative assessment.Study design and methodsThis paper summarizes the content, functionalities, and added value of the new WHO tool. A fictitious case study of a safety intervention to reduce the risk of HIV transmission by transfusion was used to demonstrate the use and usefulness of the tool.ResultsApplication of the tool highlighted strengths and weaknesses of both the quantitative and qualitative approaches. The quantitative approach facilitates assessment of the robustness of the decision but lacks nuances and interpretability especially when multiple constraints are taken into consideration. Conversely, while unable to provide an assessment of robustness, the step-by-step qualitative approach helps structuring the thought process and argumentation for a preferred intervention in a systematic manner.ConclusionThe relative strengths and weaknesses of the quantitative and step-by-step qualitative approach to risk-based decision making are complementary and mutually enhancing. A combination of the two approaches is therefore advisable to support the selection of appropriate blood safety interventions for a particular setting.
| S-EPMC7898802 | biostudies-literature
Project description:One of the interesting research questions in multi-attribute decision-making is what affects the consideration of shared information (i.e., common features) between two alternatives. Previous studies have suggested two approaches (bottom-up and top-down) in finding what characteristics of common features affect their consideration. Two bottom-up factors (salience and interdependence) were found, but no top-down factors were discovered. In the current study, we followed the top-down approach and investigated how subjective importance (SI) of a common feature affects its consideration. In two studies, we consistently found that, on both the general and individual level, the level of consideration increased with the SI of the common feature. This result provided a new explanation for the effect of common feature consideration and its individual difference; it also provided insights in explaining the underlying process of multi-attribute decision making.
| S-EPMC7841338 | biostudies-literature
Project description:BackgroundRisk stratification is challenging in conditions, such as chest pain, shortness of breath and syncope, which can be the manifestation of many possible underlying diseases. In these cases, decision tools are unlikely to accurately identify all the different adverse events related to the possible etiologies. Attribute matching is a prediction method that matches an individual patient to a group of previously observed patients with identical characteristics and known outcome. We used syncope as a paradigm of clinical conditions presenting with aspecific symptoms to test the attribute matching method for the prediction of the personalized risk of adverse events.MethodsWe selected the 8 predictor variables common to the individual-patient dataset of 5 prospective emergency department studies enrolling 3388 syncope patients. We calculated all possible combinations and the number of patients in each combination. We compared the predictive accuracy of attribute matching and logistic regression. We then classified ten random patients according to clinical judgment and attribute matching.ResultsAttribute matching provided 253 of the 384 possible combinations in the dataset. Twelve (4.7%), 35 (13.8%), 50 (19.8%) and 160 (63.2%) combinations had a match size ≥50, ≥30, ≥20 and <10 patients, respectively. The AUC for the attribute matching and the multivariate model were 0.59 and 0.74, respectively.ConclusionsAttribute matching is a promising tool for personalized and flexible risk prediction. Large databases will need to be used in future studies to test and apply the method in different conditions.
| S-EPMC7080223 | biostudies-literature