Self-assessment of parallel network systems with intuitionistic fuzzy data: a case study

Ameri, Zahra; Sana, Shib Sankar; Sheikh, Reza

doi:10.1007/s00500-019-03835-5

Cited by 20 publications

(21 citation statements)

References 13 publications

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“…ese researchers in series structure, by using the data from Puri and Yadav's study [31], evaluated 16 hospital units using triangular fuzzy data for two inputs and two outputs. In parallel structure, they used the data of Ameri et al's study [53], for evaluating 8 hospital units, including 3 inputs with crisp data and 4 outputs with fuzzy TIFNs.…”

Section: Discussionmentioning

confidence: 99%

“…In another study, Ameri et al [53] presented a model of NDEA in parallel structure and in the constant returns to scale for TIFNs and using the expected value transformed the model into a crisp linear programming model. Puri and Yadav [31], in their study, developed models to measure optimistic and pessimistic efficiencies of each DMU in intuitionistic fuzzy environment.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, in the proposed model of Ameri et al [53], a selfassessment model of NDEA in intuitionistic fuzzy environment has been developed to measure the efficiency of the parallel system, at the time interval when some inputs and outputs are intuitionistic fuzzy in nature. While in the proposed model of the present study, due to all intuitionistic fuzzy data, the variables of the model including inputs and outputs and intermediates and weights can be evaluated in uncontrollable conditions.…”

Section: Discussionmentioning

confidence: 99%

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Designing an Intuitionistic Fuzzy Network Data Envelopment Analysis Model for Efficiency Evaluation of Decision-Making Units with Two-Stage Structures

Javaherian

Hamzehee

Tooranloo

2021

Advances in Fuzzy Systems

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Data envelopment analysis (DEA) is a powerful tool for evaluating the efficiency of decision-making units for ranking and comparison purposes and to differentiate efficient and inefficient units. Classic DEA models are ill-suited for the problems where decision-making units consist of multiple stages with intermediate products and those where inputs and outputs are imprecise or nondeterministic, which is not uncommon in the real world. This paper presents a new DEA model for evaluating the efficiency of decision-making units with two-stage structures and triangular intuitionistic fuzzy data. The paper first introduces two-stage DEA models, then explains how these models can be modified with intuitionistic fuzzy coefficients, and finally describes how arithmetic operators for intuitionistic fuzzy numbers can be used for a conversion into crisp two-stage structures. In the end, the proposed method is used to solve an illustrative numerical example.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Designing an Intuitionistic Fuzzy Network Data Envelopment Analysis Model for Efficiency Evaluation of Decision-Making Units with Two-Stage Structures

Javaherian

Hamzehee

Tooranloo

2021

Advances in Fuzzy Systems

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“…Important clinical aspects of hospital internments such as the gravity of diseases, flexibility for resource transfer among the medical specialties, and substitution potential can orientate decisions of managers and policymakers closer to each health unit reality. In addition, the usage of fuzzy data for the assessment of network system such as a hospital can aid valuable support [ 48 ]. This analysis can provide a direction for public administrations to perform some of their strategies based on an objective quantitative production capacity measure and support subjective decision-making with the inclusion of such aspects.…”

Section: Discussionmentioning

confidence: 99%

A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak

Nepomuceno

Silva

Nepomuceno

et al. 2020

Journal of Healthcare Engineering

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Data envelopment analysis (DEA) is a powerful nonparametric engineering tool for estimating technical efficiency and production capacity of service units. Assuming an equally proportional change in the output/input ratio, we can estimate how many additional medical resource health service units would be required if the number of hospitalizations was expected to increase during an epidemic outbreak. This assessment proposes a two-step methodology for hospital beds vacancy and reallocation during the COVID-19 pandemic. The framework determines the production capacity of hospitals through data envelopment analysis and incorporates the complexity of needs in two categories for the reallocation of beds throughout the medical specialties. As a result, we have a set of inefficient healthcare units presenting less complex bed slacks to be reduced, that is, to be allocated for patients presenting with more severe conditions. The first results in this work, in collaboration with state and municipal administrations in Brazil, report 3772 beds feasible to be evacuated by 64% of the analyzed health units, of which more than 82% are moderate complexity evacuations. The proposed assessment and methodology can provide a direction for governments and policymakers to develop strategies based on a robust quantitative production capacity measure.

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“…LSM was proved to be trainable to accomplish speech recognition [26], but it is difficult to extract the specific state information of a complex LSM. Some researchers have tried to build a hybrid system to deal with complex tasks, such as building a hybrid artificial neural network for predicting stock price [28], using graph network to improve the visual reasoning task [29], using the portfolio theory in supply chain management [30], implementing self-assessment technique for parallel network systems [31]. However, these studies mainly focus on single tasks.…”

Section: Introductionmentioning

confidence: 99%

Hybrid neural state machine for neural network

Tian

Wu²,

2021

Sci. China Inf. Sci.

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The integration of computer-science-oriented and neuroscience-oriented approaches is believed to be a promising way for the development of artificial general intelligence (AGI). Recently, a hybrid Tianjic chip that integrates both approaches has been reported, providing a general platform to facilitate the research of AGI. The control algorithm for handling various neural networks is the key to this platform; however, it is still primitive. In this work, we propose a hybrid neural state machine (H-NSM) framework that can efficiently cooperate with artificial neural networks and spiking neural networks and control the workflows to accomplish complex tasks. The H-NSM receives input from different types of networks, makes decisions according to the fusing of various information, and sends control signals to the sub-network or actuator. The H-NSM can be trained to adapt to context-aware tasks or sequential tasks, thereby improving system robustness. The training algorithm works correctly even if only 50% of the forced state information is provided. It achieved performance comparable to the optimum algorithm on the Tower of Hanoi task and achieved multiple tasks control on a self-driving bicycle. After only 50 training epochs, the transfer accuracy reaches 100% in the test case. It proves that H-NSM has the potential to advance control logic for hybrid systems, paving the way for designing complex intelligent systems and facilitating the research towards AGI.

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Self-assessment of parallel network systems with intuitionistic fuzzy data: a case study

Cited by 20 publications

References 13 publications

Designing an Intuitionistic Fuzzy Network Data Envelopment Analysis Model for Efficiency Evaluation of Decision-Making Units with Two-Stage Structures

Designing an Intuitionistic Fuzzy Network Data Envelopment Analysis Model for Efficiency Evaluation of Decision-Making Units with Two-Stage Structures

A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak

Hybrid neural state machine for neural network

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