A supply chain network design for blood and its products using genetic algorithm: A case study of Turkey

Ağaç, Gökhan; Baki, Birdoğan; Ar, İlker Murat; Kahraman, Hamdi Tolga

doi:10.3934/jimo.2022179

Cited by 8 publications

(2 citation statements)

References 86 publications

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“…First proposed by Professor J. Holland of the University of Michigan in 1967, it is an efficient approach for solving optimization problems 18 . In recent years, genetic algorithms have found applications in a variety of fields such as materials 19 , mathematics 20 , and medicine 21 . The genetic algorithm operates in three key steps: 1) Selection, where superior individuals from the current population are chosen.…”

Section: Ga-gpr Algorithmmentioning

confidence: 99%

Optimizing Prediction of Bolt Support Drilling Pressure: A Hybrid Algorithm Approach to Screen Gaussian Process Time Series Regression Parameters

Liu

2023

Preprint

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The unpredictability of drilling pressure in bolt support systems has emerged as a significant constraint on support efficiency. Current research gaps exist in the field of machine learning for pre-drilling pressure prediction in bolt support and the selection method for key parameters (kernel function and historical points) in Gaussian processes. This study proposes a novel prediction method for bolt support drilling pressure, leveraging hybrid optimization algorithms to identify the key parameters in Gaussian process time series regression. Initially, the Gaussian process time series regression algorithm is modeled. Through data computation and simulation, it is observed that employing the Gaussian process time series algorithm for predicting the drilling pressure of bolt support results in substantial variation in the outcomes when different combinations of kernel functions and historical points are used. Therefore, it is essential to identify the optimal kernel function and the most suitable number of historical points before utilizing the Gaussian process time series algorithm for predicting drilling pressure. Subsequently, three hybrid optimization algorithms (GA-GPR, PSO-GPR, and ACA-GPR) are employed to iteratively optimize the two key parameters (kernel function and historical points) in Gaussian process time series regression. Among these, the PSO-GPR algorithm proves to be the most effective for identifying the kernel function and historical points of the key parameters in the Gaussian process time series algorithm when applied to the prediction of drilling pressure in bolt support. Remarkably, even with a small sample size and a limited number of iterations, PSO-GPR achieves 80% accuracy while reducing time consumption by 60%. Finally, a prediction system for drilling pressure in underground bolt support is established. The algorithm's generalization capability is verified through the prediction of actual drilling pressure. Thus, this study provides a robust and efficient method for predicting drilling pressure in bolt support systems, potentially enhancing support efficiency significantly.

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Section: Ga-gpr Algorithmmentioning

confidence: 99%

Optimizing Prediction of Bolt Support Drilling Pressure: A Hybrid Algorithm Approach to Screen Gaussian Process Time Series Regression Parameters

Liu

2023

Preprint

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“…Computational experiments were conducted to verify the effectiveness of the method using Mississippi as an example. Agac et al [7] designed a new blood supply chain network model using mixed-integer nonlinear programming and used a variational genetic algorithm (GA) to solve the problem. They demonstrated the usefulness of the model using the Eastern Black Sea region of the Turkish Red Crescent as an example.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the supply chain network planning problem using an improved genetic algorithm

Zhao,

Xie

2023

Int. J. Simul. Multidisci. Des. Optim.

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The planning problem of supply chain network is highly related to logistics cost and product quality. In this paper, for the optimization of supply chain network planning problem, an agricultural product supply chain network under the direct docking model between farmers and supermarkets was taken as an example to establish an agricultural product supply chain network planning model with the lowest cost as the objective. Then, an improved genetic algorithm (GA) was designed to solve the model. The analysis of the arithmetic example showed that compared with the traditional GA, the total cost obtained by the improved GA was lower, at 39,004.48 $, which was 6.5% less than that of the traditional GA; the solution result of the improved GA was also superior to that of other heuristic algorithms, such as particle swarm optimization and ant colony optimization. The experimental results demonstrate the optimization effectiveness of the improved GA for the supply chain network planning problem, and it can be applied in practice.

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Design of Intelligent Recognition English Translation Model Based on Improved GLR Algorithm

Wei,

Huang

2024

Application of Big Data, Blockchain, and Internet of Things for Education Informatization

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A supply chain network design for blood and its products using genetic algorithm: A case study of Turkey

Cited by 8 publications

References 86 publications

Optimizing Prediction of Bolt Support Drilling Pressure: A Hybrid Algorithm Approach to Screen Gaussian Process Time Series Regression Parameters

Optimizing Prediction of Bolt Support Drilling Pressure: A Hybrid Algorithm Approach to Screen Gaussian Process Time Series Regression Parameters

Optimization of the supply chain network planning problem using an improved genetic algorithm

Design of Intelligent Recognition English Translation Model Based on Improved GLR Algorithm

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