Solving product allocation problem (PAP) by using ANN and clustering

Lorenc, Augustyn; Kuźnar, Małgorzata; Lerher, Tone

doi:10.5937/fme2101206l

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…Figure 8 a,b shows samples of images before and after thresholding. After thresholding, create a density heat map 46 by applying an RGB colour scheme to the area of interest. (i.e., lung).…”

Section: Datasets and Methodologymentioning

confidence: 99%

“…After thresholding, create a density heat map 46 by applying an RGB colour scheme to the area of interest. (i.e., lung).…”

Section: Datasets and Methodologymentioning

confidence: 99%

“…These colors can be used to highlight features in the given images, such as bone density, tissue density, or blood flow given in 27 . In the case of COVID-19, infected individuals often exhibit a fever, which can be detected using thermal imaging and represented as a heat-map [46][47][48] . Heat-maps are graphical representations used to identify the region of interest in data by observing how colors changed.…”

Section: Contributionmentioning

confidence: 99%

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COVID-19 infection segmentation using hybrid deep learning and image processing techniques

Antar,

Abd El-Sattar,

Abdel-Rahman

et al. 2023

Sci Rep

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The coronavirus disease 2019 (COVID-19) epidemic has become a worldwide problem that continues to affect people’s lives daily, and the early diagnosis of COVID-19 has a critical importance on the treatment of infected patients for medical and healthcare organizations. To detect COVID-19 infections, medical imaging techniques, including computed tomography (CT) scan images and X-ray images, are considered some of the helpful medical tests that healthcare providers carry out. However, in addition to the difficulty of segmenting contaminated areas from CT scan images, these approaches also offer limited accuracy for identifying the virus. Accordingly, this paper addresses the effectiveness of using deep learning (DL) and image processing techniques, which serve to expand the dataset without the need for any augmentation strategies, and it also presents a novel approach for detecting COVID-19 virus infections in lung images, particularly the infection prediction issue. In our proposed method, to reveal the infection, the input images are first preprocessed using a threshold then resized to 128 × 128. After that, a density heat map tool is used for coloring the resized lung images. The three channels (red, green, and blue) are then separated from the colored image and are further preprocessed through image inverse and histogram equalization, and are subsequently fed, in independent directions, into three separate U-Nets with the same architecture for segmentation. Finally, the segmentation results are combined and run through a convolution layer one by one to get the detection. Several evaluation metrics using the CT scan dataset were used to measure the performance of the proposed approach in comparison with other state-of-the-art techniques in terms of accuracy, sensitivity, precision, and the dice coefficient. The experimental results of the proposed approach reached 99.71%, 0.83, 0.87, and 0.85, respectively. These results show that coloring the CT scan images dataset and then dividing each image into its RGB image channels can enhance the COVID-19 detection, and it also increases the U-Net power in the segmentation when merging the channel segmentation results. In comparison to other existing segmentation techniques employing bigger 512 × 512 images, this study is one of the few that can rapidly and correctly detect the COVID-19 virus with high accuracy on smaller 128 × 128 images using the metrics of accuracy, sensitivity, precision, and dice coefficient.

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Section: Datasets and Methodologymentioning

confidence: 99%

“…After thresholding, create a density heat map 46 by applying an RGB colour scheme to the area of interest. (i.e., lung).…”

Section: Datasets and Methodologymentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

See 1 more Smart Citation

COVID-19 infection segmentation using hybrid deep learning and image processing techniques

Antar,

Abd El-Sattar,

Abdel-Rahman

et al. 2023

Sci Rep

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“…For the space assignment, some authors based on the Product Allocation Problem (PAP). PAP consists on to locate the SKUs inside the warehouse slots by reducing cost, operation time, and used slots and increasing the service level (Lorenc, Kuźnar & Lerher, 2021;Lorenc & Lerher, 2020;Scheffler, Wesselink & Buscher, 2021).…”

Section: Research Backgroundmentioning

confidence: 99%

Warehouse management optimization using a sorting-based slotting approach

Duque-Jaramillo,

Cogollo-Flórez,

Gómez-Marín

et al. 2024

JIEM

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Purpose: Slotting is one of the main operations in warehouse management. It is based on the efficient allocation of slots for stock-keeping units (SKUs). Order picking and slotting represent a high percentage of total logistics costs; therefore, improving these activities leads to significant savings in the overall performance. This paper aims to develop an allocation model integrating SKUs physical variables, warehousing design, and operation (dimensions, layout, material handling equipment), and heterogeneous product demand.Design/methodology/approach: The modeling methodology considers two phases. First, an integer linear programming model for warehousing spaces assignment for SKUs considering priority and required orders is developed. Then, the total operation times using different strategies are calculated.Findings: The effectiveness of the model was verified through simulation using historical data. The results showed that the best performance in the total time of the slotting operation is achieved by using the ABC as a criterion for the classification of the SKUs and by sequentially assigning the row, level, column, and the section.Practical implications: This approach can be adapted to different industrial sectors and serves as a basis for more robust models regarding the number of constraints or the incorporation of additional warehouse operating parameters.Originality/value: The most important contribution of this work is the development of a flexible and adaptable methodology to changes in the operation to improve the efficiency of storage management through slotting. Future work includes other objective functions of sustainable operations and uncertainty treatment techniques.

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