A decision-making tool for the determination of the distribution center location in a humanitarian logistics network

Taouktsis, Xenofon; Zikopoulos, Christos

doi:10.1016/j.eswa.2023.122010

Cited by 9 publications

(2 citation statements)

References 88 publications

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“…Faster and more affordable computing resources have facilitated rapid convergence, making deep learning (DL) more accessible. The widespread availability of data, along with improved algorithms, enhances the value of these networks, especially in applications like chatbots for businesses [34,35]. These networks, however, demand substantial computational power and extensive data sets.…”

Section: Deep Dnnsmentioning

confidence: 99%

Neuromorphic Photonics Circuits: Contemporary Review

Kutluyarov,

Zakoyan,

Voronkov

et al. 2023

Nanomaterials

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Neuromorphic photonics is a cutting-edge fusion of neuroscience-inspired computing and photonics technology to overcome the constraints of conventional computing architectures. Its significance lies in the potential to transform information processing by mimicking the parallelism and efficiency of the human brain. Using optics and photonics principles, neuromorphic devices can execute intricate computations swiftly and with impressive energy efficiency. This innovation holds promise for advancing artificial intelligence and machine learning while addressing the limitations of traditional silicon-based computing. Neuromorphic photonics could herald a new era of computing that is more potent and draws inspiration from cognitive processes, leading to advancements in robotics, pattern recognition, and advanced data processing. This paper reviews the recent developments in neuromorphic photonic integrated circuits, applications, and current challenges.

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Section: Deep Dnnsmentioning

confidence: 99%

Neuromorphic Photonics Circuits: Contemporary Review

Kutluyarov,

Zakoyan,

Voronkov

et al. 2023

Nanomaterials

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“…In situations such as natural disasters, the urgency of the context elevates transportation efficiency to a paramount concern. Xenofon and Christos [ 21 ] employed a blend of classic heuristic algorithms, forecasting models, and deep neural networks to optimize the selection of distribution center locations. This approach is designed to swiftly expedite the delivery of aid materials to affected areas.…”

Section: Introductionmentioning

confidence: 99%

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

Ma,

Zheng,

Liang

et al. 2024

Sensors

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The last-mile logistics in cities have become an indispensable part of the urban logistics system. This study aims to explore the effective selection of last-mile logistics nodes to enhance the efficiency of logistics distribution, strengthen the image of corporate distribution, further reduce corporate operating costs, and alleviate urban traffic congestion. This paper proposes a clustering-based approach to identify urban logistics nodes from the perspective of geographic information fusion. This method comprehensively considers several key indicators, including the coverage, balance, and urban traffic conditions of logistics distribution. Additionally, we employed a greedy algorithm to identify secondary nodes around primary nodes, thus constructing an effective nodal network. To verify the practicality of this model, we conducted an empirical simulation study using the logistics demand and traffic conditions in the Xianlin District of Nanjing. This research not only identifies the locations of primary and secondary logistics nodes but also provides a new perspective for constructing urban last-mile logistics systems, enriching the academic research related to the construction of logistics nodes. The results of this study are of significant theoretical and practical importance for optimizing urban logistics networks, enhancing logistics efficiency, and promoting the improvement of urban traffic conditions.

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Facility location problems in supply chain operations

Saldanha-da-Gama

2024

Reference Module in Social Sciences

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A decision-making tool for the determination of the distribution center location in a humanitarian logistics network

Cited by 9 publications

References 88 publications

Neuromorphic Photonics Circuits: Contemporary Review

Neuromorphic Photonics Circuits: Contemporary Review

Logistics Center Selection and Logistics Network Construction from the Perspective of Urban Geographic Information Fusion

Facility location problems in supply chain operations

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