Hunger games search optimization with deep learning model for sustainable supply chain management

Abstract The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zheng Xu [verfasserIn] Deepak Kumar Jain [verfasserIn] S. Neelakandan [verfasserIn] Jemal Abawajy [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Supply chain management Agricultural and food supply Deep learning Metaheuristics Data analytics Sustainability

Übergeordnetes Werk:	In: Discover Internet of Things - Springer, 2021, 3(2023), 1, Seite 17
Übergeordnetes Werk:	volume:3 ; year:2023 ; number:1 ; pages:17

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1007/s43926-023-00040-7

Katalog-ID:	DOAJ091834910

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callnumber-first	T - Technology
author	Zheng Xu
spellingShingle	Zheng Xu misc TK7885-7895 misc QA76.75-76.765 misc Supply chain management misc Agricultural and food supply misc Deep learning misc Metaheuristics misc Data analytics misc Sustainability misc Computer engineering. Computer hardware misc Computer software Hunger games search optimization with deep learning model for sustainable supply chain management
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topic_title	TK7885-7895 QA76.75-76.765 Hunger games search optimization with deep learning model for sustainable supply chain management Supply chain management Agricultural and food supply Deep learning Metaheuristics Data analytics Sustainability
topic	misc TK7885-7895 misc QA76.75-76.765 misc Supply chain management misc Agricultural and food supply misc Deep learning misc Metaheuristics misc Data analytics misc Sustainability misc Computer engineering. Computer hardware misc Computer software
topic_unstemmed	misc TK7885-7895 misc QA76.75-76.765 misc Supply chain management misc Agricultural and food supply misc Deep learning misc Metaheuristics misc Data analytics misc Sustainability misc Computer engineering. Computer hardware misc Computer software
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title	Hunger games search optimization with deep learning model for sustainable supply chain management
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title_full	Hunger games search optimization with deep learning model for sustainable supply chain management
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title_sort	hunger games search optimization with deep learning model for sustainable supply chain management
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title_auth	Hunger games search optimization with deep learning model for sustainable supply chain management
abstract	Abstract The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use.
abstractGer	Abstract The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use.
abstract_unstemmed	Abstract The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use.
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title_short	Hunger games search optimization with deep learning model for sustainable supply chain management
url	https://doi.org/10.1007/s43926-023-00040-7 https://doaj.org/article/f84a92d14fae4374b2ada5cf56df5f36 https://doaj.org/toc/2730-7239
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author2	Deepak Kumar Jain S. Neelakandan Jemal Abawajy
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doi_str	10.1007/s43926-023-00040-7
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up_date	2024-07-03T22:32:59.396Z
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