Revenue Model of Supply Chain by Internet of Things Technology

Cai, Shaohong; Wang, Xiaoguang; Zhao, Yuanjun

doi:10.1109/access.2018.2888952

Cited by 12 publications

(6 citation statements)

References 48 publications

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“…Many previous studies have utilized IoT in two-level multi-product supply chains, concluding that the effective use of IoT can optimize multi-product supply chains. The authors demonstrated that the proper deployment of information processing leads to better information dissemination throughout the supply chain [31]. Tian in 2016 discussed the priority of developing and using RFID and blockchain technologies.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

Mohammadi,

Sajadi,

Najafi

et al. 2024

Mathematics

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With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.

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Section: Literature Reviewmentioning

confidence: 99%

Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

Mohammadi,

Sajadi,

Najafi

et al. 2024

Mathematics

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“…On the basis of their power or the cost function, certain best among initial countries will be imperialists that begin taking control of the other countries (the colonies). Every colony is attracted to an imperialist that is based on the power [19]. The colonies for every imperialist is expressed as (1):…”

Section: Imperialist Competitive Algorithm (Ica)mentioning

confidence: 99%

Optimization of Multi Hop Data Aggregation in Medical Iot Using Trust Based Secure Routing

Sunithanandhini¹,

Natarajan

Ramprasath³

2022

Preprint

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The concept of the Internet of Things (IoT) consists of various sensing devices that have been subject to the threats that stem from an increased dependence on the technologies of communication. The IoT is the concept of healthcare is the main ingredient in the provision of medical facilities to patients and also facilitates both hospitals and doctors. The system proposed contained different medical devices like the sensors and the applications that are mobile-based or web-based. These communicate by means of the devices connected through the network that helps in monitoring and recording medical information and health data of the patients. The IoT was characterized by distributed topology and limited energy. For this scenario, there is a routing algorithm that is energy-saving and secure playing a major role to ensure IoT performance. The aggregation of security data has a crucial role in reducing the transmission of data and further in prolonging the lifetime of Wireless Sensor Network (WSN). When there was a threat to node security, the nodes had generated different data errors resulting in trouble to the measures of security. Owing to the difference in standards and their communication stacks, the traditional mechanisms to security were dependent on the trust management for functioning. For the purpose of this work, there was a multi-hop routing model used in medical IoT that was proposed. For balancing of consumption of energy and trust, routing from the source to the destination has to be taken into consideration for the appraisal of trust as opposed to the destination peers. At the same time, the consumption of energy has to be one major criterion. There was an Imperialist Competitive Algorithm (ICA) with Harmony Search (HS) that can balance the consumption of energy for the reduction of trust cost. This was based on a socio-politically inspired strategy of optimization. The HS was based on the process of improvisation of musicians. The results of the experiment proved this strategy was better in terms of performance than the other methods.

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“…Due to the rapid development of science and technology, the cost of data capture and storage has been reduced dramatically, which makes it easy to record each day's trading data related to the financial market. As a result, significant financial analysis of data has become a prominent area of research in economics and a variety of other disciplines [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction

Padhi

Padhy

Bhoi

et al. 2022

Computational Intelligence and Neuroscience

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Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicated with batch processing techniques since stock prices are highly correlated. In recent years, advances in machine learning have given us a lot of chances to use forecasting theory and risk optimization together. The study postulates a unique two-stage framework. First, the mean-variance approach is utilized to select probable stocks (portfolio construction), thereby minimizing investment risk. Second, we present an online machine learning technique, a combination of “perceptron” and “passive-aggressive algorithm,” to predict future stock price movements for the upcoming period. We have calculated the classification reports, AUC score, accuracy, and Hamming loss for the proposed framework in the real-world datasets of 20 health sector indices for four different geographical reasons for the performance evaluation. Lastly, we conduct a numerical comparison of our method’s outcomes to those generated via conventional solutions by previous studies. Our aftermath reveals that learning-based ensemble strategies with portfolio selection are effective in comparison.

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Revenue Model of Supply Chain by Internet of Things Technology

Cited by 12 publications

References 48 publications

Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

Multi Objective and Multi-Product Perishable Supply Chain with Vendor-Managed Inventory and IoT-Related Technologies

Optimization of Multi Hop Data Aggregation in Medical Iot Using Trust Based Secure Routing

An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction

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