Smart Combat Simulations in Terms of Industry 4.0

Hocaoğlu, Mehmet Fatih; Genc, Ibrahim

doi:10.1007/978-3-030-04137-3_15

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…This idea could even be proved by generating all possible schemes for a particular K n and calculating their f 1 fitness score, which is out of scope for this study. On the other hand and by using f 1 as the fitness function, the network manager will be able to minimize the maximum broadcast time of any entity of the network, which is shown to be useful in different applications such as wireless sensor networks [97], industry 4.0 [98], video packet distribution in vehicular ad-hoc networks [99], robotics [100], satellite link topology [101], and age of information performance [102].…”

Section: B Fitness Functionmentioning

confidence: 99%

HUB-GA: A heuristic for universal lists broadcasting using genetic algorithm

Gholami

Harutyunyan

2023

J. Commun. Netw.

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Section: B Fitness Functionmentioning

confidence: 99%

HUB-GA: A heuristic for universal lists broadcasting using genetic algorithm

Gholami

Harutyunyan

2023

J. Commun. Netw.

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“…(2020). In distributed systems, the MBT has many applications for the Internet of Things (IoT), Wireless Sensor Networks (WSNs) and Industry 4.0 (Shang et al., 2010; Hocaoğlu and Genç, 2019; Ivanova, 2019). Other applications include communication among telephone networks (Ivanova, 2019), surveillance and reconnaissance (Dekker, 2002), robotics (Bucantanschi et al., 2007; Keshavarz et al., 2011), satellite link topology (Chu and Chen, 2018), and Direct Memory Access (DMA) (Lazard, 1992).…”

Section: Introductionmentioning

confidence: 99%

A matheuristic approach for the minimum broadcast time problem using a biased random‐key genetic algorithm

Lima

Aquino

Nogueira

et al. 2022

Int Trans Operational Res

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The Minimum Broadcast Time (MBT) is a well‐known data dissemination problem whose goal is to find a broadcast scheme that minimizes the number of steps needed to execute the broadcast operation. The problem has many applications in distributed systems and, in particular, the Industry 4.0 domain. Because Industry 4.0 applications rely primarily on the use of large‐scale machine to machine communications, they need data dissemination techniques that combine high reliability with low communication latency. This work proposes a Biased Random‐Key Genetic Algorithm and a matheuristic for the MBT. We carry out experiments with our algorithms on instances commonly used in the literature (hypercube, shuffle exchange, cube‐connected cycles, de Bruijn, Harary graphs), and also on massive synthetic instances (up to 1000 vertices), allowing to cover many possibilities of real industry topologies. Our proposal is also compared with state‐of‐the‐art exact methods and heuristics. Experimental results show that our algorithm is able to outperform the best‐known heuristics for the MBT, and also that it is a very good alternative for large instances that cannot be solved by current exact methods.

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Innovation of the Logistics Process of Grupo Logistico Especializado S.A.S. Through the Development and Implementation of the aGILE Platform

Fajardo-Toro,

Ardila

2024

Lecture Notes in Networks and Systems

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Smart Combat Simulations in Terms of Industry 4.0

Cited by 4 publications

References 35 publications

HUB-GA: A heuristic for universal lists broadcasting using genetic algorithm

HUB-GA: A heuristic for universal lists broadcasting using genetic algorithm

A matheuristic approach for the minimum broadcast time problem using a biased random‐key genetic algorithm

Innovation of the Logistics Process of Grupo Logistico Especializado S.A.S. Through the Development and Implementation of the aGILE Platform

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