A bio-inspired scheduling approach for machines and automated guided vehicles in flexible manufacturing system using hormone secretion principle

Gu, Wenjin; Li, Yuxin; Zheng, Kun; Yuan, Minghai

doi:10.1177/1687814020907787

Cited by 7 publications

(1 citation statement)

References 41 publications

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“…A strategy for avoidance conflict of AGVs was also proposed. [21] investigated the dynamic AGVs scheduling problem with AGVs and machines having specific speed. They proposed a biological intelligent approach (BIA) inspired by hormone regulation in endocrine system.…”

Section: A Related Workmentioning

confidence: 99%

Improved Particle Swarm Approach for Dynamic Automated Guided Vehicles Dispatching

Zaghdoud,

Amara,

Ghedira

2022

IJACSA

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The automated guided vehicles dispatching is one of the important operations in containers terminal because it affects the loading/unloading process. This operation has become faster and more complex until the automation advent. Although this evolution, the environment has become dynamic and uncertain. This paper aims to propose an improved particle swarm approach for solving the bi-objective problem of automated guided vehicles dispatching and routing in a dynamic environment of containers terminal. The objectives are to minimize the total travel distance of all automated guided vehicles and maximize the workload balance between them. The application of particle swarm algorithm in its basic form, shows a premature convergence. To ameliorate this convergence, the authors proposed the application of a method to escape the worst particles from the local optimum. The new Hybrid Guided Particle Swarm approach consists of hybridization between Dijkstra algorithms and a Guided Particle Swarm Algorithm. The routing problem is solved with Dijkstra algorithm and the dispatching problem with guided particle swarm approach. As a first step, this approach has been applied in a static environment where the dispatching parameters and the routing parameters are fixed in advance. The second step consists of applying this approach in a dynamic environment where the number of containers associated with each automated guided vehicles can change, the shortest path and the container locations can also change during the algorithm execution. The numeric results in a static environment show a good Hybrid Guided Particle Swarm performance with a faster and more stable convergence, which surpasses previous approaches such as Hybrid Genetic Approach and the efficiency of its extension approach Dynamic Hybrid Guided Particle Swarm in a dynamic environment.

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Section: A Related Workmentioning

confidence: 99%

Improved Particle Swarm Approach for Dynamic Automated Guided Vehicles Dispatching

Zaghdoud,

Amara,

Ghedira

2022

IJACSA

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Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.

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Bi-objective grey wolf optimization algorithm combined Levy flight mechanism for the FMC green scheduling problem

Zhou

Lei

2021

Applied Soft Computing

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A bio-inspired scheduling approach for machines and automated guided vehicles in flexible manufacturing system using hormone secretion principle

Cited by 7 publications

References 41 publications

Improved Particle Swarm Approach for Dynamic Automated Guided Vehicles Dispatching

Improved Particle Swarm Approach for Dynamic Automated Guided Vehicles Dispatching

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Bi-objective grey wolf optimization algorithm combined Levy flight mechanism for the FMC green scheduling problem

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