Despite great progress in the academic study of production planning and control (PPC), current PPC systems still fail to fully meet industry’s needs. The approach presented here aims at a more functional and user-fri...
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Despite great progress in the academic study of production planning and control (PPC), current PPC systems still fail to fully meet industry’s needs. The approach presented here aims at a more functional and user-friendly PPC by conceptualising a novel idea that leverages the potentials of biomimetic algorithms. The proposed architecture consists of four core modules: order release module, digital shadow, event detection and processing, and decision-making module. Their interactions provide the functionality of the system while emphasising the workers’ key role in the socio-technical PPC system. Requirements for the implementation of the architecture as well as starting points for future research are identified along with the concept.
Clustering has received extensive attention in recent years as an important approach to address the poor scalability in flying ad hoc networks (FANETs). However, due to the highly mobile nature of FANETs, frequent cha...
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Clustering has received extensive attention in recent years as an important approach to address the poor scalability in flying ad hoc networks (FANETs). However, due to the highly mobile nature of FANETs, frequent changes in cluster topology result in increased control overhead and reduced communication efficiency. To tackle these challenges, a novel heuristic clustering algorithm called the physarum-inspired clustering algorithm (PICA) is proposed in this paper. It leverages the intelligence and adaptability of Physarum polycephalum. The PICA adopts a distributed multi-hop clustering approach. During the cluster formation phase, and inspired by the foraging behavior of Physarum polycephalum, the algorithm incorporates link stability, residual energy, and link communication quality as evaluation metrics, thereby ensuring cluster coverage and stability. By selecting the most stable node within an n-hop range as the cluster head, the cluster stability is significantly enhanced. In the cluster maintenance phase, the PICA improves the backup cluster head selection mechanism and introduces damage detection and cluster merging mechanisms to further enhance the robustness and stability of clusters. Experimental results demonstrate that compared to existing EMASS and OSCA algorithms, the proposed PICA reduces the average end-to-end delay by 31.6 % and 25.27 %, improves the average packet delivery ratio by 27.7 % and 19.12 %, and decreases the average inter-cluster switching times by 14.71 % and 14.38 %, respectively. In conclusion, the PICA effectively addresses the issue of poor scalability in FANETs by leveraging the intelligence of multi-headed myxobacteria and adopting a multi-hop clustering approach. As indicated by experimental results, the proposed algorithm demonstrates significant performance improvements in terms of network metrics.
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