A hierarchical guidance strategy assisted fruit fly optimization algorithm with cooperative learning mechanism

Zhao, Fuqing; Ding, Ruiqing; Cao, Jie; Tang, Jianxin

doi:10.1016/j.eswa.2021.115342

Cited by 17 publications

(1 citation statement)

References 39 publications

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“…According to the research, major conventional algorithms such as linear programming, particle swarm optimization, genetic algorithms, and other well-known algorithms outperformed the optimization method (Cheng & Shi, 2022;Geruna et al, 2017;Yang Li & Xu, 2022). The foraging food behavior of fruit flies was adopted for the insect species best known for its ability to sense and see food even from 40 kilometers away (W. T. Pan, 2012;Zhao et al, 2021). Algorithms and fast computations have been widely disseminated in recent years due to their ease of application to optimization problems (Mahmoodabadi et al, 2018;Sun et al, 2022;Xing & Gao, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Trucks Pooling and Allocation in TSE Concept Using GIS Spatial and Novel FFOA

Siahaan¹,

Simatupang

Okdinawati

et al. 2022

ijjm

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Strategic system logistics business entails the importance of regulating truck pooling facilities and allocating the trucks for cost optimization goals. Regulators and investors must consider spatial constraints such as the supply-demand gap and service distance. Little attention has been paid to developing decision logistics models, particularly truck pooling and allocation decisions. In this study, the FFOA and GIS were used to determine the spatial component of truck pooling decisions, providing a scenario for origin pooling and delivery distance. The model evaluates truck allocation to each city, a distance vector, a spatial factor, and city demand are used for the cost optimization goal. The results show that the FFOA model successfully defines the optimal truck allocation for each truck pooling site with a cost. The managerial implication in developing a sharing economy concept for truck logistics is to use the study's framework model result to solve challenges in truck logistics.

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