Routing for unmanned flying robots in expedited order delivery within distribution networks and online customer service platforms

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Khosraviyan Erfan

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Journal Paper

Paper Information

Journal: Science and Technology in Mechanical Engineering Year:2023 | Volume:2 | Issue:2 Page(s): 111-125

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Information Journal Paper

Title

Routing for unmanned flying robots in expedited order delivery within distribution networks and online customer service platforms

Author(s)

Khosraviyan Erfan | Issue Writer Certificate

Keywords

routing

Aerial Robot

State space equations

Genetic optimization algorithm

distribution network and customer service

Abstract

Amidst the continuous expansion of global commerce and the urgent need for expedited order delivery within distribution networks and online customer service platforms, the demand for air cargo transportation has reached unprecedented levels, complementing traditional distribution channels such as land and sea. Consequently, this sector has garnered considerable attention in recent years. One of the primary challenges encountered in aerial distribution networks pertains to the routing problem for unmanned aerial vehicles (UAVs), which necessitates considerations of enhanced customer satisfaction and network constraints. To address this challenge, this paper commences by introducing the routing problem alongside customer and network constraints, subsequently presenting their mathematical formulations. Notably, the dynamic behavior of Aerial Robots poses a significant constraint in this context, which has been inadequately addressed in existing research on routing problems within distribution and customer service networks. This deficiency is attributed to the involvement of flight dynamics equations, complicating the problem significantly. In this study, the nonlinear equations governing Aerial Robots for customer service are reformulated in state space representation. Subsequently, the routing problem, incorporating the State space equations of flying robots and considerations of customer and network constraints, is tackled using a Genetic optimization algorithm—an optimal solver. Following the solution process using the genetic algorithm, the results are elucidated in terms of optimal routes. Simulation outcomes validate the efficacy of the proposed approach in meeting all problem requirements and objectives, thereby presenting a viable solution for routing large-scale aerial networks.

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