Abstract
The high efficiency, flexibility, and low cost of robots provide huge opportunities for the application and development of intelligent logistics. Especially during the COVID-19 pandemic, the non-contact nature of robots effectively helped with preventing the spread of the epidemic. Task allocation and path planning according to actual problems is one of the most important problems faced by robots in intelligent logistics. In the distribution, the robots have the fundamental characteristics of battery capacity limitation, limited load capacity, and load affecting transportation capacity. So, a smart community logistics service framework is proposed based on control system, automatic replenishment platform, network communication method, and coordinated distribution optimization technology, and a Mixed Integer Linear Programming (MILP) model is developed for the collaborative and persistent delivery of a multiple-depot vehicle routing problem with time window (MDVRPTW) of swarm robots. In order to solve this problem, a hybrid algorithm of genetically improved set-based particle swarm optimization (S-GAIPSO) is designed and tested with numerical cases. Experimental results show that, Compared to CPLEX, S-GAIPSO has achieved gaps of 0.157%, 1.097%, and 2.077% on average, respectively, when there are 5, 10, and 20 tasks. S-GAIPSO can obtain the optimal or near-optimal solution in less than 0.35 s, and the required CPU time slowly increases as the scale increases. Thus, it provides utility for real-time use by handling a large-scale problem in a short time.
Highlights
Since the global outbreak of the new crown pneumonia epidemic in winter 2019, robots replaced manual distribution which, used in drugs and goods delivery and supply chains, contributed a lot to the prevention and control of the epidemic
This article is based on the basic characteristics of the robot; a combined smart community logistics service framework consisting of a control system, automatic replenishment platform, network communication method, and coordinated distribution optimization technology is proposed, in real-time dynamic collaborative and persistent delivery
We proposed the S-GAIPSO hybrid algorithm, which can well represent the problem space of multiple-depot vehicle routing problem with time window (MDVRPTW) for the cooperative and persistent delivery of Swarm Robots (SR), retain the advantages of fast local search of particle swarm optimization, and improve the global optimization speed and optimization ability of particles
Summary
Since the global outbreak of the new crown pneumonia epidemic in winter 2019, robots replaced manual distribution which, used in drugs and goods delivery and supply chains, contributed a lot to the prevention and control of the epidemic. Reducing customer waiting time and improving customer satisfaction is still a key issue These researches are based on the actual delivery background of SR, using robots, unmanned aerial vehicles, and trucks for collaborative delivery. This article is based on the basic characteristics of the robot; a combined smart community logistics service framework consisting of a control system, automatic replenishment platform, network communication method, and coordinated distribution optimization technology is proposed, in real-time dynamic collaborative and persistent delivery. We propose a smart community logistics service framework, and provide detailed ideas on how to design an intelligent distribution system, including a path planning solution for SR to achieve dynamic and persistent delivery and reduce customer waiting time. Acknowledgments and references are stated at the end of the paper
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