Abstract
Intelligent manufacturing technologies have been pursued by the industries to establish an autonomous indoor manufacturing environment. It means that tasks, which are comprised in the desired manufacturing activities, shall be performed with exceptional human interventions. This entails the employment of automated resources (i.e. machines) and agents (i.e. robots) on the shop floor. Such an implementation requires a planning system which controls the actions of the agents and their interactions with the resources to accomplish a given set of tasks. A scheduling system which plans the task executions by scheduling the available unmanned aerial vehicles and automated guided vehicles is investigated in this study. The primary objective of the study is to optimize the schedule in a cost-efficient manner. This includes the minimization of makespan and total battery consumption; the priority is given to the schedule with the better makespan. A metaheuristic-based methodology called differential evolution-fused particle swarm optimization is proposed, whose performance is benchmarked with several data sets. Each data set possesses different weights upon characteristics such as geographical scale, number of predecessors, and number of tasks. Differential evolution-fused particle swarm optimization is compared against differential evolution and particle swarm optimization throughout the conducted numerical simulations. It is shown that differential evolution-fused particle swarm optimization is effective to tackle the addressed problem, in terms of objective values and computation time.
Highlights
Intelligent manufacturing environment has been an emerging topic in regard to the rise of Industry 4.0 concept across various domains.[1,2,3] It creates a smart factory, where automation of the manufacturing operations is the key factor
The main contributions of this study are described as follows: (i) This study developed a mathematical formulation of the addressed problem of collaborative unmanned aerial vehicle (UAV)– automated guided vehicle (AGV) operations in an indoor manufacturing environment
The quality of the solution produced by differential evolution (DE) is found to be usually better, while the computation time is generally longer compared to Particle swarm optimization (PSO)
Summary
Intelligent manufacturing environment has been an emerging topic in regard to the rise of Industry 4.0 concept across various domains.[1,2,3] It creates a smart factory, where automation of the manufacturing operations is the key factor. This automation enables the minimization of human–labor interventions on tedious, time-consuming, and sometimes hazardous jobs. A heavy material handling task is suitable for an automated guided vehicle (AGV) to perform This becomes the motivation of the collaborative operations among UAVs and AGVs to perform multiple tasks in an indoor manufacturing environment, which is addressed in this article
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
