标题：Scheduling unmanned aerial vehicle and automated guided vehicle operations in an indoor manufacturing environment using differential evolution-fused particle swarm optimization
期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2018
卷号：15
期号：1
DOI：10.1177/1729881417754145
语种：English
出版社：SAGE Publications
摘要：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.
关键词：Unmanned aerial vehicle; automated guided vehicle; scheduling; metaheuristic; DE and PSO hybrid
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