Scheduling unmanned aerial vehicle and automated guided vehicle operations in an indoor manufacturing environment using differential evolution-fused particle swarm optimization

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 metaheuristicbased 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.

Algoritmo Baseado na Meta-heurística Particle Swarm Optimization para Configuração de Pools de Threads em Motores de Execução de Plataformas de Integração

“…[ Khosiawan et al 2018] tinham objetivo de minimizar o makespan e o consumo total de bateria em um cronograma de sistema de agendamento. Fizeram a comparação da otimização diferencial do enxame de partículas fundidas na evolução com a evolução diferencial e a otimização do enxame de partículas.…”

Toward delay-tolerant multiple-unmanned aerial vehicle scheduling system using Multi-strategy Coevolution algorithm

“…The simulations include comparisons of the proposed method MC with Differential Evolution (DE), Particle Swarm Optimization (PSO), and Differential Evolution-Fused Particle Swarm Optimization (DEFPSO-introduced by Khosiawan et al 24 ). DE and PSO are prominent metaheuristic algorithms among others, which have been used in various optimization problems.…”

Self-Corrective Sensor Fusion for Drone Positioning in Indoor Facilities

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