Open AccessScheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm
Author(s) -
Xiao Xiangjie,
Pan Yaohui,
Lv Lingling,
Shi Yanjun
Publication year - 2021
Publication title -
iet collaborative intelligent manufacturing
Language(s) - English
Resource type - Journals
ISSN - 2516-8398
DOI - 10.1049/cim2.12016
Subject(s) - particle swarm optimization , computer science , scheduling (production processes) , mathematical optimization , swarm behaviour , algorithm , mode (computer interface) , multi swarm optimization , automated guided vehicle , job shop scheduling , artificial intelligence , mathematics , schedule , operating system
A modified particle swarm optimization (PSO) approach is presented for the multi‐mode resource‐constrained scheduling problem of automated guided vehicle (AGV) tasks. Various constraints in the scheduling process of the AGV system are analysed, and the types and quantities of AGVs as allocable resources are considered. The multiple‐AGV combined distribution mode and its impact on distribution tasks is also considered. Finally, a multi‐mode resource‐constrained task scheduling model is established for which the object is to minimise material delivery time. Based on the above model, the discrete particle swarm optimization algorithm that improved the basic PSO was proposed. The simulation results with the test set in PSPLIB standard library showed the effectiveness of the improved PSO algorithm.