Ant colony algorithm in flexible job-shop scheduling optimization based on memory curve model

doi:10.16731/j.cnki.1671-3133.2017.11.019

Abstract

Abstract: The colony algorithm shop scheduling aspect of the application has been studied for its convergence rate in terms of solving the flexible job-shop scheduling problem is slow and easy to fall into local optimal solution,proposed a biological memory curve based on the principle of information pheromone update rules.The shortest processing time as the objective function objective function flexible job-shop scheduling,combined with practical examples using MATLAB to solve.By contrast with the basic ant colony algorithm and other intelligent algorithm analysis,based on the principles of biological memory curve pheromone update rule it proposed solving ability and has a good convergence capability.

Key words: job-shop scheduling, pheromone updating rules, memory curve, flexible workshop, ant colony algorithm

CLC Number:

TP391.9

Zhang Yuxian, Ding Xiukun, Xue Dianchun, Wang Xiaoting, Cheng Shurui. Ant colony algorithm in flexible job-shop scheduling optimization based on memory curve model[J]. Modern Manufacturing Engineering, 2017, 446(11): 105-109.

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