A knowledge-guided cooperative coevolutionary algorithm for Seru system scheduling optimization
As a new production mode,Seru system has been widely used in assembly enterprises due to its flexibility,efficiency,and fast response to the market.To optimize the production and labor efficiency simultaneously,this paper investigates the multi-objective scheduling problem of Seru system to minimize the makespan and the total labor time,and a knowledge-guided cooperative coevolutionary algorithm(KCCA)is proposed to solve this problem.First,the problem can be decomposed into two subproblems:Seru formation and Seru scheduling,and two populations are constructed to solve the two subproblems respectively.Meanwhile,to improve the search efficiency,the population size adjustment strategy is designed to allocate more individuals to the more potential population.Moreover,to further enhance the exploitation capability of KCCA,the knowledge is derived by analyzing the problem properties to design effective search operators and rules,which are used to perform the knowledge-guided enhanced search on elite individuals.Computational experiments and statistical comparisons validate the effectiveness of the specific designs of the KCCA,which can achieve a better optimization performance for multi-objective scheduling than state-of-the-art algorithms.
Seru production systemcooperative searchknowledge drivenenhanced searchadjustment strategy
国家科技期刊平台
NSTL
万方数据
