Cooperative-Guided Ant Colony Optimization with Knowledge Learning for Job Shop Scheduling Problem  

作　　者：Wei Li Xiangfang Yan Ying Huang 

机构地区：[1]School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China [2]School of Mathematics and Computer Science,Gannan Normal University,Ganzhou 341000,China

出　　处：《Tsinghua Science and Technology》2024年第5期1283-1299,共17页清华大学学报自然科学版（英文版）

基　　金：supported by the National Natural Science Foundation of China(Nos.62366003 and 62066019);the Natural Science Foundation of Jiangxi Province(No.20232BAB202046);the Graduate Innovation Foundation of Jiangxi University of Science and Technology(No.XY2022-S040).

摘　　要：With the advancement of the manufacturing industry,the investigation of the shop floor scheduling problem has gained increasing importance.The Job shop Scheduling Problem(JSP),as a fundamental scheduling problem,holds considerable theoretical research value.However,finding a satisfactory solution within a given time is difficult due to the NP-hard nature of the JSP.A co-operative-guided ant colony optimization algorithm with knowledge learning(namely KLCACO)is proposed to address this difficulty.This algorithm integrates a data-based swarm intelligence optimization algorithm with model-based JSP schedule knowledge.A solution construction scheme based on scheduling knowledge learning is proposed for KLCACO.The problem model and algorithm data are fused by merging scheduling and planning knowledge with individual scheme construction to enhance the quality of the generated individual solutions.A pheromone guidance mechanism,which is based on a collaborative machine strategy,is used to simplify information learning and the problem space by collaborating with different machine processing orders.Additionally,the KLCACO algorithm utilizes the classical neighborhood structure to optimize the solution,expanding the search space of the algorithm and accelerating its convergence.The KLCACO algorithm is compared with other highperformance intelligent optimization algorithms on four public benchmark datasets,comprising 48 benchmark test cases in total.The effectiveness of the proposed algorithm in addressing JSPs is validated,demonstrating the feasibility of the KLCACO algorithm for knowledge and data fusion in complex combinatorial optimization problems.

关 键 词：Ant Colony Optimization(ACO) Job shop Scheduling Problem(JSP) knowledge learning cooperative guidance 

分 类 号：TH16[机械工程—机械制造及自动化] TP18[自动化与计算机技术—控制理论与控制工程]