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

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 high-performance 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.