蚁群系统算法求解比例多处理器开放车间调度问题OA

For efficiently solving the proportionate multiprocessor open shop scheduling problem,an ant colony system algorithm(ACS)is proposed.The algorithm adopts a stage-based encoding method with an efficient decoding strategy.Ants conduct the path search based on a task transfer strategy combining deterministic and random selection,and the con-centrations of pheromone as well as heuristic information are dynamically updated during the search and iteration process to avoid the algorithm falling into local optimality.Random solutions are introduced to expand the search scope and improve the solving efficiency.Theoretical analysis indicates that the algorithm can obtain an optimal solution within polynomial time.A test dataset consisting of 100 questions is constructed,which is utilized for conducting parameter tuning experiments and formal numerical experiments.Compared with the genetic algorithm,the ACS obtains higher-quality solutions with fewer iterations and shorter running time,thus verifying the effectiveness of the algorithm.The research results can provide theoretical basis and methodological support for optimizing the human body data collection scheduling.