On May 16th, Professor Wang Ling from Tsinghua University and Professor Sang Hongyan from Liaocheng University visited the Huangdao Forum (Mathematical and Physical Sub-forum), delivering academic reports titled Optimization and Prospects of Intelligent Scheduling for Industrial and Service Systems and Optimal Scheduling for Intelligent Manufacturing to faculty and students, respectively.
Professor Wang Ling introduced that in the deep integration of new-generation information technology with manufacturing and service industries, as well as the globalization process, digitization, intelligence, greenization, and servitization represent the development direction of manufacturing. Manufacturing systems are shifting from centralized to distributed models, advancing toward high-quality green smart manufacturing. The key for distributed networked smart manufacturing systems lies in how to achieve efficient and high-benefit product manufacturing with low costs and risks through rational resource allocation, optimal combination, and sharing. In response to challenges such as numerous subproblems, large solution spaces, strong coupling relationships, and complex elements in the scheduling optimization of new smart manufacturing systems, Professor Wang Ling and his team have strengthened the integration of intelligent technologies with domain knowledge by fusing model analysis and data parsing, coordinating knowledge-driven and swarm intelligence, and synergizing machine learning with operational research optimization. By integrating mathematical theory analysis with data-based intelligent parsing, they have enabled global, efficient, and robust optimization in scheduling, breaking through technologies for distributed cross-organizational dynamic manufacturing resource allocation and intelligent optimization decision-making. In the future, they aim to achieve rational utilization and efficient allocation of resources in distributed dynamic manufacturing systems, multi-level analysis and prediction, robust and intelligent scheduling optimization decisions, and ultimately achieve precision intelligent control.
Against the backdrop of diversified production models and increasingly complex production environments in smart manufacturing enterprises, Professor Sang Hongyan addressed the integrated scheduling problem of flexible job shops and Automated Guided Vehicles (AGVs, computer-controlled driverless transport vehicles). She pointed out that when facing numerous constraints such as environment, machinery, capacity, time, materials, and quantity, optimized AGV scheduling to assist production and transportation is conducive to improving manufacturing efficiency and automation levels. Professor Sang Hongyan and her team innovatively proposed a framework of imitation learning-assisted evolutionary algorithms, using imitation learning methods to adaptively select operational operators in evolutionary algorithms, achieving collaborative optimization of production efficiency and energy consumption, and providing a scalable new approach to intelligent scheduling for smart manufacturing.
After the reports, Professor Wang Ling and Professor Sang Hongyan engaged in lively interactive exchanges with on-site faculty and students, patiently answering their questions and providing targeted and instructive suggestions based on their own experiences.
[Editor:Yaoguang Zhang]