A Co-Evolution Algorithm With Dueling Reinforcement Learning Mechanism for the Energy-Aware Distributed Heterogeneous Flexible Flow-Shop Scheduling Problem
针对钢铁连铸生产中能耗感知的分布式异构柔性流水车间调度问题，构建了混合整数线性规划模型，并提出一种融合决斗强化学习机制的协同进化算法，以最小化总延迟和总能耗。
The production process of steelmaking continuous casting (SCC) is a typical heterogeneous distributed manufacturing system. The scheduling problem in heterogeneous distributed manufacturing systems is a complex combinatorial optimization problem. In this article, the energy-aware distributed heterogeneous flexible flow shop scheduling problem (EADHFFSP) with variable speed constraints is studied with objectives, including total tardiness (TTD) and total energy consumption (TEC). A mixed-integer linear programming (MILP) model is constructed for the EADHFFSP. A co-evolution algorithm with dueling reinforcement learning mechanism (DRLCEA) is presented to address EADHFFSP. In DRLCEA, a knowledge-based hybrid initialization operation is proposed to generate the initial population of the problem. A global search based on adversarial generative learning is designed to search the solution space. The dueling double deep Q-network (DDQN) is applied to select the operator for the local search. A speed adjustment strategy and an energy-saving strategy based on knowledge are proposed to reduce TTD and TEC of the EADHFFSP with regard to the properties of EADHFFSP. The results of experiments show that the performance of DRLCEA is superior to certain state-of-the-art comparison algorithms in solving EADHFFSP.