A learning-based co-evolution optimization framework for energy-aware distributed heterogeneous flexible flow shop lot-streaming scheduling problem

The distributed heterogeneous flexible flow shop scheduling problem (DHFFSP) has been considered in the era of economic globalization. Meanwhile, in some actual production scenarios, some jobs are divided into multiple sub-lots to boost the efficiency of intelligent manufacturing systems. The complexity of the scheduling problem is increased by the inevitable multiple time constraints among the jobs. In addition, considering the energy consumption, the energy-aware distributed heterogeneous flexible flow shop lot-streaming scheduling problem (EADHFFLSP) with release times, sequence-dependent setup and transport times is studied in the context of green manufacturing, which conforms to the actual production scenario of aluminum industry in the non-ferrous metallurgical industry. A learning-based co-evolution optimization framework (LBCOF) is designed to address EADHFFLSP with the minimization objectives of the maximum completion time and total energy consumption. In LBCOF, the population is divided into a global population and a local population, which performs global search and local search operations, respectively. Three heuristic rules are devised to generate the initial population. In local search, eight single-factory knowledge-driven operators and ten multi-factory knowledge-driven operators are proposed to update local population. A learning-based selection mechanism with dueling double deep Q-network (Dueling DDQN) component is presented to pick the best local search operator for the local population. Two energy-saving strategies are developed to improve the local population. The experimental findings reveal that LBCOF exhibits superior performance compared to some state-of-the-art algorithms for addressing EADHFFLSP.