Novel Neighborhood Search for Low Carbon Scheduling of Hybrid Flow Shop with Carbon Emission

A hybrid flow shop scheduling problem (HFSP) with the minimization of carbon emissions is considered. After the features of the problem are discussed and three sub-problems are encoded independently, an effective neighborhood search with memory and global exchange (NSMG) is presented. In NSMG, memory is used to store best solutions and perform updates according to a simple strategy. Neighborhood search and global exchange are implemented and cooperated to generate a high-quality solution. Extensive experiments are conducted on a number of instances, and result analyses show that NSMG has strong search ability and competitiveness for the considered HFSP.