Abstract:

In view of the green flexible job shop scheduling problem where machines have multiple speeds, a green flexible job shop scheduling model under multiple speeds was constructed to minimize the makespan and total energy consumption under different speeds. A discrete coronavirus herd immunity optimizer (DCHIO) was proposed for a solution. A discrete individual updating method was introduced for the relatively large solution space of the multi-speed problem, based on which a population updating mechanism with multi-scale joint search was proposed to search the solution space quickly and uniformly. A dynamic mutation operation was designed to enhance the population diversity of the algorithm while realizing the adaptive tuning. By mining the empirical knowledge of the current scheduling scheme, a knowledge-driven neighborhood search strategy was proposed to simultaneously reduce the makespan and energy consumption. The ^{experimental results show that the algorithm proposed in this paper can effectively solve the green flexible scheduling problem under multiple speeds.}

Key words: green scheduling, flexible job shop, discrete coronavirus herd immunity optimizer, multi-speed machines, knowledge-driven neighborhood search