Tri-objective lot-streaming scheduling optimization for hybrid flow shops with uncertainties in machine breakdowns and job arrivals using an enhanced genetic programming hyper-heuristic

Abstract

Lot-streaming scheduling has been widely recognized as a means to improve shop productivity, but there is few research on lot-streaming scheduling problems under dynamic disturbances. To fill the gap, lot-streaming scheduling optimization approach for hybrid flow shops with uncertainties in machine breakdowns and job arrivals is proposed. A mathematical model is formulated with objectives of minimizing maximum tardiness, total idle energy consumption of the machine, and maximum makespan. Since the Genetic Programming Hyper Heuristic algorithm has better results in solving dynamic scheduling problems, a Collaborative Harmony Search-based Genetic Programming Hyper Heuristic (CHS-GPHH) is presented to solve the dynamic lot-streaming hybrid flow shop scheduling problem (DLS-HFSSP) with the two dynamic events occurring simultaneously. In the improved algorithm, a neighborhood structure based on harmony search is developed for lot splitting. To verify the effectiveness of the proposed approach, the various comparative studies c are conducted on the lot-streaming dynamic hybrid flow shop scheduling. The results demonstrate the effectiveness of each improvement component of the CHS-GPHH, and verify that CHS-GPHH is an effective approach to deal with DLS-HFSSP with the in all the scenarios.