Food pickling workshop lot streaming scheduling with resource constraints using Q-learning-based fruit fly optimisation
研究了食品腌制中原料收缩导致的资源约束问题，提出Q学习果蝇优化算法，通过90个池的案例验证，将完工时间缩短了10%。
Food pickling is an essential technique for maintaining flavour and texture. Due to the concentrated harvest period of raw materials, the demand for pit resources increases during peak seasons. Meanwhile, significant volume shrinkage of raw materials during the pickling leads to pit underutilisation, creating a bottleneck in production capacity expansion. To improve production capacity, it is crucial to address the challenges posed by raw material shrinkage. This study investigates the hybrid flow shop lot streaming scheduling problem with resource constraints (HFSLSP-RC) and proposes a Q-learning-based fruit fly optimisation algorithm (QFOA) to minimise the makespan. First, four distinct initialisation strategies are employed to enhance the quality of the initial population. Next, the Q-learning is integrated with the smell search stage to improve the local search capability of the algorithm. Additionally, a population merging strategy is introduced in the visual search stage to enhance the global search performance. Finally, the effectiveness of QFOA is validated through comparative experiments, ablation studies, and an industrial case study. The case study, which involved processing eight types of food across 90 pits, demonstrates that QFOA reduced the makespan by 10.0% compared to FOA and by 7.3% compared to IGWO.