Geng Kaifeng, Liu Li, Wu Zhanyong
Fan Li Business School, Nanyang Institute of Technology, Nanyang, 473004, Henan, China.
School of Digital Media and Art Design, Nanyang Institute of Technology, Nanyang, 473004, Henan, China.
Sci Rep. 2022 Nov 5;12(1):18741. doi: 10.1038/s41598-022-23144-6.
In the face of energy crisis, manufacturers pay more and more attention to energy-saving scheduling. In the paper, we consider the distributed heterogeneous re-entrant hybrid flow shop scheduling problem (DHRHFSP) with sequence dependent setup times (DHRHFSP-SDST) considering factory eligibility constraints under time of use (TOU) price, which means that each job can only be assigned to its available set of factories and all factories have different number of machines and processing capacity, and so on. To deal with DHRHFSP-SDST, a multi-objective Artificial Bee Colony Algorithm (MOABC) is proposed to optimize both the makespan and total energy consumption. For the MOABC, firstly, a hybrid initialization method is presented to initialize the population; then, due to the electricity price shows significant differences vary from periods under TOU price, the energy saving operator based on right-shift strategy is proposed to avoid processing jobs with the high electricity price without affecting the productivity; thirdly, based on the full consideration of distributed heterogeneous and factory eligibility, crossover and mutation operators, three neighborhood search operators and new food sources generation strategy are designed; lastly, extensive experiments demonstrate the effectiveness of the proposed algorithm on solving the DHRHFSP-SDST.
面对能源危机，制造商越来越关注节能调度。在本文中，我们考虑了具有顺序相关设置时间的分布式异构重入混合流水车间调度问题（DHRHFSP），即DHRHFSP-SDST，该问题考虑了分时电价（TOU）下的工厂资格约束，这意味着每个作业只能分配到其可用的工厂集合中，并且所有工厂具有不同数量的机器和加工能力等。为了解决DHRHFSP-SDST，提出了一种多目标人工蜂群算法（MOABC）来同时优化完工时间和总能耗。对于MOABC，首先，提出了一种混合初始化方法来初始化种群；其次，由于分时电价下不同时段的电价差异显著，提出了基于右移策略的节能算子，以避免在不影响生产率的情况下处理高电价时段的作业；第三，在充分考虑分布式异构和工厂资格的基础上，设计了交叉和变异算子、三种邻域搜索算子以及新的食物源生成策略；最后，大量实验证明了所提算法在解决DHRHFSP-SDST问题上的有效性。
