为求解同时考虑序相关设置时间和运输时间约束的低碳混合流水车间调度问题,提出了一种多种群协同进化的离散人工蜂群(Multi-Population Discrete Artificial Bee Colony, MPDABC)算法。首先,采用工件排序和机器速度的两层编码策略表示一个可行的调度方案。解码中,针对机器设置和工件运输形成的时间约束改进传统解码规则；其次,采用锦标赛将种群拆分为多个子种群,并设计四种不同的邻域结构使各子种群能够在雇佣蜂阶段对当前邻域进行细致搜索；然后,在跟随蜂阶段,各子种群个体根据交互因子选择相应的跟随对象,实现子种群间的信息交互,并在侦查蜂阶段采用模拟退火机制避免算法陷入局部最优。最后,将MPDABC与其他三种多目标算法在24个算例上进行仿真实验,三种指标对比结果表明该算法具有优越的搜索性能,也验证了所提算法的有效性。

To address the energy-efficient hybrid flow shop scheduling problem that incorporates sequence-dependent setup time and transportation time constraints, a multi-population discrete artificial bee colony (MPDABC) algorithm is introduced. First, a two-layer encoding strategy is employed to represent a feasible scheduling solution, encompassing job sequencing and machine speed. During decoding, traditional rules are refined to accommodate time constraints arising from machine setup and job transportation. Next, a tournament mechanism partitions the population into multiple subpopulations. Four neighborhood structures are designed to facilitate detailed searches within each subpopulation during the employed bee phase. In the onlooker bee phase, individuals from each subpopulation select subsequent targets based on interaction factors, fostering inter-subpopulation information exchange. To prevent the algorithm from getting trapped in local optima during the scout bee phase, a simulated annealing mechanism is applied. Finally, the MPDABC is evaluated through simulation experiments alongside three other multi-objective algorithms on 24 instances. Comparison results demonstrate the superior search performance and effectiveness of the proposed MPDABC.

TP273

国家自然科学基金项目（面上项目，重点项目，重大项目）