###### Abstract  
Assembly shop scheduling and energy-efficient scheduling have attracted much attention in the past decades; however, energy consumption is often ignored in assembly hybrid flow shop scheduling. Neglecting energy consumption will greatly diminish the progress of sustainable manufacturing. In this study, an assembly hybrid flow shop scheduling problem considering energy consumption (EAHFSP) is investigated, and a population diversity-based artificial bee colony algorithm (DABC) is proposed to minimize the makespan and total energy consumption (TEC) simultaneously. Diversified search strategies based on rank value are introduced to the employed bee phase; a novel probability selection method in the onlooker bee phase is designed to control the selection pressure; moreover, a diversity control strategy is applied to improve the diversity of food sources and avoid falling into stagnation. A number of experiments based on 44 extended benchmark instances from the literature and a real case are conducted to test the performance of the DABC algorithm. The statistical results show that the DABC algorithm is superior to the other four state-of-the-art algorithms on over 70% of the instances corresponding to metrics \(IGD\) and \(c\), which means that the DABC algorithm is effective and competitive in solving the considered EAHFSP. asembly; hybrid flow shop scheduling; energy consumption; artificial bee colony; population diversity +
Footnote †: journal: Applied sciences  
algorithm, and [PERSON] et al. [18] proposed an artificial immune algorithm. Considering lot streaming in the AHPSP, [PERSON] et al. [19] presented a genetic algorithm and simulated annealing algorithm to optimize the sum of weighted completion time. Recently, [PERSON] et al. [20] proposed a novel shuffled frog leaping algorithm with reinforcement for the distributed AHPSP. Regarding a multi-objective AHPSP, [PERSON] and [PERSON] [21] presented a novel neighborhood search to minimize the total tardiness, makespan, and maximum tardiness in the AHPSP with key objectives. [PERSON] et al. [22] used a mutant firefly algorithm to solve a bi-objective AHPSP with the simultaneous rate and on-time delivery rate. [PERSON] et al. [23] modified the genetic algorithm for the AHPSP with common due dates to minimize the total waiting time and the total earliness/tardiness. In recent years, energy-efficient scheduling has often been adopted in manufacturing systems. Regarding energy-efficient assembly shop scheduling problems, [PERSON] et al. [24] proposed a novel heuristic algorithm for energy-aware assembly flexible job shop scheduling problems. [PERSON] et al. [25] applied a hybrid estimation of distribution algorithm to an energy-efficient, three-stage, integrated scheduling problem. For energy-efficient distributed assembly flow-shop scheduling problems, the cooperative memetic algorithm [26] and brainstorm optimization algorithm [27] were designed. Meanwhile, the energy-efficient hybrid flow shop scheduling problem (EHFSP) was also considered. [PERSON] et al. [28] raised a Q-learning and general variable neighborhood search-driven non-dominated sorting genetic algorithm II for EHPSP with uniform machines. [PERSON] et al. [29] proposed an improved particle swarm optimization algorithm for an energy-efficient dynamic HFSP. [PERSON] et al. [30] applied a novel hybrid Aquila optimizer to an EHPSP. [PERSON] et al. [31] presented a novel multi-population artificial bee colony to solve an EHPSP with variable speeds. As stated above, studies on the AHPSP only consider the efficiency-related objectives and no existing paper involves energy-oriented criteria. In fact, the energy crisis and environmental problems caused by production activities are increasingly serious. Energy-oriented criteria are as important as the efficiency-related objectives in realizing green manufacturing. Energy consumption has been considered in hybrid flow shop, assembly flow shop, and assembly flexible job shop scheduling problems. However, it is often ignored in AHFSPs.