## 3 Background of ABC  
In the present research, a modified artificial bee colony (ABC) algorithm, i.e., the archived ABC (AABC) is used to evaluate the developed framework for SI. The ABC technique is one of the latest and fastest processing algorithms for solving multi-objective problems. In [27], the ABC algorithm was introduced for the first time. It simulates the foraging swarm behavior of honey bees uses very few control parameters, and shows fast and efficient results. The researchers in [28] demonstrated that, for multimodal or multivariable problems, the ABC approach produces better results than other algorithms. Reasons for using the ABC algorithm in this study include its swift processing and optimized results. A number of the studies that offered a comparison of ABC with other multi-objective algorithms are compiled in Table 1 to further justify its use. The rapid development of technology and economy has increased the consumption and waste of electronic products. Recycling and recycling valuable and hazardous parts of used electronic products can effectively save resources while reducing environmental pollution and promote coordinated development of economy and environment. As one of the most critical aspects of the recycling process, its efficiency directly affects the cost of remanufacturing [29], thus, the disassembly line balance problem (DLBP) has gradually received attention. [PERSON] et al. [30] proposed DLBP, and the establishment of a simple mathematical model for the DLBP. Subsequently, precise mathematical methods and meta-heuristic algorithms were mainly used to optimize DLBP literature. The authors in [31; 32; 33; 34] proposed a method using traditional mathematical programming based on different strategies. The method solves DLBP, which has the advantage of high precision; however, DLBP is a complete None Polynomial (NP) problem [35]. Accurate mathematical methods are not suitable for solving large-scale DLBP. Therefore, [PERSON] et al. [36] proposed an applied genetic algorithm to optimize DLBP with the goal of minimizing the number of workstations and load balancing. [PERSON] [37] proposed a scheme based on the constraint of the simulated annealing algorithm to determine the order of removal to minimize product inventory levels based on Pareto theory. The researchers in [38; 39] proposed schemes through a bacterial foraging algorithm ant sColony algorithm Solving multi-objective DLBP. [PERSON] et al. [40] established a multi-objective U-shaped DLBP model based on lean production and solved it by using an artificial bee colony algorithm. Considering the uncertainty of disassembly time, [PERSON] et al. [41] introduced triangular fuzzy membership degree 1. The above research on DLBP only considers the constraint relationship between tasks. In fact, there may be mutual interference between the two tasks without the constraint relationship. The task disassembly time is uncertain due to the different disassembly sequence, which ultimately affects the disassembly line balance. This type of problem is defined as a sequential dependency problem, first proposed by [PERSON] et al. [42] in the assembly line balance problem (ALBP), and a sequential dependent assembly line balance problem model (SDALBP) was constructed. Greedy random adaptive search method [43] and hybrid genetic algorithms [44] were used to solve single-objective SDALBP. Considering the minimum number of stations, minimum total assembly cost, and smoothing index, [PERSON] et al. [45] proposed combining particle swarms with variable neighborhood search algorithms to solve the multi-objective SDALBP. For multi-product mixed-flow assembly on parallel assembly lines, [PERSON] et al. [46] established a parallel hybrid SDALBP mathematical model and proposed a hybrid ant colony-genetic algorithm. Similar to the assembly line, there is also a sequential dependency problem in the product disassembly process.