## 2 Related Works  
One of the most important issues in determining the assembly sequence is the appropriate data structure, which means graph representations, mainly directed graphs or hypergraphs. This kind of structure can be considered as formalisms to encode the feasible assembly sequences. To determine all feasible sequences an appropriate graph search algorithm is necessary. The commonly used algorithm for directed graphs or hypergraphs is a heuristically guided search algorithm A*. Although exhaustive search is the simplest and most popular strategy ensuring the complete of the task, it is quite often impractical. This approach is usually used in cases where the number of parts is small (simple assembly objects). In the case when the number of parts increases, these strategies may have limitations due to the problem of combinatorial explosion.  
Studies on ASP have implemented different heuristics optimization algorithms such as genetic algorithm, simulated annealing, evolutionary algorithm, ant colony optimization algorithm, and immune and other heuristic methods [10; 11; 12; 13; 14; 15; 16; 17].  
In paper [10] to solve the assembly sequence planning of a certain type of product, first of all, the rule of nomenclature is designed. Secondly, geometric feasibility and coherence are designed as constraint conditions and these two are combined with each other as the objective function. Finally, authors proposed a novel method under the name of immune particle swarm optimization algorithm. The results show that the immune particle swarm algorithm can be effective and useful in solving the problem of planning the assembly sequence.  
Authors of [12] address assembly sequence planning problem and propose an improved cat swarm optimization (CSO) algorithm and redefine some basic CSO concepts and operations according to ASP characteristics. The feasibility and the stability of this improved CSO are verified through an assembly experiment and compared with particle swarm optimization.  
Paper [13] proposes an ASP algorithm based on the harmony search (HS), which has an outstanding global search ability to obtain the global optimum. To solve the sequence planning problem, an improved harmony search algorithm is proposed in four aspects: (1) an encoding of harmony is designed based on ASP problems; (2) an initial harmony memory (HM) is established using the opposition-based learning (OBL) strategy; (3) a particular way to improvise a new harmony is developed; and (4) a local search strategy is introduced to accelerate the convergence speed. The proposed ASP algorithm is verified by two experiments.  
In paper [17], an attempt is made to generate optimal feasible assembly sequences using design for assembly concept by considering all the assembly sequence testing criteria from obtained feasible assembly sequences. To generate all sets of assembly sequences a simulated annealing technique is used. Sequences consist of \(\mathrm{n}-1\) levels during assembly, which are reduced by the DFA concept. DFA uses functionality of the assembled parts, material of the assembled parts, and liaison data to reduce the number of levels of the assembly by considering the directional changes as the objective function.  
In this article, an assembly sequence planning system is proposed. The neutral network structure is suitable for this kind of problem. The network is capable of predicting the assembly time, which allows one to choose the best assembly sequence from all the feasible sequences.