The assembly is the last process of controlling the product quality during manufacturing. The installation guidance should provide the appropriate assembly information, e.g., to specify the components in each product. The installation guidance with low quality results in rework or the resource waste from the failure products. This article extends the dimensional chain assembly problem proposed by Tsung et al. to consider the multiple dimensional chains in the product. Since there are multiple dimensional chains in a product, the installation guidance should consider inseparability and acceptability as computing the installation guidance. The inseparability means that the qualities of all dimensional chains in the part should be evaluated together without separation, while the acceptability stands for that the size of each product should be satisfied with the specification. The simulated annealing (SA) algorithm is applied to design the assembly guidance optimizer named as AGOMDC to compute the assembly guidance in the dimensional chain assembly problem with multiple dimensional chains. Since SA has high performance in searching neighbor solutions, the proposed approach could converge rapidly. Thus, proposed AGOMDC could be applied in real-world application for the implementation consideration. The simulations consist of two parts: the feasibility evaluation and the algorithm configuration discussion. The first part is to verify the inseparability and acceptability that are the hard constraints of the assembly problem for the proposed AGOMDC, and the second one is to analyze the algorithm configurations to calculate the assembly guidance with 80% quality. The simulation results show that the inseparability and acceptability are achieved, while the proposed AGOMDC only requires more than two seconds to derive the results. Moreover, the recommended algorithm configurations are derived for evaluate the required running time and product quality. The configurations with product quality 80% are that the temperature descent rate is 0.9, the initial temperature is larger than 1000, and the iteration recommended function is derived based on the problem scale. The proposed AGOMDC not only helps the company to save the time of rework and prevent the resource waste of the failure products, but is also valuable for the automatic assembly in scheduling the assembly processes.
