In this study, we address the deterministic assembly line balancing problem (ALBP) in a multiple product-models environment with multiple objectives. We have been motivated by the assembly line balancing problem of a white goods product production line that is a multi-model type line with 68 stations through which four product-models are assembled, each with approximately 400 precedence relations and 300 tasks. In the plant, to cope with the increasing demand in the medium term, the efficiency of the line is to be improved first before additional lines are added to the capacity in the longer term. Due to the large size of the real ALBP, optimal line balance could not be found out by means of the exact solution methods in reasonable times. Hence, in order to find some satisfactory solutions in relatively shorter computational times for the real-life-sized problems, we propose an adaptive simulated annealing (ASA)-based approach that uses a modified COMSOAL algorithm to construct an initial solution and then two ASA algorithms in succession to improve the solution. The approach has such a flexible structure that it can provide solutions for any type of deterministic ALBP as single, mixedmodel and multi-model ALBPs with several practical constraints like zoning, and consider multiple appropriate objectives such as minimization of cycle time, minimization of the number of stations, maximization of efficiency, and maximization of smoothness over the stations on the line. The simulated annealing algorithms proposed in the approach are adaptive in the sense that they have a novel adaptive mechanism for adjusting their parameters. Our computational experiments with the proposed ASA algorithms on several test problem instances in the literature indicate that they find the optimum solution in most of the instances for the simple ALB problems, both type 1 and 2, as well as the real-life instance considered. The overall approach proposed for the multimodel ALBP is tested for the real-life case problem and found to be satisfactory.