Simulation Based Capacity Optimization of a General Assembly Line with Extremely Unbalanced Station Process Time

A general assembly shop in an aerospace industry has extremely unbalanced workstation time and several re-entry processes. The capacity allocation of buffer-like workstations has been identified as a crucial factor of production improvement. Experience based what-if analyses based on simulation of feasible alternatives are time-consuming and cannot get the optimization solutions. In this study, a data driven simulation-based optimization framework is presented and used to re-design the capacity of buffer-like workstations in the line. The assembly processes with distinct characteristics are introduced and a data driven method is adopted to build the different structures of the simulation model. An optimization problem with constraints is formulated and solved based on genetic algorithm, which uses self-adoptive refusal and penalty function strategy to improve the performance. The case study of the real system is carried out and the results show that the data driven simulation-based optimization is efficient to the problem.