Multi-objective sequencing problem on mixed-model multi-manned assembly lines is known
to be NP-hard resulting in being nearly impossible to obtain an optimal solution for practical
problems. This paper presents a method called Combinatorial Optimization with Coincidence Expand
Algorithm (COIN-E) for the sequencing problem. Three objectives are simultaneously considered;
minimum production rates variance, minimum utility work, and minimum setup times. The results
from the experiments clearly show that COIN-E has better performances than Non-dominated Sorting
Genetic Algorithms (NSGA-II), which is another well-known algorithm, in terms of convergence to
the Pareto-optimal set, spread of solutions, ratio of non-dominated solution, and computation time to
solution.