A multi-objective mixed-model multi-man assembly line balancing problem is classified as
an NP-hard problems; hence, using heuristics might be a suitable approach to effectively solve the
problem. The Extended Combinatorial Optimization with Local Search (COIN-E) algorithm is
developed for tackling this problem to optimize four objectives simultaneously, i.e. number of worker,
number of workstations, balance workload between workstations, and work relatedness. The
experimental results show that COIN-E outperforms the well-known algorithm, biogeography-based
optimization (BBO), in terms of convergence, spread, number of non-dominated solutions, and CPU
time.