The permutation method of multiple attribute decision making has two significant deficiencies:
high computational time and wrong priority output in some problem instances. In this paper, a
novel permutation method called adjusted permutation method (APM) is proposed to
compensate deficiencies of conventional permutation method. We propose Tabu search (TS)
and particle swarm optimization (PSO) to find suitable solutions at a reasonable computational
time for large problem instances. The proposed method is examined using some numerical
examples to evaluate the performance of the proposed method. The preliminary results show
that both approaches provide competent solutions in relatively reasonable amounts of time
while TS performs better to solve APM.