Scheduling Job Machines with Swap Sequence to Minimize Makespan Using Spider Monkey Optimization Algorithm

Job scheduling is an NP-Hard combinatorial problem, so it requires optimization techniques to solve the problem. Several studies were conducted using various heuristic and meta-heuristic techniques. This paper presents the spider monkey optimization (SMO) algorithm, a newcomer algorithm in Swarm Intelligence. We added the swap sequence technique in the solution search process. Swap rate parameter testing was carried out at 0.2, 0.5, and 0.8; the results show that a swap rate of 0.2 can provide a smaller makespan but increases computation time. We analyze against the JSPLIB dataset test, which indicates the feasibility of the proposed algorithm. A comparison was made of the SMO, GA, HGA, and MCSA algorithms, and the results showed that MCSA was still superior for the
$10\mathrm{x}10$
and
$10\mathrm{x}5$
datasets. The results of the 15×5 and 20×5 datasets show that SMO tends to be superior to GA and HGA.