A shuffled multi-swarm micro-migrating birds optimizer for a multi-resource-constrained flexible job shop scheduling problem

Abstract

Scheduling problems with resource constraints have been a new research trend in recent years. This paper addresses a multi-resource-constrained flexible job shop scheduling problem that is very common in semiconductor manufacturing, precision engineering, and many other modern industries. To address this important problem, a novel algorithm called the shuffled multi-swarm micro-migrating birds optimization (SM2-MBO) algorithm is presented with a two-vector representation. The SM2-MBO forms a number of micro-swarms, each of which performs its own MBO independently. A random shuffle process applied to the entire population is invoked periodically to propagate the good information that is found in some of the micro-swarms. A diverse controlling strategy based on the aging phenomenon of life is proposed to diversify the population. An adaptive search operator based on a problem-specific crossover and a two-vector crossover helps to balance exploitation and exploration. Numerical experiments and comparisons are conducted against the best performing algorithms reported in the literature for the considered problem. The results demonstrate that the proposed SM2-MBO performs significantly better than the existing algorithms in solving the multi-resource-constrained flexible job shop scheduling problem with the makespan criterion. Furthermore, the proposed SM2-MBO can improve 9 out of 10 best known solutions for the benchmark instances in the literature.