An effective discrete invasive weed optimization algorithm for lot-streaming flowshop scheduling problems

Lot-streaming scheduling problem has been an active area of research due to its important applications in modern industries. this paper deals with the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion. an effective discrete invasive weed optimization (diwo) algorithm is presented with new characteristics. a job permutation representation is utilized and an adapted nawaz–enscore–ham heuristic is employed to ensure an initial weed colony with a certain level of quality. a new spatial dispersal model is designed based on the normal distribution and the property of tangent function to enhance global search. a local search procedure based on the insertion neighborhood is employed to perform local exploitation. the presented diwo is calibrated by means of the design of experiments approach. a comparative evaluation is carried out with several best performing algorithms based on a total of 280 randomly generated instances. the numerical experiments show that the presented diwo algorithm produces significantly better results than the competing algorithms and it constitutes a new state-of-the-art solution for the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion.