LAUSR

Blocking lot-streaming flow shop scheduling problem with the stochastic processing time has a wide range of applications in various industrial systems. However, this problem has not yet been well studied. In this paper, the above-mentioned problem is transformed into a determinate multi-objective optimization one using the Monte Carlo sampling method. A Multi-Objective Migrating Birds Optimization (MOMBO) algorithm is then proposed to solve the above-mentioned re-formulated multi-objective scheduling problem, in which the multiple-based PFE is proposed to yield the initial solutions with high quality, the information of the non-dominated solutions is learned and sampled to improve the global searching ability of MOMBO, and a reference-point-assisted local search method for multi-objective optimization is applied to further enhance the exploitation capability of MOMBO. To evaluate the performance of the MOMBO, several comparative experiments are executed on 180 test scheduling instances. The experimental results demonstrate that the MOMBO outperforms the compared algorithms in convergence and distributivity and has capacities to tackle the uncertainties.