Improved lion swarm optimization algorithm to solve the multi-objective rescheduling of hybrid flowshop with limited buffer

Abstract

As the realities of production and operation in green and intelligent workshops become more variable, the adverse risks arising from disruptions to modernized workshop energy consumption schedules and customer churn caused by dynamic events are increasing. In order to solve those problems, we take the intelligent hybrid flow shop as the research subject, use buffer capacity and automated guided vehicles (AGVs) transport devices as resource constraints, construct a multi-objective rescheduling model that considers both energy consumption and customer satisfaction. According to the model characteristics, an improved lion swarm optimization algorithm (ILSO) is designed to solve the above model. To improve the initial solution quality and global search capability of the algorithm, ILSO is improved by combining the reverse learning initialization strategy of Logistic chaotic mapping with the tabu search strategy. The results of experiments on the proposed algorithm with different sizes of arithmetic cases and real cases in the workshop indicate that ILSO can effectively solve the bi-objective rescheduling problem oriented to inserting orders, and the proposed model can provide green dynamic scheduling solutions for manufacturing enterprises to achieve the purpose of transformation to green intelligent manufacturing.