A Modified Migrating Birds Optimization for Multi-Objective Lot Streaming Hybrid Flowshop Scheduling

Abstract

This paper proposes an adaptive migrating birds optimization (AMBO) method based on variable neighborhood search to solve the inequal lot streaming hybrid flowshop scheduling problem (ILS-HFSP) for a 2+1+1 hybrid flowshop, which realizes multi-objective optimization of minimizing makespan and minimum average work in process. Compared with the original migrating birds optimization, the AMBO algorithm adopts the variable neighborhood search strategy with an adaptive selection probability of neighborhood operator that is adaptively adjusted with the number of iterations. Besides, a time-window operator is adopted to improve the search performance of exchange operators and convergence rate. Several orders of different scales generated randomly are studied, and the results show that the AMBO algorithm has a higher solution quality and a better convergence performance than the migrating birds optimization algorithm and the genetic algorithm, thereby verifying the effectiveness of the AMBO algorithm.

Keywords

• lot streaming problem
• hybrid flowshop scheduling problem
• variable neighborhood search
• adaptive migrating birds optimization (ambo)
• time window operation