An improved biogeography-based optimization algorithm for fuzzy distributed flexible job-shop scheduling problem
SUN Mei-ling
GU Xing-sheng
Abstract：The globalization of the economy has prompted manufacturing enterprises to transition from a single factory to a multi-factory collaborative model,making the fuzzy distributed flexible job-shop scheduling problem(FDFJSP)a re-search hotspot in the scheduling field.In this paper,a novel biogeography-based optimization algorithm based on simulated annealing and local search strategy(BBOSL)is proposed to minimize the maximum fuzzy completion time of FDFJSP.Based on the characteristics of the problem,a new factory-random key encoding and decoding scheme is designed.Schedul-ing rules are used to generate half of the initial population to improve the population quality.A new solution acceptance method based on a simulated annealing algorithm and a local search strategy based on a critical factory are proposed to enhance the search capability.The algorithm parameters are tuned to improve algorithm performance.The experimental re-sults validate the effectiveness of the improved strategy and compare it with the existing algorithms to verify its superiority in fuzzy centralized and fuzzy distributed flexible job-shop scheduling problems.
Keywords：production schedulingfuzzy distributedflexible job-shopbiogeography-based optimization algorithmscheduling rulesimulated annealing
Publication Date：2025-04-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 713-721 )