A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

作者：Sun Ze-wen; Gu Xing-sheng^*

来源：Journal of Central South University, 2017, 24(8): 1779-1788.

DOI：10.1007/s11771-017-3586-6

• hybrid estimation of distribution algorithm
• teaching learning based optimization strategy
• hybrid flow shop
• unrelated parallel machine
• scheduling

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine (HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The (EDA) structure was used for global search while the teaching learning based optimization (TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

• 出版日期2017-8
• 单位华东理工大学