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中南大学学报(英文版)

Journal of Central South University

Vol. 24    No. 8    August 2017

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A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine
SUN Ze-wen(孙泽文), GU Xing-sheng(顾幸生)

Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education(East China University of Science and Technology), Shanghai 200237, China

Abstract: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.

 

Key words: hybrid estimation of distribution algorithm; teaching learning based optimization strategy; hybrid flow shop; unrelated parallel machine; scheduling

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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