A modified teaching-learning-based optimisation algorithm for bi-objective re-entrant hybrid flowshop scheduling

作     者：Shen, Jing-nan Wang, Ling Zheng, Huan-yu 

作者机构：Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH》 (国际生产研究杂志)

年 卷 期：2016年第54卷第12期

页      面：3622-3639页

核心收录：

学科分类：12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0802[工学-机械工程] 

基　　金：National Science Fund for Distinguished Young Scholars of China National Science Foundation of China Doctoral Program Foundation of Institutions of Higher Education of China 

主　　题：re-entrant hybrid flowshop scheduling bi-objective teaching-learning-based optimisation decoding method 

摘      要：In this paper, a modified teaching-learning-based optimisation (mTLBO) algorithm is proposed to solve the re-entrant hybrid flowshop scheduling problem (RHFSP) with the makespan and the total tardiness criteria. Based on the simple job-based representation, a novel decoding method named equivalent due date-based permutation schedule is proposed to transfer an individual to a feasible schedule. At each generation, a number of superior individuals are selected as the teachers by the Pareto-based ranking phase. To enhance the exploitation ability in the promising area, the insertion-based local search is embedded in the search framework as the training phase for the TLBO. Due to the characteristics of the permutation-based discrete optimisation, the linear order crossover operator and the swap operator are adopted to imitate the interactions among the individuals in both the teaching phase and the learning phase. To store the non-dominated solutions explored during the search process, an external archive is used and updated when necessary. The influence of the parameter setting on the mTLBO in solving the RHFSP is investigated, and numerical tests with some benchmarking instances are carried out. The comparative results show that the proposed mTLBO outperforms the existing algorithms significantly.