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首页> 外文期刊>International Journal of Production Research >A modified teaching-learning-based optimisation algorithm for bi-objective re-entrant hybrid flowshop scheduling
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A modified teaching-learning-based optimisation algorithm for bi-objective re-entrant hybrid flowshop scheduling

机译：改进的基于教学学习的双目标可重入混合流水车间调度算法

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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.
机译：本文提出了一种改进的基于教学学习的优化算法（mTLBO），以解决带有返工期和总延迟标准的可重入混合流水车间调度问题（RHFSP）。基于简单的基于作业的表示，提出了一种新的解码方法，该方法称为基于等效到期日的置换计划，以将个人转移到可行的计划中。在基于帕累托的排名阶段中，每一代都选出了许多优秀的人作为教师。为了提高在有希望的地区的开发能力，将基于插入的本地搜索嵌入到搜索框架中，作为TLBO的培训阶段。由于基于排列的离散优化的特性，在教学阶段和学习阶段都采用线性顺序交叉算子和交换算子来模仿个体之间的交互。为了存储在搜索过程中探索的非主导解决方案，需要使用外部档案并在需要时进行更新。研究了参数设置对mTLBO求解RHFSP的影响，并进行了一些基准测试实例的数值测试。比较结果表明，提出的mTLBO明显优于现有算法。

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