基于改进教学算法的车间作业调度问题

Improved teaching-learning-based optimization algorithm for solving job shop scheduling problem

为求解车间作业调度问题,提出一种基于个体差异化自学习的改进教学算法.针对教学算法局部搜索能力不高的缺陷,提出学生不仅应向能力好的学习者学习,亦应进行有差异的自我学习.通过学习者的完工时间评估学生的学习能力,提出学习次数概念,并设计自学习算子,完善学生阶段的更新,提高算法的局部搜索能力.最后,对OR-Library中的标准仿真实例进行实验,结果表明改进教学算法是有效的,其在收敛精度和鲁棒性能上均有较好的提高.

To solve the job shop scheduling problem, an improved teaching-learning-based optimization algorithm（TLBO）is proposed in this paper. Aiming at the weak local search ability in the existing TLBO, it is proposed that the learner should learn knowledge not only from the better learners, but also from itself, therefore, a differential self-learning operator is designed. The learning ability of learner is evaluated by its optimal, and its learning times are adaptive calculated according to its learning ability. The learners with higher learning ability have more chance to self-learn. Finally, the proposed method is applied to solve the benchmark instances in OR-Library. The experimental results show that the proposed algorithm is effective while solving the job shop scheduling problem, and its accuracy and robustness can be improved further.