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Research on Immune Genetic Algorithm for Solving Bi-Objective Scheduling Problems Subjected to Special Process Constraint

机译：求解特殊过程约束的双目标调度问题的免疫遗传算法研究

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The study presents a bi-objective scheduling model on parallel machines (BOSP), and proposes an immune genetic algorithm (VIGA) based on the vector group encoding method and the immune method. Compared with other scheduling problems on parallel machines. The BOSP is distinct for the following characteristics: (1) parallel machines are non-identical; (2) the sort of jobs processed on every machine can he restricted; (3) take minimizing the total tardiness penalty and minimizing the total completion time into account as a bi-objective problem. For VIGA, its three distinct characteristics are described as follows. Firstly, individuals are represented by a vector group, which can effectively reflect the virtual scheduling policy; Secondly, an immune operator is adopted and studied in order to guarantee diversity of the population; Finally, a local search algorithm is applied to improve quality of the population. Numerical experiments show that it is efficient, and can better overcome drawbacks of the genetic algorithm proposed in [13]. A much better prospect of application can be optimistically expected.
机译：该研究介绍了平行机（BOSP）的双目标调度模型，并提出了一种基于载体组编码方法和免疫方法的免疫遗传算法（Viga）。与并联机器上的其他调度问题相比。 BOSP对于以下特点是不同的：（1）并联机器是非相同的; （2）每台机器上处理的作业都可以限制; （3）尽量减少总迟到惩罚，并将总完成时间最小化视为双目标问题。对于Viga，其三种不同的特性描述如下。首先，个体由传染媒介组代表，这可以有效地反映虚拟调度策略;其次，采用免疫算子并研究以保证人口的多样性;最后，应用了本地搜索算法来提高人口的质量。数值实验表明它是有效的，并且可以更好地克服[13]中提出的遗传算法的缺点。可以乐观地预期应用更好的应用前景。

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