This paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to find the near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms.

本文研究了单机多任务调度问题，并考虑了两主体问题。目的是寻找最佳时间表，以使一个代理的总拖延最小化，而另一代理的总完成时间则具有上限。对于此问题，采用了具有几种主要属性和下界的分支定界方法来搜索小规模作业的最佳解决方案。提出了三种元启发法，云模拟退火算法，遗传算法和模拟退火算法，分别采用三种改进方法，以找到适合大型作业的最佳解决方案。提供计算研究，实验以评估所提出算法的能力。最后，