在工作流管理系统中,个人工作列表的优化调度具有重要意义,已有的相关研究主要关注工作流实例的调度,而关于个人工作列表调度的研究还较少.首先描述了工作流实例动态执行环境下个人工作列表调度问题,并提出了一个基于遗传算法的个人工作列表资源调度算法.该算法要为每一个执行人推荐一个可行工作列表,并在保证工作项联合执行成功率的同时最小化总体延误代价.最后,通过一个仿真实验将该遗传算法与其他7种基于分配规则的典型调度算法进行了比较.结果表明,所提出的基于遗传算法的个人工作列表资源调度算法比已有的其他典型调度算法具有更好的调度效果.%In workflow management systems (WFMSs), appropriate consideration of applying scheduling techniques to manage actors' personal worklists is essential for successful implementation of workflow technology. Mainly, the attention of existing workflow scheduling has focused on the process perspective. As a result, issues associated with personal worklist's perspective, I.e., worklists that contain actors' to-do activity instances, have been largely neglected. Given this motivation, this paper for the first time, investigates issues in personal worklist scheduling under dynamic workflow environment. Towards these issues, the paper proposes a novel genetic algorithm to optimize the personal worklist management. This algorithm recommends for each actor a feasible worklist that will ensure the worklist's activity instances' successful execution while minimizing the total overtime costs for all personal worklists. Through comparing with other well-known workflow scheduling algorithms, the paper evaluates the effectiveness of the proposed genetic algorithm with a specific example and a simulation experiment.
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