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- W2066081574 abstract "Applying scheduling techniques to manage activity instances (tasks) in executors’ personal work lists is crucial in workflow management systems (WFMSs). To date, most practical scheduling techniques focus on process (control-flow) perspective of WFMSs but do not adequately address issues relating to work lists containing executors’ to-do activity instances. Existing simple-dispatching-rule based techniques cannot fully capture the complexity in personal work list management under dynamic workflow environments. Given this motivation, this paper proposes an approach that applies genetic algorithm (GA) to manage activity instances in personal work lists. This approach is applied according to activity instances’ probabilities of satisfying deadlines and costs of violating deadlines. The approach can schedule activity instances among multiple executors’ personal work lists and recommend to each executor a list of activity instances that can be successfully executed, while minimising the overall deadline violat..." @default.
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- W2066081574 date "2013-09-01" @default.
- W2066081574 modified "2023-09-26" @default.
- W2066081574 title "Applying genetic algorithm to optimise personal worklist management in workflow systems" @default.
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- W2066081574 doi "https://doi.org/10.1080/00207543.2013.793856" @default.
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