One of the most important issues in cloud manufacturing involves obtaining an optimal manufacturing service composition solution. However, traditional manufacturing service composition methods either focused on single-task-oriented service composition or optimized solutions under a deterministic environment. In the study, a multitask-oriented manufacturing service composition (MMSC) model with two stages in uncertain environment is proposed. It handles the problem of multitask scheduling and also deals with the inherent uncertainty and ambiguity in cloud manufacturing including the occurrence of urgent task requests and the delayed delivery time of raw materials. In order to solve the MMSC model, a new genetic based hyper-heuristic algorithm (GA-HH) with adjustable length of chromosome is proposed. The GA-HH contains a set of low-level heuristics that directly operate on the solution domain that are organized by the high-level heuristic (i.e., genetic algorithm). Finally, the proposed GA-HH is proved as an efficient, effective, and robust algorithm to solve the MMSC model with considerations of multitask and uncertainty, by comparing it with other well-known meta-heuristic algorithms such as the genetic algorithm and particle swarm optimization.

云制造中最重要的问题之一涉及获得最佳制造服务组合解决方案。然而，传统的制造服务组合方法要么专注于面向单一任务的服务组合，要么专注于确定性环境下的优化解决方案。在研究中，提出了一种在不确定环境下具有两个阶段的面向多任务的制造服务组合（MMSC）模型。它处理多任务调度问题，也处理云制造中固有的不确定性和模糊性，包括紧急任务请求的发生和原材料的延迟交货时间。为了求解MMSC模型，提出了一种新的染色体长度可调的基于遗传的超启发式算法(GA-HH)。GA-HH 包含一组低级启发式算法，它们直接对由高级启发式算法（即遗传算法）组织的解域进行操作。最后，通过与其他著名的元启发式算法（例如遗传算法和粒子群算法）进行比较，证明了所提出的 GA-HH 是一种高效、有效和鲁棒的算法，可以解决多任务和不确定性的 MMSC 模型。优化。