Multitask-oriented manufacturing service composition in an uncertain environment using a hyper-heuristic algorithm

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.