LAUSR

Cloud manufacturing (CMfg) is a new manufacturing mode formed by the integration of information technology and communication technology with manufacturing. As a core role in CMfg, the CMfg platform is responsible for decomposing a large number of tasks from demander and allocating them to available services. The scheduling requires comprehensive consideration of the relevance, complexity and dynamics of task and service. When the decomposable task is multi-composite, how to allocate the optimum services to multi-composite tasks is a tricky and important problem. To solve the issue, a hierarchical scheduling model for multi-composite tasks is proposed, which is divided into user-level scheduling and sublevel scheduling to reduce the scale and difficulty of scheduling. User-level scheduling achieves two-way matching between demander and provider based on various attributes. For the sublevel scheduling, an improved firefly genetic algorithm is created for multi-objective optimisation. A detailed analysis of the hierarchical scheduling strategy is performed by testing several different instances. Experimental results indicate that this strategy reduces the complexity than collective scheduling; and has a better comprehensive balance effect on multiple optimisation goals than sequential scheduling.