Resource allocation model in cloud manufacturing

Abstract

Allocation of resources in cloud manufacturing is one of the key points of cloud manufacturing technology. To optimize cloud manufacturing resource management, it is indispensable to improve the process and efficiency of scheduling by matching jobs with resources according to the size of the job and establishing a four-level structure for resources based on the enterprise level, workshop level, primitive cell and service level. A resource scheduling model containing four indicators of cost, time, quality and risk with their own mathematical expressions is proposed. We also simulate the model with a new swap-shuffled leap-frog algorithm (SSFLA). Finally, we test the algorithm with different example scales and different end conditions and compare it with particle swarm optimization (PSO) and genetic algorithm (GA). The result shows that SSFLA performs well in convergence speed and robustness and does much better than PSO and GA. This algorithm provides an alternative choice for allocation of resources in cloud manufacturing model.