Abstract: In order to implement the scheduling of multiple tasks with the same type in cloud manufacturing systems, a mathematical model is established and is solved by using a discrete particle swarm-genetic hybrid algorithm with two objectives, namely the least total completing time and the least cost of all tasks being considered simultaneously. The hybrid algorithm employs integer coding method to establish the mapping between particle location matrix and service allocation scheme, and introduces the crossover and mutation idea of genetic algorithm to update particle swarm position with four formulas being conditionally used in a progressive and overlaying way, and thus the diversity of groups is ensured effectively. Simulated results indicate that the proposed algorithm is of high effectiveness and execution efficiency.

Key words: cloud manufacturing, multi-task scheduling, service-oriented architecture, service combination, multi-objective optimization, particle swarm optimization, discrete particle swarm-genetic hybrid algorithm