Game theory–based multi-task scheduling in cloud manufacturing using an extended biogeography-based optimization algorithm

Abstract

Cloud manufacturing is an emerging paradigm of global manufacturing networks. Through centralized management and operation of distributed manufacturing services, it can deal with different requirement tasks submitted by multiple customers in parallel. Therefore, the cloud manufacturing multi-task scheduling problem has attracted increasing attention from researchers. This article proposes a new cloud manufacturing multi-task scheduling model based on game theory from the customer perspective. The optimal result for a cloud manufacturing platform is derived from the Nash equilibrium point in the game. As the cloud manufacturing multi-task scheduling problem is known as an NP-hard combinatorial optimization problem, an extended biogeography-based optimization algorithm that embeds three improvements is presented to solve the corresponding model. Compared with the basic biogeography-based optimization algorithm, genetic algorithm, and particle swarm optimization, the experimental simulation results demonstrate that the extended biogeography-based optimization algorithm finds a better schedule for the proposed model. Its benefit is to provide each customer with reliable services that fulfill the demanded manufacturing tasks at reasonable cost and time.