An effective adaptive adjustment model of task scheduling and resource allocation based on multi-stakeholder interests in cloud manufacturing

Abstract

Cloud manufacturing (CMfg) is a new manufacturing mode emerging in the global manufacturing industry. One of the key issues in CMfg is task scheduling and resource allocation (TSRA), which is to allocate suitable resources for multi-tasks while satisfying the interests of multi-stakeholders. Among them, dynamic TSRA is a challenging but crucial problem to ensure the smooth operation of CMfg system since it involves several exceptions. Under these contexts, this study first analyzes the optimized objectives and conflict situations that may damage the interests of multi-stakeholders in dynamic TSRA process. And then, four adaptive adjustment strategies are designed to deal with these conflict situations and ensure the smooth operation. After that, an adaptive adjustment TSRA model based on multi-stakeholder interests (TSRA-MSI) is proposed. To solve the problem, a multi-objective algorithm called HHO-NSGA2 is proposed by combining the advantages of standard Harris Hawks Optimizer and Non-dominated Sorting Genetic Algorithm-II, which contains several problem-specific optimization strategies. In the numerical experiments, the superiority of HHO-NSGA2 is demonstrated by comparing with other five algorithms in terms of convergence, diversity, and comprehensive performance. Finally, a case study is conducted under the actual auto parts production environment, and the results also demonstrate the effectiveness of the proposed TSRA-MSI model and HHO-NSGA2 algorithm.