A Preference Information of Multi-Participant Based Evolutionary Algorithm for Manufacturing Task Scheduling in Cloud Manufacturing System

Cloud manufacturing task scheduling (CMTS) gains widespread attention in the cloud manufacturing (CMfg) community. At present, most related researches are mainly aimed at task-oriented scheduling and there are few researches considering user’ characteristics, like user preferences. In this paper, we integrate the preference information presented as dual hesitation fuzzy (DHF) elements into the CMTS process. First, the available manufacturing services (MSs) are obtained with multi-attribute decision making method. Then a bi-objective CMTS optimization model considering user utility and energy consumption is constructed. Meanwhile, a preference information of multi-participant based evolutionary algorithm (PIMP-EA) is designed for solving the problem. Specifically, matching satisfaction degrees of MSs is adopted in service selection of population initialization. And preference information of all users and platform operator is integrated into environment selection to meet preference demands. Finally, multiple experimental cases are designed to evaluate the effectiveness and performance of the presented approach. The results demonstrate PIMP-EA is competitive to address preference-based CMTS problem compared with other state-of-the-art evolutionary algorithms. It can satisfy preference demands of different user as much as possible