A preference-based multi-objective evolutionary algorithm for user needs-driven multi-task scheduling in cloud manufacturing

Juan Liu

doi:10.1080/0951192X.2025.2544542

A preference-based multi-objective evolutionary algorithm for user needs-driven multi-task scheduling in cloud manufacturing

2025-09-01

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AI

DOI：10.1080/0951192X.2025.2544542

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Cloud manufacturing task scheduling (CMTS) has gained widespread attention in the cloud manufacturing (CMfg) community. At present, most related research primarily focuses on task-oriented scheduling, with few studies considering user characteristics such as preferences. In this paper, we integrate the preference information presented as dual hesitant fuzzy (DHF) elements into the CMTS process. First, the available manufacturing services (MSs) are obtained with the 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 proposed to solve the problem. Specifically, matching satisfaction degrees of MSs are adopted in service selection of population initialization. Moreover, preference information of all users and the 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 that PIMP-EA is competitive in addressing preference-based CMTS problems compared with other state-of-the-art evolutionary algorithms. It can satisfy the preference demands of different users as much as possible.

Keyword:

Cloud manufacturing

multiple users

manufacturing service matching

manufacturing task scheduling

preference information

A preference-based multi-objective evolutionary algorithm for user needs-driven multi-task scheduling in cloud manufacturing

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