.png){kind=icon}
.png){kind=icon}
Return
A multi-phase integrated scheduling method for cloud remanufacturing systems
.png){kind=icon}
OA
AI
DOI：10.1016/j.aei.2024.102802.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The cloud remanufacturing system embraces a series of interdependent remanufacturing macroscopic phases (RMAs) with intricate precedence relationships, increasing the complexity of task scheduling and resource allocation. Thus, the multi-phase integrated scheduling is necessary to manage remanufacturing tasks and optimize resources and capabilities effectively in the cloud environment. This research investigates the multiphase integrated scheduling problem for cloud remanufacturing system involving a series of RMAs including initial inspection, disassembly, reprocessing, reassembly, and final test. A mathematical model is created to explain the scheduling issue using the suggested cloud remanufacturing framework. Due to the high complexity of integrated scheduling, traditional meta-heuristic algorithms cannot be directly applied to solving the problem. Thus, an improved whale optimization algorithm (IWOA) incorporating the self-adaptive weighting and quadratic interpolation techniques is proposed for addressing the studied problem efficiently. A case study is designed and conducted, and the findings indicate that the IWOA is more effective than other methods in addressing the proposed complex scheduling issues with better accuracy, faster computation, and improved convergence efficiency.
Keywords:
Cloud remanufacturing
Multi-phase integrated scheduling
Remanufacturing system
Improved whale optimization algorithm
Service searching and matching
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.9
Papers:
4.0K
Citations:
1.7W