.png){kind=icon}
Return
Collaborative optimization for logistics and processing services in cloud manufacturing
.png){kind=icon}
PRE
AI
DOI：10.1016/j.rcim.2020.102094.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Efficient service scheduling is an important technique to support collaborative manufacturing platforms such as IoT-enable manufacturing systems and cloud manufacturing. In the past few years, optimization problems for processing services have attracted the most attention of researchers and practitioners in terms of task matching, service selection, and scheduling. Logistics services, as another important kind of services in the cloud manufacturing environment, need to be explored further, beyond parameters of costs and time, in order to obtain more efficient task execution and more timely product delivery. In this paper, we consider the problem of synchronous scheduling of logistics services and processing services in cloud manufacturing. Based on the mathematical description, we present a collaborative optimization algorithm for logistics and processing services which we call COOPS to generate scheduling solutions for both processing tasks and logistics tasks at the same time. Typical optimization algorithms such as pattern search, particle swarm optimization and simulated annealing are compared with the proposed algorithm to show their performance on the average completion time of all manufacturing tasks. Results show that the proposed method obtains a shorter average completion time for all tasks in different scenarios.
Keywords:
Cloud manufacturing
Optimization scheduling
Logistics
Production
Transportation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
R
IF:
11.4
Papers:
3.3K
Citations:
1.3W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveAn event-triggered dynamic scheduling method for randomly arriving tasks in cloud manufacturing
PREAI
ShareSave
View more