Multi-objective optimisation of multi-task scheduling in cloud manufacturing

Cloud manufacturing is a consumer-centric requirement-driven manufacturing paradigm that integrates distributed resources for providing services to consumers in an on-demand manner. Scheduling of multiple tasks is an important technical means for satisfying consumer requirements in cloud manufacturi...

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Bibliographic Details
Published in:International journal of production research 2019-06, Vol.57 (12), p.3847-3863
Main Authors: Li, Feng, Zhang, Lin, Liao, T. W., Liu, Yongkui
Format: Article
Language:English
Subjects:
Algorithms
cloud manufacturing
Clouds
Dependence
Heuristic methods
Manufacturing
meta-heuristic
multi-objective optimisation
Multi-task scheduling
Multiple objective analysis
Optimization
Pareto set
Production scheduling
Scheduling
Task scheduling
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Summary:Cloud manufacturing is a consumer-centric requirement-driven manufacturing paradigm that integrates distributed resources for providing services to consumers in an on-demand manner. Scheduling of multiple tasks is an important technical means for satisfying consumer requirements in cloud manufacturing. However, high individualised requirements and the associated complex task structures complicate the task scheduling in cloud manufacturing. This paper establishes a more comprehensive model for scheduling multiple distinct tasks with complicated manufacturing processes. The hierarchical relationships (a mixture of dependency and independency) of subtasks within tasks are considered. The objectives involve three kinds of time and cost factors, namely processing time, setup time, transfer time and the respective cost. In addition, service quality is also considered into the optimisation objective. Two multi-objective-meta-heuristic algorithms, i.e. ACO-based multi-objective algorithm (MACO) and NSGA-II-based multi-objective algorithm (MGA), are designed to solve the scheduling problem. A detailed analysis of the performance of the two algorithms is performed by applying them to several different scheduling instances. Experimental results indicate that in most cases the MACO algorithm can obtain a more diverse set of Pareto solutions hence offering more alternatives to meet widely different users' needs.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2018.1538579