Abstract
Aiming at the problem of low response speed and unbalanced distribution of data resources of production process (DRPP) for the distributed workshop production environment, an optimization scheduling method of DRPP based on a multicommunity cooperative search algorithm is proposed. A heuristic data resource service scheduling framework including a load manager and dynamic scheduling engine is first built to deal with the uncertainty of data resource service response and the imbalance of resource allocation; a core scheduling optimization mathematical model with the objectives: resource service efficiency, reduced response time, and load balancing, is established. Then, a multicommunity cooperative search algorithm for the scheduling model is presented, and the mapping relationship between the particle position vector and resource allocation is established via binary coding. Thus, the optimization algorithm is mapped to discrete data space, and the multicommunity bidirectional driving evolutionary mechanism is used to realize the cooperative and interactive search between common and model community, which enhances the adaptability of the algorithm to dynamic random scheduling tasks. Finally, the effectiveness of the proposed method is verified by an example of multiprocess quality prediction service scheduling in silk production process, which provides an effective means for solving the complex scheduling problem of production process data.
Highlights
It is noted that the traditional manufacturing mode, data of information flow, material flow, and control flow are still isolated from each other in each stage of production execution, and it is difficult to form a joint force due to the lack of effective data resource scheduling mechanism, which restricts the further improvement of production efficiency and system intelligence level [4]
This paper addresses the multitask adaptive scheduling of data resources of production process (DRPP)
A heuristic scheduling framework are employed to deal with the uncertainty of DRPP service response and the imbalance of resource allocation
Summary
The intelligent workshop integrates modern sensing technology, network technology, automation technology, and other advanced technologies, and a large number of intelligent equipment such as sensors and data acquisition devices have been put into use in the workshop [1–3]. The data have no subjective initiative; the data-based analysis and processing algorithm can not actively serve the business needs such as perception, decision-making, and execution of the production process; and the current research has not comprehensively considered the coupling and impact among demand, service, resources, and energy efficiency in the production process. Literature [12] considers the problems of process connection and blocking of prefabricated parts in the process of workshop assembly line operation and establishes a scheduling model to minimize the total penalty cost of advance and delay, which improves the production efficiency of Prefabrication Yard. This study is to integrate the load manager and dynamic task scheduling engine, and combine them with scheduling processes to form a scheduling scheme, so as to provide intelligent support for production process
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