A Scheduling Algorithm for Multi-Workshop Production Based on BOM and Process Route

Qiao, Lihong; Zhang, Zhenwei; Huang, Zhicheng

doi:10.3390/app11115078

Cited by 7 publications

(1 citation statement)

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“…Considering workshop process and production scheduling, literature [10] based on the dependence of the production planning, and scheduling problem of continuous production line on timing, a repair and optimization solution is proposed to solve the problem of energy efficiency in the production process. For the scheduling problem of complex products in multiworkshop production, literature [11] studies the characteristics of BOM structure and process route of complex products. Based on the construction of multilevel process network diagram, an improved particle swarm optimization algorithm is used to ensure the effectiveness of the algorithm search path.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Scheduling Method for Data Resources of Production Process Based on Multicommunity Collaborative Search Algorithm

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%