Dynamic Spatiotemporal scheduling of hull parts under complex constraints in shipbuilding workshop

ABSTRACT

The extremely competitive shipbuilding sector is going through a digital transformation in the context of Industry 4.0. The spatiotemporal problem has gained considerable attention during hull construction, particularly for blocks. There are fewer studies on intermediate products at lower levels, such as the dynamic spatiotemporal scheduling for hull parts under complex constraints in subassembly workshops. Therefore, this paper proposes a multi-queue two-level optimization method along with a dynamic spatiotemporal scheduling model based on the multi-directed acyclic graph (multi-DAG) and queueing theory. Firstly, a mathematical description of the complex constraints imposed by processing tasks and various resource types is provided. Then, a dynamic spatiotemporal scheduling model and an objective function are proposed to minimize the average time for parts in the system. At the sequencing level, different priority determination methods for queue events based on an improved genetic algorithm and the contribution ratio for release space strategy are proposed. At the service level, the allocation strategies for space and workers are constructed to improve utilization. Finally, several simulation experiments are conducted using the data gathered from the workshop. The proposed method is compared with the heuristic rules and combination rules using several queueing indicators and the space utilization vs. time graphs.

KEYWORDS:

• Ship industry
• dynamic scheduling
• space resource
• precedence constraints
• queueing model
• spatiotemporal problem

Acknowledgments

This work was supported by the Natural Science Foundation of Guangdong Province, China, under Grant 2021A1515011946.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the Natural Science Foundation of Guangdong Province for Distinguished Young Scholars [2021A1515011946].