Mixture Pulsation Model-Based Decision-Making for Resource-Efficient Scheduling in Large-Scale Assembly Lines

Area of Science:

• Industrial Engineering
• Operations Research
• Manufacturing Systems

Background:

• Large-scale assembly production faces challenges in resource allocation, workstation coordination, and congestion.
• Massive scale, complex tasks, and fluctuating constraints exacerbate these issues.

Purpose of the Study:

• To propose a resource-efficient scheduling decision-making method for large-scale assembly production.
• To address inefficient resource allocation, coordination difficulties, and congestion.

Main Methods:

• Developed a mixture pulsation model (DMMPM) for resource-efficient scheduling.
• Defined quantitative criteria for pulsation rhythm (takt-time) consistency.
• Designed a spatiotemporally constrained task-allocation method.
• Proposed a bi-level 'scheduling-collaboration' architecture.

Main Results:

• The DMMPM integrates production rhythm alignment with workforce optimization.
• Task allocation balances interstation resource demand conflicts and rhythm synchronization.
• The bi-level architecture enables decentralized decision-making and global optimization.
• Validated model using ILOG CPLEX.

Conclusions:

• DMMPM significantly reduces integrated scheduling costs in large-scale aircraft manufacturing.
• The method demonstrates superior decision-making capability and improved control of pulsation rhythm compared to conventional approaches.