Collaborative optimization of layout and cutting scheduling for large-scale customized metal structural parts

Area of Science:

• Manufacturing Engineering
• Operations Research
• Computational Intelligence

Background:

• Optimizing layout and cutting schedules is crucial for manufacturing efficiency.
• Balancing makespan, delay penalties, and sheet utilization is a complex challenge for custom metal parts.

Purpose of the Study:

• To develop a collaborative optimization mechanism for layout and cutting scheduling.
• To improve sheet utilization, minimize makespan, and reduce delay penalties for large-scale customized metal structural parts.

Main Methods:

• A two-stage model was proposed: Stage 1 for layout optimization (maximizing sheet utilization) and Stage 2 for cutting scheduling (minimizing makespan and delay penalty).
• Stage 1 utilized the Improved Grey Wolf Optimizer (IGWO) algorithm.
• Stage 2 employed the Modified Non-dominated Sorting Genetic Algorithm (MNSGA-II) with a production scheduling heuristic.

Main Results:

• The IGWO algorithm effectively maximized sheet utilization in the layout stage.
• The MNSGA-II algorithm successfully minimized makespan and delay penalties in the cutting scheduling stage.
• Experimental results validated the effectiveness of the proposed two-stage model and the enhanced algorithms.

Conclusions:

• The proposed two-stage model provides an effective approach for cooperative optimization of layout and cutting scheduling.
• The IGWO and MNSGA-II algorithms demonstrate superior performance in addressing the complexities of custom metal part manufacturing.
• This research offers a valuable scientific solution for enterprises facing urgent optimization challenges in production processes.