Efficient cutting stock optimization strategies for the steel industry

Area of Science:

• Operations Research
• Industrial Engineering
• Computational Optimization

Background:

• Cutting stock problems are prevalent in industries like steel manufacturing, leading to significant material waste and high operational costs.
• Existing methods often struggle to efficiently balance machine constraints, cutting conditions, and customer order demands.
• Optimizing cutting processes is crucial for reducing waste and improving economic efficiency in manufacturing.

Purpose of the Study:

• To develop a novel mathematical model and optimization algorithm for solving the steel cutting stock problem.
• To incorporate machine specifications and cutting conditions as constraints within the optimization model.
• To significantly reduce waste, planning time, and costs associated with industrial cutting operations.

Main Methods:

• Developed a three-step solution process: problem representation, problem space reduction, and optimal solution search.
• Introduced a new Adaptive Pathfinding Optimization Algorithm combining Wandering Ant Colony Optimization and a brute force method.
• Modeled feasible cutting solutions based on pre-cut steel bars and customer orders, eliminating suboptimal solutions.

Main Results:

• The algorithm reduced the number of planners from four to one and cutting planning time from six hours to under one hour.
• Achieved an average cost saving of USD 3.95 per ton, representing a 52.18% reduction of the baseline cost.
• Demonstrated applicability to other cutting stock problems, including paper, metal rod, and wood plank cutting.

Conclusions:

• The proposed Adaptive Pathfinding Optimization Algorithm offers a highly effective solution for the steel cutting stock problem.
• The developed mathematical model and algorithm lead to substantial economic benefits and operational efficiencies in manufacturing.
• This approach provides a versatile tool for optimizing various industrial cutting stock applications.