Efficient cutting stock optimization strategies for the steel industry
Chattriya Jariyavajee, Suthida Fairee, Charoenchai Khompatraporn, Jumpol Polvichai, Booncharoen Sirinaovakul
TL;DR
This paper presents a new algorithm to optimize steel cutting, reducing planning time and costs significantly in the steel industry.
Contribution
A novel Adaptive Pathfinding Optimization Algorithm combining Wandering Ant Colony Optimization and brute force methods is introduced.
Findings
The algorithm reduced planning time from six hours to under one hour.
It cut the number of planners needed from four to one.
The algorithm achieved an average cost saving of USD 3.95 per ton.
Abstract
This study addresses a cutting stock problem in steel cutting industry by developing a mathematical model in which machine specifications and cutting conditions are constraints. The solution process involves three key steps: (i) Problem representation, where feasible cutting solutions are modeled based on pre-cut steel bars and customer orders, (ii) Problem space reduction, which reduces the problem space by eliminating suboptimal solutions and following manufacturer loss limits, and (iii) Optimal solution search, whereas the optimal solution is identified using a new Adaptive Pathfinding Optimization Algorithm. This algorithm combines a newly proposed Wandering Ant Colony Optimization with a brute force method, and uses specific conditions to determine which of these two approaches to be used to obtain the solution. The proposed algorithm can also be applied to other cutting stock…
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Taxonomy
TopicsOptimization and Packing Problems · Advanced Manufacturing and Logistics Optimization · Manufacturing Process and Optimization