1570. A large neighborhood search for the 2D bounded-sized cutting stock problem
Invited abstract in session MB-21: 2D Cutting and Packing, stream Cutting and packing (ESICUP).
Monday, 10:30-12:00Room: Esther Simpson 2.12
Authors (first author is the speaker)
| 1. | Khadija Alice HADJ SALEM
|
| NUMA, KU Leuven | |
| 2. | Alexis Robbes
|
| Université de Technologie de Troyes |
Abstract
We introduce the two-dimensional bounded-sized cutting stock problem, referred to as (2D-BSCSP), which involves optimizing the cutting of small rectangular items from a set of larger rectangular plates, where the plates have bounded dimensions.
In the (2D-BSCSP), the cutting patterns are restricted to non-exact two-stage guillotine cuts.
The objective function is to minimize the total amount of raw material used, effectively reducing waste.
This is a crucial concern in manufacturing industries, where material cost is a significant factor in overall production efficiency.
To address this challenge, we propose a Mixed Integer Linear Programming (MILP) formulation based on strip types. In addition, we introduce a Large Neighborhood Search (LNS) algorithm to solve larger instances.
To evaluate the performance of the proposed approaches, computational experiments are conducted on a set of randomly generated instances, as well as on adapted 2D cutting stock problem instances drawn from the existing literature, with the aim of providing insights into their practical applicability and efficiency in solving real-world cutting stock problems.
Keywords
- Cutting and Packing
- Programming, Mixed-Integer
- Metaheuristics
Status: accepted
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