A column generation on two-dimensional cutting stock problem with fixed-size usable leftover and multiple stock sizes

2020 ◽  
Vol 35 (2) ◽  
pp. 273
Author(s):  
Supphakorn Sumetthapiwat ◽  
Boonyarit Intiyot ◽  
Chawalit Jeenanunta
Keyword(s):  
Column Generation ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Fixed Size

A column generation heuristic for the two-dimensional two-staged guillotine cutting stock problem with multiple stock size

2012 ◽  
Vol 218 (1) ◽  
pp. 251-260 ◽  
Author(s):  
Fabio Furini ◽  
Enrico Malaguti ◽  
Rosa Medina Durán ◽  
Alfredo Persiani ◽  
Paolo Toth
Keyword(s):  
Column Generation ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Stock Size ◽  
Guillotine Cutting

scholarly journals Set Covering Model in Solving Multiple Cutting Stock Problem

2020 ◽  
Vol 5 (4) ◽  
pp. 121
Author(s):  
Sisca Octarina ◽  
Devi Gusmalia Juita ◽  
Ning Eliyati ◽  
Putra Bahtera Jaya Bangun
Keyword(s):  
Column Generation ◽  
Pattern Generation ◽  
Cutting Stock Problem ◽  
Set Covering ◽  
Cutting Stock ◽  
Second Stage ◽  
Generation Technique ◽  
Cutting Pattern ◽  
Covering Model

Cutting Stock Problem (CSP) is the determination of how to cut stocks into items with certain cutting rules. A diverse set of stocks is called multiple stock CSP. This study used Pattern Generation (PG) algorithm to determine cutting pattern, then formulated it into a Gilmore and Gomory model and solved by using Column Generation Technique (CGT). Set Covering model was generated from Gilmore and Gomory model. Based on the results, selected cutting patterns in the first stage can be used in the second stage. The combination of patterns generated from Gilmore and Gomory model showed that the use of stocks was more effective than Set Covering model.  

A Hybrid Genetic Algorithm for Optimization of Two-Dimensional Cutting-Stock Problem

2012 ◽  
pp. 56-71
Author(s):  
Ahmed Mellouli ◽  
Faouzi Masmoudi ◽  
Imed Kacem ◽  
Mohamed Haddar
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Production Cost ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Genetic Approach ◽  
Cutting Pattern

In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfill the customer orders with minimal production cost. The effectiveness of the hybrid genetic approach has been evaluated through a set of instances which are both randomly generated and collected from the literature.

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