Mathematical Modeling and Optimal Blank Generation in Glass Manufacturing

This paper discusses the stock size selection problem (Chambers and Dyson, 1976), which is of relevance in the float glass industry. Given a fixed integer N, generally between 2 and 6 (but potentially larger), we find the N best sizes for intermediate stock from which to cut a roster of orders. An o...

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Main Authors: Raymond Phillips, Matthew Woolway, Dario Fanucchi, M. Montaz Ali
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2014/959453
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spelling doaj-e0235b6a79d64310906200f906b450f02020-11-24T21:06:36ZengHindawi LimitedJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/959453959453Mathematical Modeling and Optimal Blank Generation in Glass ManufacturingRaymond Phillips0Matthew Woolway1Dario Fanucchi2M. Montaz Ali3School of Computational and Applied Mathematics, University of the Witwatersrand, 1 Jan Smuts Avenue, Private Bag 03, WITS 2050, Johannesburg, South AfricaSchool of Computational and Applied Mathematics, University of the Witwatersrand, 1 Jan Smuts Avenue, Private Bag 03, WITS 2050, Johannesburg, South AfricaSchool of Computational and Applied Mathematics, University of the Witwatersrand, 1 Jan Smuts Avenue, Private Bag 03, WITS 2050, Johannesburg, South AfricaSchool of Computational and Applied Mathematics, University of the Witwatersrand, 1 Jan Smuts Avenue, Private Bag 03, WITS 2050, Johannesburg, South AfricaThis paper discusses the stock size selection problem (Chambers and Dyson, 1976), which is of relevance in the float glass industry. Given a fixed integer N, generally between 2 and 6 (but potentially larger), we find the N best sizes for intermediate stock from which to cut a roster of orders. An objective function is formulated with the purpose of minimizing wastage, and the problem is phrased as a combinatorial optimization problem involving the selection of columns of a cost matrix. Some bounds and heuristics are developed, and two exact algorithms (depth-first search and branch-and-bound) are applied to the problem, as well as one approximate algorithm (NOMAD). It is found that wastage reduces dramatically as N increases, but this trend becomes less pronounced for larger values of N (beyond 6 or 7). For typical values of N, branch-and-bound is able to find the exact solution within a reasonable amount of time.http://dx.doi.org/10.1155/2014/959453
collection DOAJ
language English
format Article
sources DOAJ
author Raymond Phillips
Matthew Woolway
Dario Fanucchi
M. Montaz Ali
spellingShingle Raymond Phillips
Matthew Woolway
Dario Fanucchi
M. Montaz Ali
Mathematical Modeling and Optimal Blank Generation in Glass Manufacturing
Journal of Applied Mathematics
author_facet Raymond Phillips
Matthew Woolway
Dario Fanucchi
M. Montaz Ali
author_sort Raymond Phillips
title Mathematical Modeling and Optimal Blank Generation in Glass Manufacturing
title_short Mathematical Modeling and Optimal Blank Generation in Glass Manufacturing
title_full Mathematical Modeling and Optimal Blank Generation in Glass Manufacturing
title_fullStr Mathematical Modeling and Optimal Blank Generation in Glass Manufacturing
title_full_unstemmed Mathematical Modeling and Optimal Blank Generation in Glass Manufacturing
title_sort mathematical modeling and optimal blank generation in glass manufacturing
publisher Hindawi Limited
series Journal of Applied Mathematics
issn 1110-757X
1687-0042
publishDate 2014-01-01
description This paper discusses the stock size selection problem (Chambers and Dyson, 1976), which is of relevance in the float glass industry. Given a fixed integer N, generally between 2 and 6 (but potentially larger), we find the N best sizes for intermediate stock from which to cut a roster of orders. An objective function is formulated with the purpose of minimizing wastage, and the problem is phrased as a combinatorial optimization problem involving the selection of columns of a cost matrix. Some bounds and heuristics are developed, and two exact algorithms (depth-first search and branch-and-bound) are applied to the problem, as well as one approximate algorithm (NOMAD). It is found that wastage reduces dramatically as N increases, but this trend becomes less pronounced for larger values of N (beyond 6 or 7). For typical values of N, branch-and-bound is able to find the exact solution within a reasonable amount of time.
url http://dx.doi.org/10.1155/2014/959453
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