TY - JOUR
T1 - Strip generation algorithms for constrained two-dimensional two-staged cutting problems
AU - Hifi, Mhand
AU - M'Hallah, Rym
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2006/7/16
Y1 - 2006/7/16
N2 - The constrained two-dimensional cutting (C_TDC) problem consists of determining a cutting pattern of a set of n small rectangular piece types on a rectangular stock plate of length L and width W, as to maximize the sum of the profits of the pieces to be cut. Each piece type i, i = 1, ..., n, is characterized by a length li, a width wi, a profit (or weight) ci and an upper demand value bi. The upper demand value is the maximum number of pieces of type i which can be cut on rectangle (L, W). In this paper, we study the two-staged fixed orientation C_TDC, noted FC_2TDC. It is a classical variant of the C_TDC where each piece is produced, in the final cutting pattern, by at most two guillotine cuts, and each piece has a fixed orientation. We solve the FC_2TDC problem using several approximate algorithms, that are mainly based upon a strip generation procedure. We evaluate the performance of these algorithms on instances extracted from the literature.
AB - The constrained two-dimensional cutting (C_TDC) problem consists of determining a cutting pattern of a set of n small rectangular piece types on a rectangular stock plate of length L and width W, as to maximize the sum of the profits of the pieces to be cut. Each piece type i, i = 1, ..., n, is characterized by a length li, a width wi, a profit (or weight) ci and an upper demand value bi. The upper demand value is the maximum number of pieces of type i which can be cut on rectangle (L, W). In this paper, we study the two-staged fixed orientation C_TDC, noted FC_2TDC. It is a classical variant of the C_TDC where each piece is produced, in the final cutting pattern, by at most two guillotine cuts, and each piece has a fixed orientation. We solve the FC_2TDC problem using several approximate algorithms, that are mainly based upon a strip generation procedure. We evaluate the performance of these algorithms on instances extracted from the literature.
KW - Approximate algorithms
KW - Cutting problems
KW - Dynamic programming
KW - Optimization
KW - Single constrained knapsack problems
UR - http://www.scopus.com/inward/record.url?scp=33644687223&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2004.10.020
DO - 10.1016/j.ejor.2004.10.020
M3 - Article
AN - SCOPUS:33644687223
SN - 0377-2217
VL - 172
SP - 515
EP - 527
JO - EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
JF - EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
IS - 2
ER -