Ad hoc heuristic for the cover printing problem

Romero, David; Alonso-Pecina, Federico

doi:10.1016/j.disopt.2011.10.002

Cited by 9 publications

(27 citation statements)

References 8 publications

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“…The first two problem instances LPP18 (L = 18, S = 14, T = 3, 4, 5) and LPP22 (L = 22, S = 15, T = 3, 4, 5) are proposed in [1,8] and Appendix A and the third problem instance LPP100 (L = 100, S = 25, T = 8) is proposed in [8]. The data for these problem instances can be found in the website www.matcuer.unam.mx/~davidr/cpp.html and the best known results can be found in Appendices B-D.…”

Section: Numerical Results For the Lpp Problemmentioning

confidence: 99%

“…The solutions obtained with the new GRASP procedure are compared to those obtained in [1] and [8] using the following waste measure percentage…”

Section: Numerical Results For the Lpp Problemmentioning

confidence: 99%

“…In this paper, we completely redesign the GRASP procedure in order to solve both the label printing and the cover printing problems. Moreover new numerical instances are considered and the obtained results are not only compared with the previous GRASP results but also with those of two existing methods in the literature [1,8].…”

Section: Introductionmentioning

confidence: 94%

“…Part 1: T −j+1 candidate templates (n t ̸ = 0) are build (see lines [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. In lines 9-14, m t labels are assigned to a candidate template t. In lines 15-22, some labels are eventually removed from the candidate template t in order to be able to split the number of required copies of m t − n t labels.…”

Section: The Construction Phasementioning

confidence: 99%

“…In [6], a branch and price algorithm is presented but the results are not quite satisfactory since only bounds on the optimal solution are provided. More recently, a GRASP method has been proposed in [7] and an ad hoc original heuristic has been developed in [8].…”

Section: Introductionmentioning

confidence: 99%

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Towards an efficient resolution of printing problems

Tuyttens¹,

Vandaele²

2014

Discrete Optimization

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a b s t r a c tThis paper addresses two different problems coming from the printing industry: the label printing problem and the cover printing problem. In both cases, the problem consists in the assignment of a fixed number of labels on different templates in order to fulfill the printing requirements and to minimize the waste of excessive printed labels in the first case or to minimize the total production cost in the second case. In these problems, allocating suitable labels to each template is a strategical task to improve the efficiency of the printing process. As the considered problems are hard, we propose a new GRASP algorithm to solve these problems. The proposed algorithm is tested on some reference instances, comparing the obtained results with those found in the literature. The results prove that the GRASP algorithm is particularly well suited to these problems.

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Section: Numerical Results For the Lpp Problemmentioning

confidence: 99%

“…The solutions obtained with the new GRASP procedure are compared to those obtained in [1] and [8] using the following waste measure percentage…”

Section: Numerical Results For the Lpp Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 94%

Section: The Construction Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards an efficient resolution of printing problems

Tuyttens¹,

Vandaele²

2014

Discrete Optimization

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Planning of a make-to-order production process in the printing industry

2014

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Offset printing is a common method to produce large amounts of printed matter. We consider a real-world offset printing process that is used to imprint customer-specific designs on napkin pouches. The production equipment used gives rise to various technological constraints. The planning problem consists of allocating designs to printing-plate slots such that the given customer demand for each design is fulfilled, all technological and organizational constraints are met and the total overproduction and setup costs are minimized. We formulate this planning problem as a mixed-binary linear program, and we develop a multi-pass matchingbased savings heuristic. We report computational results for a set of problem instances devised from real-world data.

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