On the optimization of material usage in power transformer manufacturing

Chasparis, Georgios C.; Zellinger, Werner; Haunschmid, Verena; Riedenbauer, Markus; Stumptner, Reinhard

doi:10.1109/is.2016.7737384

Cited by 4 publications

(6 citation statements)

References 8 publications

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“…A preliminary version of the proposed methodology first introduced by the authors in [8], however, without providing the details of the optimization algorithm. The present paper provides a ready-to-use comprehensive treatment of one-dimensional cutting-stock problems usually encountered in the industry.…”

Section: Contributionmentioning

confidence: 99%

“…We have currently chosen a rather small number of constraints, namely the job-admissibility and rest-width constraint. However, it is important to point out that additional constraints may also be added, depending on the application of interest, such as the eco-design measures mentioned in [8]. Such constraints may be nonlinear in the parameters.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Cutting-stock problems formulate economic optimization problems aiming at the minimization of stock use so that certain job requirements are satisfied. They Preliminary versions of part of this paper appeared in [8], [30]. The research reported in this article has been supported by the Austrian Ministry for Transport, Innovation and Technology, the Federal Ministry of Science, Research and Economy, and the Province of Upper Austria in the frame of the COMET center SCCH.…”

Section: Introductionmentioning

confidence: 99%

“…In industrial environments, such as in the electrical transformers industry [8], linear-programming techniques may not be appropriate due to the large number of constraints, some of which may be nonlinear in the parameters. Examples of such nonlinear constraints may include constraints imposed through eco-design measures (cf., [8]). Due to these nonlinear constraints, heuristic-based optimization techniques seem more appropriate for addressing this family of problems.…”

Section: Introductionmentioning

confidence: 99%

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An Evolutionary Stochastic-Local-Search Framework for One-Dimensional Cutting-Stock Problems

Chasparis,

Rossbory,

Haunschmid

2017

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We introduce an evolutionary stochastic-local-search (SLS) algorithm for addressing a generalized version of the so-called 1/V/D/R cutting-stock problem. Cutting-stock problems are encountered often in industrial environments and the ability to address them efficiently usually results in large economic benefits. Traditionally linear-programmingbased techniques have been utilized to address such problems, however their flexibility might be limited when nonlinear constraints and objective functions are introduced. To this end, this paper proposes an evolutionary SLS algorithm for addressing one-dimensional cutting-stock problems. The contribution lies in the introduction of a flexible structural framework of the optimization that may accommodate a large family of diversification strategies including a novel parallel pattern appropriate for SLS algorithms (not necessarily restricted to cutting-stock problems). We finally demonstrate through experiments in a real-world manufacturing problem the benefit in cost reduction of the considered diversification strategies.

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Section: Contributionmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Evolutionary Stochastic-Local-Search Framework for One-Dimensional Cutting-Stock Problems

Chasparis,

Rossbory,

Haunschmid

2017

Preprint

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“…Introduction 2DLCSP appears in many manufacturing industrial areas. The most common are the transformer core (Gerstl and Karisch, 1997;Chasparis et al, 2016;Lettner et al, 2020), electric motor (Cui et al, 2007), paper (Chen et al, 2019;Khan et al, 2020) and stainless steel (Cui et al, 2009;Karelahti, 2011) manufacturing industries. Transformer core manufacturers use laminations cut from silicon steel coils to make transformer core.…”

mentioning

confidence: 99%

Manufacturing-oriented silicon steel coil lengthwise cutting stock problem with useable leftover

Yan

2021

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PurposeThis paper propose an algorithm for the multiple silicon steel coils multiperiod two-dimensional lengthwise cutting stock problem (m2DLCSP), so as to minimize the total cost of materials and production.Design/methodology/approachThe authors propose a sequential leftovers utilization correction (SLUC) algorithm for the m2DLCSP. The algorithm primarily considers three optimization strategies. First, it considers usable leftovers to simplify the cutting process and improve material utilization. The total quantity and types of leftovers should be limited in order to avoid leftover overstock. Second, it uses a splice method of items to improve the generated cutting plan. Third, it takes into account operational restrictions in the cutting operations. Operational restrictions include imposing maximum and minimum lengths on the cutting patterns, and the limitation of cutting knives at the slitting machines.FindingsSeveral sets of benchmark with real-world and randomly generated instances are provided to evaluate the algorithm. Compared with literature algorithm and current procedure applied in enterprises, the computational results indicate that proposed algorithm can effectively reduce the total cost, and the computation time is reasonable for practical use.Social implicationsThis algorithm can effectively reduce the total cost.Originality/valueThe proposed method can effectively applied to solve the m2DLCSP and improve the economic efficiency of enterprises.

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