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A schematic illustration of trim loss problem.

A schematic illustration of trim loss problem.

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The “trim loss problem” (TLP) is one of the most challenging problems in context of optimization research. It aims at determining the optimal cutting pattern of a number of items of various lengths from a stock of standard size material to meet the customers’ demands that the wastage due to trim loss is minimized. The resulting mathematical model i...


... Suppose if a paper reel to expansiveness Bmax is being sliced in divergent sizes which satisfy client requests. [20] Then interest conditions are given, fabrication and algae growth, as well as toxicity to exposed communities, thermal impact, and colour issues. [21] ...
The "trim Trim loss issue" (TLP) perhaps uttermost intriguing issues with regards to structure of advancement research It targets to determine the best cutting example of several things of varying lengths from a load of regular size measurable to meet the clients' wants while reducing waste due to trim misfortune. The subsequent numerical pattern is exceptionally nonconvex in nature went with a few imperatives with additional limitations of paired factors. This evades the utilization of straight advancement techniques. In this review work we utilize synergetic differential advancement (SDE) for the arrangement of this sort of issues. Four theoretical yet significant instances of trim misfortune issue emergingare used in the paper industry the test. The exploratory outcomes contrasted and those of different procedures show the capability of the SDE to take care of the issue. This paper includes introduction which explains a complete terminologies related to cutting stock problem, in
... For solving one-dimensional cutting problem, Gilmore and Gomory [1] presented a delayed pattern generation technique by means of linear programing. Other approaches can be found in the literature [2][3][4][5][6][7][8][9][10][11][12][13]. Different objectives such as trim loss and cutting time were considered by Morabito and Arenales [14] in cutting plan preparation. ...
... Gilmore and Gomory [1] presented a delayed pattern generation technique for solving a one-dimensional cutting problem using linear programing. Other methods can also be found in the literature2345678910111213. Morabito and Arenales [14] considered different objectives (e.g., cutting time and trim loss) in the preparation of the cutting plan. ...
In the paper industry, numerous studies have explored means of optimizing order allocation and cutting trim loss. However, enterprises may not adopt the resulting solutions because some widths of the inventory exceed or are less than those required for acceptable scheduling. To ensure that the results better suit the actual requirements, we present a new decision model based on the adjustment of scheduling and limitation of inventory quantity to differentiate trim loss and inventory distribution data. Differential analysis is used to reduce data filtering and the information is valuable for decision making. A numerical example is presented to illustrate the applicability of the proposed method. The results show that our proposed method outperforms the manual method regarding scheduling quantity and trim loss.
The paper industry supply chain is highly complex and consists of many processes and planning tasks. It starts with the collection of raw wood and ends up in different paper products from paperboard to high class art papers. Managing the supply chain is difficult and the permanently changing business environment constantly brings new challenges. This paper starts with outlining the challenges in the supply chain management of today’s paper industry. After that we evaluate the state of research to determine planning problems covered by the literature. The purpose is to catch up time-wise on the previous overview from 2009 and also to update the planning problems. This overview paper summarizes comprehensively the paper industry as of today. It provides a reference work for researchers and other interested parties, and uncovers areas for future research.