Research on Cut Order Planning for Apparel Mass Customization

Liu, Yanmei; Shao-cong, Yan; Zhang, Shuting

doi:10.1007/978-3-642-24282-3_36

Cited by 4 publications

(7 citation statements)

References 4 publications

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“…In purpose of solving larger-size problem, Deng et al [18] present the two stages optimization method based on probability search and genetic algorithm to solve cut order planning (COP) problem. The major concept is to decompose the original COP problem into three sub-problems which each sub-problem corresponds with only one decision variable.…”

Section: The Relaxation Methodsmentioning

confidence: 99%

“…This objective is represented in three forms which are all nearly the same in meaning. Firstly, it is directly represented in the form of minimum number of markers or lays used [16][17][18]. When numbers of markers are tried to minimized, number of units in each marker should be maximized.…”

Section: Objective Functionmentioning

confidence: 99%

“…Almost papers [3,9,12,[18][19][20][21][22][23][24]26] used only a set of basic constraints addressed in the basic mark planning model as shown in section 2.2. However, in [16,17], they add a constraint that allows no excess to occur.…”

Section: Constraintmentioning

confidence: 99%

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The Review of Mark Planning Problem

Puasakul

Chaovalitwongse

2016

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Abstract. Mark planning is one of the most important planning processes in garment industry. The major function is to generate a set of markers which is used as cutting guidelines or cutting templates in a cutting process. In other words, this process can be seen as a planning step of a cutting process which determines what sequence and how many parts will be cut. For the academic point of view, mark planning has been of interests for more than 15 years. Many papers are published with various objective functions, problem scenarios, and constraints. Therefore, key content summary of these papers are useful for researchers who are interested in this type of problem. The purpose of this paper is to introduce an exhaustive review of mark planning papers which is composed of three sections: the relevant background, the summary of reviewed papers, and the relationship between objective functions which is cost combinations and major garment characteristics. In conclusion, future researches should go forward the trend of integration between this process and other relevant processes in the production chain, e.g. cut scheduling, assembly planning, marker making.

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Section: The Relaxation Methodsmentioning

confidence: 99%

Section: Objective Functionmentioning

confidence: 99%

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The Review of Mark Planning Problem

Puasakul

Chaovalitwongse

2016

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“…Yan-mei et al focused on the length of the cutter blade, the thickness of the fabric, the size of the clothes, and the cutting table to conduct their research (Yan-mei et al, 2011).…”

Section: Research Parameters Used To Optimize Cut Order Planning (Rq3)mentioning

confidence: 99%

Optimal Cut Order Planning Solutions using Heuristic and Meta-Heuristic Algorithms: A Systematic Literature Review

Ranaweera,

Rathnayaka,

Chathuranga

2023

KDU J. Multidisc. Stud.

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Cut order planning is a significant task in the apparel industry which determines the fabric spreading layout that can affect different aspects of the apparel industry including the cost of garments, the efficiency of the sewing line, etc. Due to the nature of the cut order problem, it is very hard to determine an optimal solution for the cut order plan although there are ready-made software and also industry experts working on this. Hence, various attempts have been made to optimize it by using machine learning algorithms in the modern world. This review aims at identifying how heuristic and meta-heuristic algorithms are used to optimize the cut-order planning solutions to obtain a near-optimal solution. Furthermore, the lack of research limits it down to 13 papers to be reviewed, and this paper discusses the methodologies and algorithms used, research parameters, issues, and future areas that need to be investigated for the cut order problem. The review shows that the genetic algorithm is widely used to optimize the cut-order plans by adopting the hybridization approaches along with some other meta-heuristic algorithms such as simulated annealing, tabu search, etc. Experimental results indicate that researchers were able to minimize fabric waste by optimizing the cut order plans.

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“…Yan‐Mei et al. () consider a further simpler version of the problem of Rose and Shier () where the objective is to minimize the number of sections while satisfying the demand for each size. They enforce the constraint regarding the constant number of patterns per section and the maximum number of layers per section.…”

Section: Literature Reviewmentioning

confidence: 99%

Heuristics for the combined cut order planning two‐dimensional layout problem in the apparel industry

M’Hallah

Bouziri

2014

Int Trans Operational Res

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Cut order planning (COP) is an NP-hard nonlinear optimization problem. Managers of apparel manufacturing units face this problem during the planning of the first stage of the manufacturing process. It affects the fluidity of the work flow and use of fabric. It consists in dividing every garment's order into sections, assigning the sizes to them, and determining their lengths and numbers of layers such that the total fabric length is minimized. Current industrial practice assumes that the length of the layout of a section is known a priori, and it does not depend on its combination of sizes. That is, the industry solves COP independently of the two-dimensional layout (TDL) that is the second stage of the manufacturing process. By relying on the length estimates in lieu of determining the actual length of a section, the industry is obtaining erroneous estimates of the true length used. Herein, COP and TDL are combined into a single problem CT (CT = COP + TDL) whose objective is to minimize fabric length. CT is solved using constructive heuristics, and three metaheuristics: a stochastic local improvement method, global improvement method, and hybrid approach. The approaches are tested on existing benchmark instances and new industrial cases. Their results provide computational proof of the benefits that industry can ripe by combining COP and TDL. The comparison of the performance of the approaches highlights their respective academic and practical utilities.

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Research on Cut Order Planning for Apparel Mass Customization

Cited by 4 publications

References 4 publications

The Review of Mark Planning Problem

The Review of Mark Planning Problem

Optimal Cut Order Planning Solutions using Heuristic and Meta-Heuristic Algorithms: A Systematic Literature Review

Heuristics for the combined cut order planning two‐dimensional layout problem in the apparel industry

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