Optimization of markers in clothing industry

Authors:
Iztok Fister;Marjan Mernik;Bogdan Filipič
Affiliations:
Mura, European Fashion Design, Plese 2, SI-9000 Murska Sobota, Slovenia;Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, SI-2000 Maribor, Slovenia;Department of Intelligent Systems, Joef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia
Venue:
Engineering Applications of Artificial Intelligence
Year:
2008

Citing 5
Cited 1

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability; A Guide to the Theory of NP-Completeness

Computers and Intractability; A Guide to the Theory of NP-Completeness
The Design and Analysis of Computer Algorithms

The Design and Analysis of Computer Algorithms
Evolutionary search for optimal combinations of markers in clothing manufacturing

Proceedings of the 8th annual conference on Genetic and evolutionary computation

A hybrid self-adaptive evolutionary algorithm for marker optimization in the clothing industry

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The optimization of markers is one of the most important preparatory steps for production in the clothing industry. It determines the sizes of clothes to be laid and cut together. Markers are built on a work order basis. The work order is a matrix of sizes by colors that have to be covered by markers in an optimal way. The optimal way can be defined in a number of ways: it can be the minimum number of markers to accomplish the work order, or the least time for fulfilling the work order, or the minimal amount of cloth used. In this article, the problem of marker optimization is treated formally. The problem is transformed into the 0/1 knapsack problem and solved using evolutionary algorithms. The results obtained on ten real-world work orders show an important improvement over the previously best known solutions.