A hybrid self-adaptive evolutionary algorithm for marker optimization in the 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 ulica 17, SI-2000 Maribor, Slovenia;Department of Intelligent Systems, Joef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia
Venue:
Applied Soft Computing
Year:
2010

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Abstract

The task of marker optimization in clothing production is to eliminate pieces from a work order using an optimal sequence of markers and plies, where the work order is given as a matrix of colors by sizes, markers are vectors of sizes to be laid-out and cut together, and the number of plies determines how many pieces are eliminated from the work order each time. Although the optimality of a marker sequence can be determined in several ways, we consider minimum preparation cost as a key objective in clothing production. The traditional algorithms and the simple evolutionary algorithms used in marker optimization today have relied on minimizing the number of markers, which only indirectly reduces production costs. In this paper we propose a hybrid self-adaptive evolutionary algorithm (HSA-EA) for marker optimization that improves the results of the previous algorithms and successfully deals with the objective of minimum preparation cost.