Marker planning problem in the apparel industry: Hybrid PSO-based heuristics

Abstract

In the apparel industry, fabric contributes to the high cost of raw materials, and thus an improvement in terms of a shorter used marker layout would improve the cost efficiency of this industry. The marker planning problem, also known as a 2D irregular cutting and packing problem in the apparel industry, focuses on optimizing the fabric resource by arranging a set of irregularly shaped clothing patterns on a sheet of fabric while preventing any overlap between the patterns, with the aim of finding the shortest length arrangement. Due to the irregular shapes of clothes, the solution time increases exponentially when more pieces are involved, making this problem become NP-hard or NP-complete. In this study, particle swarm optimization (PSO)-based heuristics were evaluated to address the above problem. The moving heuristic proposed by Tsao et al. (2020) acts as a placement strategy considering the order of the pattern and the degree of rotation. A pixel-based representation was used to handle the geometry of the pattern. PSO-based heuristics were developed by enhancing PSO performance with a local search, a genetic algorithm, and simulated annealing. Mixed-size order and a special case of the separated-size arrangement were also considered. The proposed algorithms were tested in an apparel company and compared with the well-known bottom-left fill heuristic approach to obtain competitive results with shorter fabric length and CPU time.