| dc.description.abstract | 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. | en_US |
