Genetic algorithm implementation for solving nesting problem in garment industry

Abstract

In order to quickly respond the fluctuating market demand, a company has to provide itself with an appropriate tool that leads to simplification of raw material supply, route of manufacturing process, high utilization of raw material, simple tooling system and so on. Garment industry includes manufacturing processes dealing with cutting a ply of long raw cloth according to the pattern defined in advanced. This kind of industry faces a lot of material preparation operations. The quantity of raw material to be prepared should be based on the variation of demand and combination of sizes. Order quantity for every size (i.e.: S, M, L or XL) in fact might never be the same and it is relatively unpredictable. Knowing the quantity of every size, placement of patterns on one ply of clothes is not a simple job. The objective is to minimize the waste (unused material) after nesting or placement of pattern is performed. This research focuses on developing an algorithm or technique for solving the nesting problem. As there are a number of pattern combinations to be placed on cloth’s ply, then Genetic Algorithm is formulated to find the best nesting and minimum waste of material. Solution offered by this technique is that roughly between 7 to 20% of waste is still produced. On the other hand, exact and appropriate pattern position on a ply of clothes is automatically determined. This feature leads to a simplified process in the next cutting operation. A case study has been performed on a local T-shirt factory in Bandung. The type of T-shirt is limited only for a plain type clothes and for T-shirt type without pocket and collar. Examples of pattern placement show the effectiveness of this algorithm.