Cutting stock waste reduction using genetic algorithms

Abstract

A new model for the One-dimensional Cutting Stock problem using Genetic Algorithms (GA) is developed to optimize construction steel bars waste. One-dimensional construction stocks (i.e., steel rebars, steel sections, dimensional lumber, etc.) are one of the major contributors to the construction waste stream. Construction wastes account for a significant portion of municipal waste stream. Cutting one-dimensional stocks to suit needed project lengths results in trim losses, which are the main causes of one-dimensional stock wastes. The model developed and the results obtained were compared with real life case studies from local steel workshops. Cutting schedules produced by our new GA model were tested in the shop against the current cutting schedules. The comparisons show the superiority of this new GA model in terms of waste minimization.

An Application of Optimization With Multiple Rebar Length For Reducing Construction Rebar Waste Rate

“…Bu kesme problemine bir boyutlu malzeme kesme optimizasyon problemi (one-dimensional material cutting optimization problem) denmektedir [7,8]. İnşaat demirinde kesme optimizasyonunun amacı statik hesap ile projede belirlenen inşaat demirinin boyutu ve uzunluğuna göre kesim boylarını optimize etmek [9][10][11] ve sipariş verilen malzemenin israfını minimize etmektir [7,12]. Malzeme kesme problemine çözüm üretmek için birçok çalışma yapılmıştır.…”

Cutting stock problem: A solution based on novel pattern based chromosome representation using modified GA

“…Weakness of described chromosome representation are that It can solve only single chromosome representation or it can be applied for only limited stocksize.Binitha et al [3] applied GA that uses variable length chromosome representation. First chromosome defines stock size (jumbo) and second chromosome defines items (reels) cut from that stock.Khalifa et al [4] provided chromosome representation in which each chromosome was processed in pairs of gene; the first gene in each pair would give the pattern number to be used and the second gene would give the no. of times this pattern would be used in the solution.…”

3D overlapped grouping Ga for optimum 2D guillotine cutting stock problem

“…They are namely, sequential heuristic procedures [14,16], linear programming based methods [8,9,15] and meta-heuristics techniques [18,19], especially for the two-dimensional case. Among these different heuristics, it is worth mentioning that application of (GA), [17,21,33] is now ubiquitous in the studies related to materials and manufacturing [3,4,31], (GA) is applied to determine the optimized layout of rectangular parts that are related to metal cutting problems. This paper aims to solve those problems by determining a set of cutting patterns (to obtain the blanks).…”