Using genetic algorithms in solving the one-dimensional cutting stock problem in the construction industry

In the United States, vast amounts of construction waste are produced every year. Construction waste accounts for a significant portion of the municipal waste stream of the United States. One-dimensional stocks are one of the major contributors to construction waste. Cutting one-dimensional stocks to suit needed project lengths results in trim losses, which are the main causes of one-dimensional stock waste. Although part of such waste is recyclable such as steel waste, reduction in the generation of waste can enhance the stock material usage and thereby increase the profit potential of the company. The traditional optimization techniques (i.e., linear programming and integer programming) suffer some drawbacks when they are used to solve the one-dimensional cutting stock problem (CSP). In this paper, a genetic algorithm (GA) model for solving the one-dimensional CSP (GAID) is presented. Three real life case studies from a local steel workshop in Fargo, North Dakota have been studied, and their solutions (cutting schedules) using the GA approach are presented and compared with the actual workshop cutting schedules. The comparison shows a high potential of savings that could be achieved.