Material cutting plan generation using a genetic algorithm in the steel construction industry

Construction firms specializing in large commercial buildings must often design and build steel structural elements as a part of each project. Such firms must purchase large steel plates, cut them into pieces and then weld the pieces into H-beams and other construction components. We formalize the material ordering and cutting problem faced by this industry, and propose the genetic algorithm (GA) as a solution methodology. A heuristic for combining steel elements into plates to control relevant costs is used to generate an initial feasible population of solutions. Possible genetic representations of the feasible solution are formulated and compared. It is shown that the conventional one-point crossover can be utilized after reformulating the representation chromosomes. Possible outcomes after the crossover are discussed. One company, Lien-Kang Heavy Industrial Company, Ltd. (LK), has supplied historical data for testing the result. The comparison to LK's solutions indicate that the solution from the heuristic is less costly while the genetic algorithm may provide better result and computational efficiency.