One-dimensional cutting stock problems consist of cutting standard objects available in stock into smaller pieces called items in order to meet a known demand. Only one dimension of the stock objects is considered in the cutting process (rolls, bars, etc.) This problem might be formulated as a large scale integer linear programming problem, which an optimal solution cannot be obtained in practice, especially when dozens of items have to be produced. This paper addresses the problem of finding integer solutions to the one-dimensional cutting stock problem, with special attention to problems with low demand. We study some heuristic approaches proposed in literature and some straightforward variants. These methods are compared with respect to solution quality, number of cutting patterns and computational time. Our evaluation is based on solving randomly generated instances.
cutting stock problem; integer programming; column generation
