The cutting-stock approach to bin packing: Theory and experiments (original) (raw)

We report on results of an experimental study of the Gilmore-Gomory cutting-stock heuristic GG61, GG63] and related LP-based approaches to bin packing, as applied to instances generated according to discrete distributions (previously studied theoretically in such papers as CCG + 91, CCG + 00, CJSW93, CJSW97, KRS98]). We examine the questions of how best to solve the knapsack problems used to generate columns in the Gilmore-Gomory approach, how the various algorithms' running times and solution qualities scale with key instance parameters, and how the algorithms compare to more traditional bin packing heuristics.

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