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Statistical experimental design and analysis is a cornerstone for scientific inquiry that is rarely applied in reporting computational testing. This approach is employed to study the relative performance characteristics of the four leading algorithmic and heuristic alternatives to solve the Linear Cost Generalized Assignment Problem (LCGAP) against a newly developed heuristic, Variable-Depth Search Heuristic (VDSH). In assessing the relative effectiveness of the prominent solution methodologies and VDSH under the effects of various problem characteristics, we devise a carefully designed experimentation of state-of-the-art implementations; through a rigorous statistical analysis we identify the most efficient method(s) for commonly studied LCGAPs, and determine the effect on solution time and quality of problem class and size.combinatorial optimization, generalized assignment problem, variable-depth search, experimental design and analysis

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Success in the express-mail market hinges on paying attention to every parcel in the system. A key component is safe, efficient loading of aircraft. Federal Express, a perennial leader in the express business, has recently added the Airbus A300, a lighter aircraft, to its fleet. Because of the Airbus design, shear limits—shear being a measure of the downward forces exerted on the plane—are extremely sensitive to the loading, a problem not experienced in the heavier craft. We developed a heuristic for loading containers into positions on the aircraft to address loading preferences and maintain feasibility constraints.