A Rigorous Computational Comparison of Alternative Solution Methods for the Generalized Assignment Problem
用统计实验设计比较四种主流算法和新启发式VDSH求解线性成本广义分配问题的性能，通过严格分析找出最有效方法并评估问题特征对求解时间和质量的影响。
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.