Tailoring Instances of the 1D Bin Packing Problem for Assessing Strengths and Weaknesses of Its Solvers

摘要: Solvers for different combinatorial optimization problems have evolved throughout the years. These can range from simple strategies such as basic heuristics, to advanced models metaheuristics and hyper-heuristics. Even so, set of benchmark instances has remained almost unaltered. Thus, any analysis solvers been limited assessing their performance under those scenarios. if this fruitful, we deem necessary provide a tool that allows better study each available solver. Because that, in paper present strengths weaknesses solvers, by tailoring them. We propose an evolutionary-based model test our idea on four heuristics 1D bin packing problem. This, however, does not limit scope proposal, since it be used other domains with few changes. By pursuing in-depth tailored instances, more relevant knowledge about solver derived.