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

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