Comparative Study of Heuristics for the One-Dimensional Bin Packing Problem

The Bin Packing problem (BPP) is a classic optimization problem that is known for its applicability and complexity, which belongs to a special class of problems called NP-hard, in which, given a set of items of variable size, we search to accommodate them inside fixed size containers, seeking to optimize the number of containers to be used, that is, using the least number of containers to place the largest number of items possible. The BPP has been preserved as a current study problem due to the various applications that it offers mainly in the industry; therefore, in the recent state-of-the-art, there are different algorithms, mainly heuristics and metaheuristics for solving the problem. In this paper, we present an empirical comparison of the algorithms that have been used to solve one of the variants of BPP; the One-dimensional Bin Packing Problem (1D-BPP), and the works that have reported the best results in the state-of-the-art, as well as those found in literature from today to the last two decades. This survey aims to identify which components and techniques were used for each of the algorithms and which of these contribute more to their performance. Twenty-one algorithms were selected from the specialized literature that have the aforementioned characteristics, their results were analyzed with different instances and the methods they use were added, for example, neighborhood and local searches, evolutionary and genetic algorithms, among others. The main objective is that this study can help both researchers and professionals interested in using specific components or techniques that help improve the behavior of an algorithm to solve a problem, in this case, 1D-BPP, since we hope that our conclusions can provide some ideas about the advantages or limitations of each of the methods studied here.