We review the most important mathematical models and algorithms developed for the exact
solution of the one-dimensional bin packing and cutting stock problems, and experimentally …

Grouping problems are combinatorial optimization problems, most of them NP-hard, related
to the partition of a set of items into different groups or clusters. Given their numerous real …

This paper presents a Grouping Genetic Algorithm (GGA) to solve a maximally diverse
grouping problem. It has been applied for the classification of an unbalanced database of …

Grouping problems are a special type of combinatorial optimization problems that have
gained great relevance because of their numerous real-world applications. The solution …

The bin-packing problem (BPP) is an age-old NP-hard combinatorial optimization problem,
which is defined as the placement of a set of different-sized items into identical bins such …

This paper describes a novel approach making use of genetic algorithms to find optimal
solutions for multi-dimensional vector bin packing problems with the goal to improve cloud …

Grouping problems aim to partition a set of items into multiple mutually disjoint subsets
according to some specific criterion and constraints. Grouping problems cover a large class …

Evolutionary algorithms have been reported to be efficient metaheuristics for the
optimization of several NP-Hard combinatorial optimization problems. In addition to their …

In this paper, we address the Bin Packing Problem with Compatible Categories (BPCC), a
challenging optimization problem that arises in the context of last mile distribution to …

In this paper, we develop and apply a genetic algorithm to solve surgery scheduling cases in
a Mexican Public Hospital. Here, one of the most challenging issues is to process containers …