An Approach to Aid Decision-Making by Solving Complex Optimization Problems Using SQL Queries

Abstract

In combinatorial optimization, the more complex a problem is, the more challenging it becomes, usually causing most research to focus on creating solvers for larger cases. However, real-life situations also contain small-sized instances that deserve a researcher’s attention. For example, within a web development context, a developer might face small combinatorial optimization cases that fall in the following situations to solve them: (1) the development of an ad hoc specialized strategy is not justified; (2) the developer could lack the time, or skills, to create the solution; (3) the efficiency of naive brute force strategies might be compromised due to the programming paradigm use. Similar situations in this context, combined with a recent increasing interest in optimization information from databases, open a research area to develop easy-to-implement strategies that compete with those naive approaches and do not require specialized knowledge. Therefore, this work revises Structured Query Language (SQL) approaches and proposes new methods to tackle combinatorial optimization problems such as the Portfolio Selection Problem, Maximum Clique Problem, and Graph Coloring Problem. The performance of the resulting queries is compared against naive approaches; its potential to extend to other optimization problems is studied. The presented examples demonstrate the simplicity and versatility of using a SQL approach to solve small optimization problem instances.