Introduction

Shelf space is one of the most valuable resources of a retail company, giving utmost importance to shelf space management. The underlying challenge consists in distributing the scarce shelf space of a retail store among the different products to be displayed. As customer choices can be influenced by in-store factors such as the location and space allocated to the products, enhanced shelf space allocation decisions can improve sales and ultimately boost the stores financial performance. Therefore, retailers strive to assign the right space to the right products at the right place.

Goals

Inspired by the case of a Portuguese supermarket chain, this proposal aims to tackle the Shelf Space Allocation problem (SSAP) through the development of optimization algorithms for the problem and its comparison to state-of-the-art methods. In particular, the objective is to study and apply the Biased Random Key Genetic Algorithm (BRKGA). The BRKGA is a specific class of Genetic Algorithms (GA) that has proven to successfully solve many complex optimization problems. It differs from common GA by its solution representation and the way it combines the individuals to create new generations.

Strategies

1. Literature Review on Optimization methods with an emphasis in Genetic Algorithms (GA) and Biased Random Key Genetic Algorithms (BRKGA);
2. Literature Review on the Shelf Space Allocation and related Problems (SSAP);
3. Full understanding of the case study - a Portuguese supermarket chain;
4. Study of a SSAP model and its implementation using OPL;
5. Familiarization with the BRKGA API;
6. Design and implementation of a BRKGA algorithm to the SSAP;
7. Test and validation of the algorithm using case study instances;
8. Comparison of the algorithm with the SSAP model and other optimization methods;
9. Writing of the dissertation;