Genetic Algorithm Based Floor Planning System

TL;DR

This paper presents a genetic algorithm approach for optimizing superstore shelf layouts, focusing on improving visibility and sales metrics efficiently.

Contribution

It introduces a novel chromosome representation for shelf layout optimization using genetic algorithms, enhancing design quality and computational speed.

Findings

The approach produces good layouts quickly.

The chromosome design improves search effectiveness.

Results indicate practical applicability in retail planning.

Abstract

Genetic Algorithms are widely used in many different optimization problems including layout design. The layout of the shelves play an important role in the total sales metrics for superstores since this affects the customers' shopping behaviour. This paper employed a genetic algorithm based approach to design shelf layout of superstores. The layout design problem was tackled by using a novel chromosome representation which takes many different parameters to prevent dead-ends and improve shelf visibility into consideration. Results show that the approach can produce reasonably good layout designs in very short amounts of time.