Genetic Algorithm Based Floor Planning System
This work addresses layout optimization for superstores to boost sales, but it appears incremental as it builds on existing genetic algorithm methods.
The paper tackled the problem of designing shelf layouts for superstores to improve sales by using a genetic algorithm with a novel chromosome representation that considers parameters to prevent dead-ends and enhance visibility, resulting in reasonably good layouts generated quickly.
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.