Heuristic based model for groceries shopping navigator

Abstract

This paper presents a heuristic based model for groceries shopping navigator that attempts to improve the navigation problem that usually face by customers while doing their shopping. A system known as Shopping Navigator or shortly SHoNa was developed to give the optimal sequence of shelves to be visited by the customer and the total estimated shopping time so that the user can plan their shopping task earlier. Genetic algorithm was employed and implemented in a web-based platform that is compatible with other devices such as smartphones and tablets. SHoNA can minimize the shopping time by identifying the most optimal order of shelves inside the supermarket that needs to be visited by the customer. A series of experimental was performed in producing the optimum model. Our findings showed that the combination of order one crossover and inverse mutation produced a better optimal performance, which is the minimum total amount of groceries shopping time.  SHoNA can be further enhanced with visualization features for a better shopping experience.