Strategies for Resource Allocation of Two Competing Companies using Genetic Algorithm

TL;DR

This paper explores how two competing supermarket chains can optimize shop locations in a city using a genetic algorithm combined with Monte Carlo methods, identifying initial configurations that lead to faster market dominance.

Contribution

It introduces a novel approach applying genetic algorithms and Ising model concepts to strategic location planning in a competitive retail environment.

Findings

Certain initial topological patterns accelerate market dominance

Genetic algorithm effectively evolves initial configurations towards dominance

Topological properties influence the speed of market capture

Abstract

We investigate various strategic locations of shops in shopping malls in a metropolis with the aim of finding the best strategy for final dominance of market share by a company in a competing environment. The problem is posed in the context of two competing supermarket chains in a metropolis, described in the framework of the two-dimensional Ising model. Evolutionary Algorithm is used to encode the ensemble of initial configurations and Monte Carlo method is used to evolve the pattern. Numerical simulation indicates that initial patterns with certain topological properties do evolve faster to market dominance. The description of these topological properties is given and suggestions are made on the initial pattern so as to evolve faster to market dominance.