Inventory Optimisation in A Retail Store: Particle Swarm Optimisation Approach

Abstract

Inventory management is essential to a company's success in the highly competitive retail industry. Particle Swarm Optimisation (PSO) is one of the most successful approaches for improving inventories by determining the ideal values for critical parameters. This research applies PSO to a real-world retail business, and the effects of varying parameters are analysed. We obtained considerable inventory optimisation gains by adjusting the swarm size, individual and global learning coefficients, and several iterations. Our findings indicate that increasing the size of the swarm and the global learning coefficient may reduce inventory holding costs significantly while maintaining a high level of customer satisfaction. In addition, we discovered that adjusting the number of iterations may significantly enhance the algorithm's performance. This research illustrates the potential of PSO for inventory optimisation and emphasises the significance of parameter tweaking for best outcomes.

Keywords: Inventory Optimisation, Retail Store, Particle Swarm Optimisation

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