A hybrid firefly algorithm for the sales representative planning problem

Abstract

In the rapidly increasing pharmaceutical sector, sales representatives are employed by pharmaceutical manufacturers and distributors to inform and educate physicians. To convince providers to prescribe the medications to their patients, these representatives rely on their product expertise and people’s abilities to close deals. Instead of making direct sales, pharmaceutical sales representatives help medical professionals get the medications, treatments, and information they need to give their patients the best care possible. Furthermore, they inform the public about novel and occasionally life-saving treatments and share interesting medical developments. This study presents a hybrid methodology that integrates the benefits of local search and the firefly algorithm (FA) to determine the optimal planning for a sales representative. The objective is to maximize the rewards while adhering to certain constraints. The objective is to maximize the rewards while adhering to certain limits. Utilizing local search, the hybrid algorithm enhances firefly’s global search behaviour and produces the best possible sales presentation planning. The experimental findings demonstrate the superior performance of the suggested algorithm compared to the FA and other literature methods in the sense of enhancing the convergence rate and preventing local minima. Furthermore, it can enhance the best-known solution for most benchmark instances.