A profit-cost ratio maximization model for dynamic pricing and inventory management incorporating infection awareness, solved using Ant Colony and Cuckoo search algorithms

Purpose The COVID-19 pandemic has revealed critical vulnerabilities in pharmaceutical inventory management, including challenges in optimizing investment strategies for medications, addressing product degradation, and managing fluctuating demand and supply. Compounded by the need for sustainability in inventory practices, such as reducing carbon emissions, these challenges underscore the necessity for a dynamic and comprehensive inventory management model tailored to pandemic scenarios. Design/methodology/approach To address these challenges, the primary objectives of this research are to develop a model that optimizes investment strategies for pharmaceutical products across diverse price ranges while incorporating factors like item degradation, preservation methods, service investments, and carbon emissions. The study aims to dynamically adjust investment rates to maximize the profit-cost ratio and provide decision-makers with insights to enhance resource allocation efficiency during pandemics. Findings The proposed model is underpinned by optimal control theory, which allows for dynamic decision-making by integrating factors such as infection rates, deterioration influenced by preservation technology investments, and replenishment schedules. Advanced optimization algorithms, including Ant Colony and Cuckoo Search, are employed to fine-tune pricing strategies, preservation investments, replenishment schedules, and carbon emissions management. These algorithms are particularly effective in identifying optimal solutions for highly complex, nonlinear inventory systems. Research limitations/implications Based on the theory-practice gap analysis, several implications emerge. Strategic investments in service facilities and marketing can enhance profitability while improving operational efficiency. Policymakers and inventory managers can leverage the model to develop operational plans that balance profitability, product availability, and carbon footprint reduction. The model’s adaptability makes it particularly valuable for managing pharmaceutical inventory during rapidly changing pandemic scenarios. Originality/value The implementation of the model involves analyzing real and synthetic datasets, reflecting various pandemic scenarios and dynamically evolving conditions. Numerical simulations are conducted to evaluate the model’s effectiveness, focusing on key metrics such as the profit-cost ratio and sensitivity to pricing, preservation investments, and carbon emissions. The results indicate a significant improvement in the profit-cost ratio as selling prices increase, demonstrating the model’s ability to mitigate pricing’s adverse effects on demand. Sensitivity analyses reveal critical parameters that decision-makers must consider, underscoring the importance of dynamic inventory management during crises.