Ant Colony Optimization-Based Inventory Model for Deteriorating Items with Polynomial Demand and Time-Dependent Costs

Abstract

This paper develops and analyses an advanced inventory model for deteriorating items with polynomial demand, quadratic deterioration, and time-dependent holding costs under the condition of complete backlogging. The primary objective is to minimize the average total cost by optimizing decision variables such as cycle length and order quantity. Due to the nonlinear and complex nature of the model, traditional analytical methods may be insufficient or computationally intensive. To address this challenge, the study integrates Ant Colony Optimization (ACO), a powerful metaheuristic inspired by the foraging behaviour of ants, to efficiently search for optimal inventory policies. Numerical examples, graphical illustrations, and sensitivity analyses are provided to demonstrate the effectiveness of the proposed approach. The results show that the ACO-based method achieves a significant reduction in total cost compared to conventional optimization techniques. This research not only enhances the practical applicability of inventory models for deteriorating items but also demonstrates the potential of ACO for solving complex, real-world supply chain and inventory management problems.