Aggregate Production Planning Optimization: Comparing the Efficiency of Particle Swarm Optimization and Simulated Annealing Algorithms

Aggregate Production Planning is a critical decision-making process for balancing production capacity and demand over a medium-term horizon. The objective of this paper is to propose an optimization model for aggregate production planning that fits a single-product and single-period system. Aggregate production planning often becomes highly inaccurate due to the involvement of various uncertainties, making it more difficult to address all relevant cost factors accurately. The other methods focus on minimizing just production-related costs; it often ignores other significant cost components, such as inventory costs, man-hired & fired costs, machine maintenance costs etc. These costs are sometimes significantly influenced by aggregate production planning, & their mismanagement can increase overall expenses for the company. The model presented here overcome this problem by integrating all the relevant cost factors, whether directly or indirectly influenced by aggregate production planning, into a more effective way of minimizing the total cost The total costs are divided into two categories: direct costs, which include raw material purchasing cost, ordering & inventory, and indirect costs, machine maintenance costs, man-hired costs etc. The model’s effectiveness is demonstrated using a real-life electronic industry case study, solved with particle swarm optimization & simulated annealing methods, giving a comparison between two methods. The result highlights that the simulated annealing method is more effective than Particle Swarm Optimization method & offers a reliable solution for various industrial applications.