An Optimization Model for Aggregate Production Planning and Control: A Genetic Algorithm Approach

Document Type: Research Paper


Industrial and Production Engineering, Jashore University of Science and Technology



In this paper, an optimization model for aggregate planning of multi-product and multi-period production system has been formulated. Due to the involvement of too many stakeholders as well as uncertainties, aggregate production planning sometime becomes extremely complex in dealing with all relevant cost criteria. Most of the existing approaches have focused on minimizing only production related costs, consequently ignored other cost factors, for instance supply chain related costs. However, these types of other cost factors are greatly affected by aggregate production planning and its mismanagement often results in increased overall costs of the business enterprises. Therefore, the proposed model has attempted to incorporate all the relevant cost factors into the optimization model which are directly or indirectly affected by the aggregate production planning. In addition, the considered supply chain related costs have been segregated into two major categories. While raw material purchasing, ordering and inventory costs have been grouped into upstream category, finished good inventory and delivery costs in the downstream category. The most notable differences with the other existing models of aggregate production planning are in the consideration of the cost factors and formulation process in the mathematical model. A real-life industrial case problem is formulated and solved by using genetic algorithm to demonstrate applicability and feasibility of the proposed model. The findings show that the suggested model is capable of efficiently and effectively solving any kind of aggregate production planning.