A NOVEL NUMERICAL PYTHON-BASED OPTIMIZED ALGORITHM FOR ADVANCING IN AGGREGATE PRODUCTION PLANNING

Abstract

Aggregate Production Planning (APP) is the procedure that seeks to establish optimal levels of production, inventory and labor over an intermediate period in order to satisfy demand at the lowest possible cost. This study presents an optimization of aggregate production scheduling using a variable labor model. The model incorporates crucial production scheduling decisions, including work allocation, inventory management, backorders, and personnel adjustments such as hiring and firing. To solve the model, two Python-based solvers are used: SciPy and Pyomo, which apply linear programming relaxation and mixed integer programming, respectively. The study evaluates both solvers in terms of computational efficiency and solution accuracy. The results show that SciPy offers a fast approximation, while Pyomo provides accurate solutions at a higher computational cost. This comparison highlights the swap between accuracy and speed and supports the effective use of open-source solvers in real-world production scheduling framework.