Optimizing Production Costs through Linear Programming and the Hax-Canda Model: A Case Study of the Chlef Cement Factory (2022–2023)

Abstract

This study examines the integration of linear programming and the hierarchical Hax-Canda model to optimize product cost calculation and aggregate production planning within the manufacturing sector. Using a quantitative modeling approach grounded in operations research, the research investigates how mathematical optimization techniques restructure cost allocation mechanisms, specifically contrasting traditional accounting methods with advanced algorithmic planning. The methodology applies a mixed-integer linear programming (MILP) framework to empirical data obtained from the Chlef Cement Factory (ECDE) in Algeria, spanning the 2022–2023 operational period.

 Results indicate that deploying the Hax-Canda hierarchical model significantly streamlines resource allocation, yielding a 49.36% reduction in total production costs compared to the historical baseline. The findings suggest that industrial enterprises can substantially mitigate operational inefficiencies by transitioning from static cost accounting to dynamic, algorithm-driven production planning. The study highlights the critical necessity of integrating hierarchical decision-making frameworks to maintain cost competitiveness in energy-intensive manufacturing environments, with direct implications for industrial policy in developing economies.