Purpose: Use mathematical models of Mixed Integer Linear Programming oriented to cellular distribution and aggregate production planning in order to obtain the appropriate product family for each manufacturing cell and from this, minimize production and material handling costs through the allocation of production resources.Design/methodology/approach: This article develops two mathematical models in LINGO 18.0 software, performing the computational calculation to obtain the best efficiency in cell formation at minimum production cost.Findings: The mathematical model oriented to the formation of manufacturing cells allows a grouping of products and machines with 82.5% group efficiency. By reallocating machines to each cell and redistributing facilities, the cost of material handling is reduced by 35.1%, and the distance traveled in product manufacturing is reduced by 26.6%. The mathematical model of aggregated planning provides information on production resource requirements such as personnel, machinery, distances traveled, as well as the cost generated by the need to outsource part of the production, inventory maintenance and overtime work.Research limitations/implications: It is necessary to clearly define the capacity variables. The model does not take into account the cost of mobilizing machines and readjusting facilities.Practical implications: The case study company can adequately plan production and efficiently manage its resources.Social implications: The study can be applied to other textile SMEs.Originality/value: The aggregate production planning model requires the assignment of the mathematical model of manufacturing cell formation in order to calculate the resource requirements needed to meet a demand.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.