FRSF - INVESTIGACIÓN Y PRODUCCIÓN CIENTÍFICA

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    Efficient mixed-integer linear programming model for integrated management of ready-mixed concrete production and distribution
    (Automation in Construction, 2025-02-14) Tibaldo, Aldana; Montagna, Jorge Marcelo; Fumero, Yanina
    The ready-mixed concrete industry plays a key role in the construction sector. Tight integration between different actors is required for developing a successful project. In this context, this paper presents a contribution to optimal daily planning of production and distribution operations in the concrete industry to meet the requirements of construction sites. Customized products must be produced and delivered satisfying the specific time windows proposed by customers. To solve this problem, traditional models often resort to approaches based on decomposition techniques or approximate methods, which may provide poor quality solutions that deviate significantly from optimal planning. In contrast, this article presents an integrated and exact approach to solve the problems of ready-mixed concrete batching, production and distribution, reaching the optimal global solution in good computational times. Several examples are solved to assess the capability and performance of the proposed formulation and its applicability to a real case in this industry.
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    Detailed planning and inventory optimization model in the mattress industry
    (2015) Montagna, Jorge Marcelo; Cecchietti, Aldo; Rodriguez, María Analía; Corsano, Gabriela
    In this work, a mixed integer linear programming (MILP) model for the simultaneous production and inventory planning problems is proposed, which arise in the industrial context of polyurethane foam manufactory. The production process considers the foam blooming stage as well as the curing step where the blocks are dried. Several tradeoffs are involved in the overall model given that detailed inventory management decisions are also taken into account in the formulation. Several study cases provided by a local company are tested to show the model performance.