Planejamento hierárquico da produção em usinas de açúcar e álcool: modelagem de otimização robusta

Paiva, Rafael Piatti Oiticica de; Morábito, Reinaldo

doi:10.1590/s0103-65132013005000032

Cited by 2 publications

(1 citation statement)

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“…This study revisits the two‐level hierarchical supply chain model called PASUC (Paiva and Morabito, 2013, 2014) and discusses the results of applying a robust optimization approach based on Bertsimas and Sim (2004) to two uncertainty model parameters that managers in the sugarcane sector state find relevant for the whole business, and which could not be analyzed through deterministic models as they were (namely, price of selling products, VP pt , and yields from industrial processes, A pkw ). A set of real data adjusted to have some pattern to help with analysis was used in the present study, different from the case study conducted in Paiva and Morabito (2014), in which the objective was to validate the conception of the models, revised here with a few improvements to the original formulation.…”

Section: Introductionmentioning

confidence: 99%

A hierarchical supply chain model for the sugar–alcohol energy sector with robust optimization analysis

Paiva

Rocco

Morábito

2020

Int Trans Operational Res

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This paper addresses a hierarchical optimization model for the sugar-alcohol energy sector with robust optimization analysis to provide managerial insights. It has two levels: the upper level is a linear model in which more aggregated decisions are related to allocating production targets to mills belonging to a cooperative society aiming to meet the potential market demand, as well as managing the inventories over months in annual planning; the lower level is a mixed-integer model that represents the less aggregated decisions of each mill, such as the milling rhythm, the industrial process selection to produce sugar-alcohol-molasses and energy, purchasing sugarcane from the suppliers on a weekly basis. Robust optimization analysis is performed for product prices and industrial yields to investigate their impacts on the supply chain that could not be inferred through deterministic approaches. One important result was related to the uncertainty in product prices that had a greater impact on the whole supply chain compared to the industrial yield, which affects only the mill itself, particularly increasing the inventory backlog of the mill for not meeting the demand assigned by the cooperative. Cogeneration of energy, and the sale of surplus energy was an important strategy to mitigate the decrease in industrial yield performed in the robust analysis while the change in the product production pattern was a consequence of product price uncertainty. The models can be applied in practice to provide better strategic and tactical planning of mills in cooperatives.

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