Model-Based Decision Support Tools at Jugos S.A. Concentrated Fruit Juice Plant

Aníbal M. Blanco (), M. Susana Moreno (), Carolina Taraborelli (), Flavio D’Angelo (), Facundo Iturmendi () and J. Alberto Bandoni ()
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Aníbal M. Blanco: Planta Piloto de Ingeniería Química - PLAPIQUI (Universidad Nacional del Sur - CONICET), Bahía Blanca 8000, Buenos Aires, Argentina;
M. Susana Moreno: Planta Piloto de Ingeniería Química - PLAPIQUI (Universidad Nacional del Sur - CONICET), Bahía Blanca 8000, Buenos Aires, Argentina;
Carolina Taraborelli: Planta Piloto de Ingeniería Química - PLAPIQUI (Universidad Nacional del Sur - CONICET), Bahía Blanca 8000, Buenos Aires, Argentina;
Flavio D’Angelo: Jugos S.A., Parque Industrial, Villa Regina 8336, Río Negro, Argentina; Universidad Nacional de Río Negro, CIT Río Negro 8336, Río Negro, Argentina
Facundo Iturmendi: Jugos S.A., Parque Industrial, Villa Regina 8336, Río Negro, Argentina; Universidad Nacional de Río Negro, CIT Río Negro 8336, Río Negro, Argentina
J. Alberto Bandoni: Planta Piloto de Ingeniería Química - PLAPIQUI (Universidad Nacional del Sur - CONICET), Bahía Blanca 8000, Buenos Aires, Argentina

Interfaces, 2020, vol. 50, issue 4, 255-268

Abstract: We describe the development of a decision-support tool to assist in the operations of a large concentrated apple and pear juice plant. The tool’s objective is to generate detailed schedules of clarified juice batches to be produced in the following weeks considering incoming fruit forecasts, commercial commitments, and infrastructural constraints. The tool is based on two interactive modules, PLANNER and SIMOPT, with different and complementary purposes. Each module is based on mixed-integer models with specific inputs, outputs, and user interfaces. PLANNER consists of three submodules: (i) planning assigns a batch of concentrated juice to be produced on a specific day, taking into account cleaning activities, rest days, raw material availability, and production and storage constraints; (ii) preprocessing organizes juice orders in batches; and (iii) pooling provides a detailed monitoring of semielaborated juice in storage pools in terms of inventories and sugar and acid content. Finally, SIMOPT provides a detailed optimal operative condition of the plant together with a thorough calculation of specific costs. This information is used by PLANNER to evaluate the corresponding economic objective functions. Besides providing optimal target conditions to the plant and feasible production schedules, the developed tools generate production guidelines in the long term and allow performing scenario studies.

Keywords: concentrated fruit juice; decision making; batch scheduling; production planning; mixed-integer nonlinear programming (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:50:y:2020:i:4:p:255-268

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