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An optimisation approach for the agricultural and industrial tactical planning in the fresh fruit processing industry.

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Title: An optimisation approach for the agricultural and industrial tactical planning in the fresh fruit processing industry.
Authors: Rocco, Cleber Damião1 (AUTHOR) cdrocco@unicamp.br, Guimarães, Luís2 (AUTHOR), Almada-Lobo, Bernardo2 (AUTHOR), Morabito, Reinaldo3 (AUTHOR)
Source: International Journal of Production Research. Oct2025, Vol. 63 Issue 19, p6982-7012. 31p.
Subjects: Mixed integer linear programming, Fruit processing, Scheduling, Optimization algorithms, Inventory control, Manufacturing processes, Resource allocation, Agriculture
Abstract: This paper presents an optimisation approach based on mixed-integer programming for tactical planning decisions within fresh fruit processing industries. It applies to fruits such as oranges, tomatoes, guavas and others, where diluted fruit juice needs to be concentrated in evaporators to produce semi-finished or finished products. It considers agricultural and industrial activities, integrating them to address complex and interconnected decisions. Agricultural tasks include planting, harvesting, and transporting fruits from fields to processing plants, while industrial activities involve the production, inventory, and transportation of semi-finished and final products. This approach accommodates multiple agricultural regions, fruit varieties, processing plants, and products, operating on a weekly basis within a one-year planning horizon. It offers a detailed solution for harvesting, the fruit juice concentration process, inventory management for the products produced, and transportation of raw materials and products among processing plants. Production of semi-finished products is modelled using the Proportional Lot-Sizing and Scheduling Problem and the production of finished products is modelled adopting a blending lot-sizing problem. The results were validated through computational experiments using a dataset from a company that processes tomatoes and guavas. Scenario analyses were conducted to evaluate the solution's consistency and real-world applicability. The findings indicate that the approach can support decision making in practice, highlighting its potential as a valuable managerial, analytical, and optimisation tool for some agri-food industries. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: An optimisation approach for the agricultural and industrial tactical planning in the fresh fruit processing industry.
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  Data: <searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Fruit+processing%22">Fruit processing</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Inventory+control%22">Inventory control</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Agriculture%22">Agriculture</searchLink>
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  Data: This paper presents an optimisation approach based on mixed-integer programming for tactical planning decisions within fresh fruit processing industries. It applies to fruits such as oranges, tomatoes, guavas and others, where diluted fruit juice needs to be concentrated in evaporators to produce semi-finished or finished products. It considers agricultural and industrial activities, integrating them to address complex and interconnected decisions. Agricultural tasks include planting, harvesting, and transporting fruits from fields to processing plants, while industrial activities involve the production, inventory, and transportation of semi-finished and final products. This approach accommodates multiple agricultural regions, fruit varieties, processing plants, and products, operating on a weekly basis within a one-year planning horizon. It offers a detailed solution for harvesting, the fruit juice concentration process, inventory management for the products produced, and transportation of raw materials and products among processing plants. Production of semi-finished products is modelled using the Proportional Lot-Sizing and Scheduling Problem and the production of finished products is modelled adopting a blending lot-sizing problem. The results were validated through computational experiments using a dataset from a company that processes tomatoes and guavas. Scenario analyses were conducted to evaluate the solution's consistency and real-world applicability. The findings indicate that the approach can support decision making in practice, highlighting its potential as a valuable managerial, analytical, and optimisation tool for some agri-food industries. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00207543.2025.2490981
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      – Code: eng
        Text: English
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        PageCount: 31
        StartPage: 6982
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      – SubjectFull: Mixed integer linear programming
        Type: general
      – SubjectFull: Fruit processing
        Type: general
      – SubjectFull: Scheduling
        Type: general
      – SubjectFull: Optimization algorithms
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      – SubjectFull: Inventory control
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      – SubjectFull: Manufacturing processes
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      – SubjectFull: Resource allocation
        Type: general
      – SubjectFull: Agriculture
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      – TitleFull: An optimisation approach for the agricultural and industrial tactical planning in the fresh fruit processing industry.
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            NameFull: Rocco, Cleber Damião
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            NameFull: Guimarães, Luís
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            NameFull: Almada-Lobo, Bernardo
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            NameFull: Morabito, Reinaldo
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            – D: 01
              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
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