Doctoral Degrees (Industrial Engineering)

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Browsing Doctoral Degrees (Industrial Engineering) by Subject "Agricultural innovations -- South Africa"

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    A harvest and processing decision support system for table grape production
    (Stellenbosch : Stellenbosch University, 2023-02) Wium, Jolene; van Eeden, Joubert; Bekker, James; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: Harvesting and production decisions for large table grape producers are complex. Customers demand specific product requirements, based on the cultivar, packaging type, and grape quality. Harvest efforts by producers depend on the ripeness and volume of stock, which can change over time, justifying a constant modification in company-wide planning. This study concerned the harvest and processing decisions for table grape producer-exporters, using data obtained from a large South African producer exporter as a case study. A mixed-method—exploratory sequential design was employed in this study to support the engineering design process. User requirements were obtained from interviews with operational, tactical, and strategic decision-makers. The user requirements guided the design and build of a system, supporting decisions at each decision-making level in the company. The decision support system uses data and model driven methods. It comprises managing source data, an algorithm for prescribing suitable harvest and pack plans and, last, data visualisation, presenting results and information to the decision-maker. Data were extracted from source systems and stored in a data warehouse providing a stable, non-transactional environment, suitable for large database queries. The non-dominated sorting genetic algorithm II was used to prescribe weekly harvest, processing, and delivery plans. The two model objectives minimised deviation from the demand plan and travel distance between the orchard and the pack site to preserve grape quality. An operational model builds on the weekly tactical model, providing an in-week daily schedule for each pack site and farm. Users could access role-specific visualisations by providing insights into their deliverables. End-users and industry experts validated the decision support system. It was also compared to an existing human system. The developed model closely resembles the human model; however, it can provide a result in a much-improved time. The interconnectedness of the harvest supply and processing facilities justifies an update of the entire plan when an attribute of the plan changes. Changes to either processing or demand factors can easily be incorporated through a model, whereas the human system relies on heuristic methods for an end result. Automation through optimisation, therefore, supplies a solution in a constantly changing harvest and demand plan environment. The study produced a harvest and processing model, contributing to an omission in literature, therefore, focusing on table grapes. Specific handling techniques required for table grapes justifies the need for specific objective function values during modelling. It also presents a unique contribution through a geographically diverse case study focus, accommodating customers’ specific pack-to-order needs. Past studies established orders by cultivar. This study extends past work by including box type and the ability for a customer to reject a specific cultivar as an order.