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dc.contributor.author Price, Catherine S
dc.contributor.author Moodley, Deshendran
dc.contributor.author Pillay, Anban W
dc.date.accessioned 2019-04-10T10:51:59Z
dc.date.available 2019-04-10T10:51:59Z
dc.date.issued 2018-09
dc.identifier.citation Price, C.S., Moodley, D. and Pillay, A.W. 2018. Dynamic Bayesian decision network to represent growers’ adaptive pre-harvest burning decisions in a sugarcane supply chain. In: Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists, (SAICSIT), Port Elizabeth, South Africa, 26-28 September 2018 en_US
dc.identifier.isbn 978-1-4503-6647-2
dc.identifier.uri https://dl.acm.org/citation.cfm?id=3278681&picked=prox
dc.identifier.uri http://hdl.handle.net/10204/10933
dc.description Presented at: Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists, (SAICSIT), Port Elizabeth, South Africa, 26-28 September 2018. Due to copyright restrictions, the attached PDF file only contains the abstract version of the full text item. For access to the full text item, please consult the publisher's website. en_US
dc.description.abstract Sugarcane growers usually burn their cane to facilitate its harvesting and transportation. Cane quality tends to deteriorate after burning, so it must be delivered as soon as possible to the mill for processing. This situation is dynamic and many factors, including weather conditions, delivery quotas and previous decisions taken, affect when and how much cane to burn. A dynamic Bayesian decision network (DBDN) was developed, using an iterative knowledge engineering approach, to represent sugarcane growers' adaptive pre-harvest burning decisions. It was evaluated against five different scenarios which were crafted to represent the range of issues the grower faces when making these decisions. The DBDN was able to adapt reactively to delays in deliveries, although the model did not have enough states representing delayed delivery statuses. The model adapted proactively to rain forecasts, but only adapted reactively to high wind forecasts. The DBDN is a promising way of modelling such dynamic, adaptive operational decisions. en_US
dc.language.iso en en_US
dc.publisher Association for Computing Machinery, Inc. en_US
dc.relation.ispartofseries Workflow;22135
dc.subject Adaptive operational decisions en_US
dc.subject Burning and harvesting sugarcane en_US
dc.subject Cognitive model en_US
dc.subject Dynamic Bayesian decision network en_US
dc.subject Sugarcane grower en_US
dc.title Dynamic Bayesian decision network to represent growers’ adaptive pre-harvest burning decisions in a sugarcane supply chain en_US
dc.type Presentation en_US


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