Modelling sugarcane supply consistency at a sugar mill

Kadwa, M.; Bezuidenhout, C. N.

doi:10.1016/j.compag.2014.12.019

Cited by 13 publications

(7 citation statements)

References 27 publications

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“…Weather can also affect the cane burning decision: the grower cannot burn in rain, or in high winds. Long periods of soft soaking drizzle leave the cane wet, unable to be burned (Kadwa & Bezuidenhout, 2015). After rain, cane dries based on the daily temperature and wind.…”

Section: Case Studymentioning

confidence: 99%

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An adaptive, probabilistic, cognitive agent architecture for modelling sugarcane growers’ operational decision-making

Price¹,

Moodley

Pillay

et al. 2022

SACJ

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Building computational models of agents in dynamic, partially observable and stochastic environments is challenging. We propose a cognitive computational model of sugarcane growers’ daily decision-making to examine sugarcane supply chain complexities. Growers make decisions based on uncertain weather forecasts; cane dryness; unforeseen emergencies; and the mill’s unexpected call for delivery of a different amount of cane. The Belief-Desire-Intention (BDI) architecture has been used to model cognitive agents in many domains, including agriculture. However, typical implementations of this architecture have represented beliefs symbolically, so uncertain beliefs are usually not catered for. Here we show that a BDI architecture, enhanced with a dynamic decision network (DDN), suitably models sugarcane grower agents’ repeated daily decisions. Using two complex scenarios, we demonstrate that the agent selects the appropriate intention, and suggests how the grower should act adaptively and proactively to achieve his goals. In addition, we provide a mapping for using a DDN in a BDI architecture. This architecture can be used for modelling sugarcane grower agents in an agent-based simulation. The mapping of the DDN’s use in the BDI architecture enables this work to be applied to other domains for modelling agents’ repeated decisions in partially observable, stochastic and dynamic environments

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Section: Case Studymentioning

confidence: 99%

“…After rain, cane dries based on the daily temperature and wind. High winds pose a risk of runaway fires during burning (Kadwa & Bezuidenhout, 2015).…”

Section: Case Studymentioning

confidence: 99%

An adaptive, probabilistic, cognitive agent architecture for modelling sugarcane growers’ operational decision-making

Price¹,

Moodley

Pillay

et al. 2022

SACJ

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“…At the beginning of the process, two variables belonging to the raw material process were important: chopped sugarcane delivered (1) and total TRS extraction (2). The importance can be related to the fact that transport operations of raw material withdrawn from the field to the industry, require a limit of hours and continuity of the same, and long periods of storage affect the quality, the profitability of sugarcane and the extraction of TRS (Kadwa & Bezuidenhout, 2015;Carvalho et al, 2016;Masoud et al, 2016).…”

Section: Raw Materialsmentioning

confidence: 99%

Proposal of stages by controlling the efficiency analysis in sugar and ethanol mills

et al. 2018

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This study aimed to identify the main points of control in the efficiency of the productive process in sugarcane mills. Thus, a two-stage DEA approach applied for a sample of 121 mills, distributed among the seasons of 2010/2011 until 2014/2015. The first stage separated the efficient mills in transforming the total recovered sugar (TRS) of the processed sugarcane into sugar and ethanol, and the second stage identified significant production process variables in the efficiency with truncated regression. Besides, with the help of industry experts and statistical analysis, established 13 variables, which could control and increase the efficiency of the sugar and ethanol production processes, contributing to the overall efficiency of sugar and ethanol units.

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“…The important actors in the Indian sugar supply chain include cane farmers; transporters; millers; distributors (including brokers, wholesalers, and retailers) and lastly, consumers. The starting raw material, i.e., sugar cane comes from farmers who sell it to the millers for producing sugar and this chain ends at consumer who uses sugar in a variety of ways (Amu et al, 2013;Kadwa and Bezuidenhout, 2015;Kumar et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

IT adaptation in sugar supply chain: a study at milling level

Kumar

Nath

2020

IJLSM

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The purpose of the study is to catalogue issues in the sugar supply chain at the procurement and sugar production levels. By a review of prior art and expert panel discussions, certain issues were identified for the facilitation of e-applications in the sugar production process. The relevance of the issues identified was checked by conducting a field survey. A total of 30 sugar mills in the states of Uttar Pradesh and Uttarakhand, India were targeted and 120 were collected from the mill managers to identify issues at procurement and sugar production stages. For checking the scope of IT applications, a total of 180 respondents were collected from 120 mill managers and 60 employees of IT companies who provide supply chain IT solutions. On the basis of the findings, the paper proposes certain IT solutions for solving the identified issues which will help in improving sugar cane processing at the sugar production level.

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Modelling sugarcane supply consistency at a sugar mill

Cited by 13 publications

References 27 publications

An adaptive, probabilistic, cognitive agent architecture for modelling sugarcane growers’ operational decision-making

An adaptive, probabilistic, cognitive agent architecture for modelling sugarcane growers’ operational decision-making

Proposal of stages by controlling the efficiency analysis in sugar and ethanol mills

IT adaptation in sugar supply chain: a study at milling level

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