An integrated statistical and optimisation approach to increasing sugar production within a mill region
Individual farms within a sugar mill region exhibit large differences in sugar content of cane (CCS), and hence in sugar yield, due to harvest date, crop variety, soil type and geographical location of the farms. For example, the CCS of some farms peaks before the middle of a harvest season, while the CCS of other farms peaks towards the end of the season. While it is desirable to harvest all cane when the likely sugar yields are at the season's peak, this is not possible due to limited capacities of harvesting, mill crushing and transport. The harvesting of cane is therefore carried out over several months. In this study, a second-order polynomial was employed to fit CCS across sugar cane farms within a mill region. Parameters estimated from the model were used within a linear programming model for better harvest scheduling to maximize gain in CCS in a harvest season. The parameters were also used to classify sugarcane farms into CCS trends groups. We applied this technique to three mill regions within the Australian sugar industry and showed potential gains in profitability from increased CCS to be A$ 1.10 per tonne of cane on average. A software package, encapsulating the statistical and linear programming models, was developed to enable the harvesting groups and growers within the industry to produce their own optimised schedules. In a trial application of the package during the 2003 harvest season, schedules were produced for about 20 growers.
ccs sugar cane linear programming harvesting scheduling ccs trend group polynomial regression