Capacity planning in a sugarcane harvesting and transport system using simulation modeling

REDUCING costs within the harvesting and transport system is a high priority for many sugar milling regions in Australia. Typical issues include reducing the number harvesting groups, harvesting over a longer time window in a day, rationalising infrastructure and achieving a better co-ordination between harvesting and transport activities. As part of a series of integrated models to conduct the analysis, a simulation model for capacity planning was developed to estimate the: 1) number of locomotives and shifts required; 2) the number of bins required; and 3) the period of time harvester operators spend waiting for bins. The model belongs to the category of planning models for unscheduled traffic, which means a locomotive schedule does not need to be produced. While new for the Australian sugar industry, these types of models have been used in the past extensively for planning in road and urban/freight railroad systems. Some key advantages of the model versus a scheduling tool are the ability to: 1) work in situations with high probabilities of delay and down time in the transport system; 2) measure capacity independently of a schedule or in applications where it is impossible to produce an effective transport schedule using a model; and 3) fully integrate with other models within the sugar harvesting and transport system for whole-of-system optimisation. The benefits of the model are demonstrated through application to the Mourilyan case study, to provide the region with an understanding of the impacts from: 1) removing double handling of bins; 2) extending the time window of harvesting; 3) reducing the number of harvesting groups; 4) and upgrading bin fleet and sidings. A scenario for harvesting during a time window of 18 hours was piloted within Mourilyan during the 2003 harvest season. The benefits of integrating the capacity planning model with a model for scheduling harvesters into sidings is demonstrated with the Mossman case study, showing significant reductions in the daily variability of demand on the transport system.