A Real-World Transport Scheduler Applied to Australian Sugarcane Industry

This paper describes a new approach to develop a real-world automated scheduler applicable for Australian sugarcane industry. In Australia, the transport sector plays a critical role in raw sugarcane harvest and accounts for over 35% of the total cost of raw sugar production. The generation of an optimised schedule can bring the following practical benefits: eliminate bin supply delays to harvesters, minimise the number of locomotives/bins, reduce the locomotive shifts, control the sugarcane age/quality, etc. To generate such a scheduler, a new optimisation approach is developed based on job shop scheduling techniques using constraint programming and mixed integer programming. The proposed approach can produce solutions for small-scale and large-scale cases in agriculture/crops transport systems in a reasonable time. Mixed integer programming focuses on objective function using linear relaxation to prune suboptimal solutions, while constraint programming focuses on the model using filtering algorithms to eliminate infeasible candidate solutions. The applicability of the developed scheduler has been validated by a real-world case study for Kalamia Mill in Queensland, Australia. Following from the validation and discussion, it is concluded that the automated scheduler would be a valuable optimisation tool for transport modellers in Australian sugarcane industry.