Optimal mechanical harvester route planning for sugarcane field operations using particle swarm optimization

Abstract

Since current agricultural production systems such as the sugarcane supply system in the sugar industry are developing towards larger and more complicated systems, there is consequently increasing use of agricultural machinery. Even though mechanization can help to increase the sugarcane yield, if the mechanical operation efficiency is low, then higher harvest costs and machinery shortages will occur. Global route planning for mechanical harvesters is one of the most important problems in the field of sugarcane harvesting and transporting operations. Improved efficiency and realistic implementation can be achieved by applying advanced planning methods for the execution of field operations, especially considering the field accessibility aspect. To address this issue, participative research was undertaken with a sugar milling company to produce and implement a mixed integer programming model that represents the mechanical harvester route plan. Particle swarm optimization was applied to find a solution to the model, leading to potential cost savings versus schedules produced manually by the mill officer. The model was also applied to explore regional planning options for a more integrated harvesting and transport system.