Harvest scheduling algorithm to equalize supplier benefits: A case study from the Thai sugar cane industry

In this study, the harvest scheduling problem of a group of cane growers in Thailand is addressed. Each member in a group is required to consistently supply sugar cane to a mill for the entire harvest season. However, the current scheduling does not account for the time-variant cane production of each cane field, which leads to unequal opportunities for growers to harvest. A portion of growers could have the opportunity to harvest in periods that provide higher sugar cane yields, while others in the same group do not. This inefficient harvest scheduling causes conflicts between growers and unnecessary loss of sugar cane and sugar yields. An artificial neural network is applied to estimate cane yield over a harvest season. Then, an optimization model and a heuristic algorithm with the objective of maximizing the estimated sugar cane yield under the condition of fair benefits for all of the growers in the group were developed to determine the most suitable sugar cane harvest schedule for a cluster of sugar cane fields. For the heuristic, the initial solution is first constructed based on the yield trends, and the solution is then improved by the tabu search approach. The results indicated that there are potential benefits from applying the model to cane scheduling within a group of heterogeneous yield trends. Sensitivity analysis showed that the more that the yield trends in a group differ from one another, the higher the benefit the group is likely to gain from adopting the proposed framework. 2014 Elsevier B.V. All rights reserved.