A Two-Stage Stochastic Model for Selection of Processing Hubs to Avoid Broccoli Losses

Abstract

It is estimated that, at present, around one third of all food produced is lost, either in production and distribution or after retail. To further complicate matters, uncertainty and variability in the commercial and natural environments must also be taken into account when trying to reduce food losses. The objective of the present paper is to develop a decision support system to increase the efficiency of the Australian broccoli supply chain and reduce food losses considering uncertainty. To that end, we develop a two-stage stochastic mixed-integer linear programming model to assist Australian broccoli producers in taking the most cost-effective investment decisions and, at the same time, reduce the losses by producing novel, high value-added products from produce discarded on the field or during transportation. The stochastic model we propose selects, in the first stage, the optimal location of processing facilities to add value to the produce that would otherwise be considered as food loss, and suggests transportation operations as the recourse decisions. The model is solved using Lagrangian decomposition and the subgradient method. The data used to feed the model was collected on the field through a survey applied to broccoli growers nationwide. The model suggests near-optimal investment decisions that are far from the worst possible outcome, had the final market and environmental conditions turned out to be very adverse. Our results represent viable operations for the industry in the medium term.