Economics of Establishing and Producing Pitaya in Southern Florida: A Stochastic Budget Analysis

Pitaya (Hylocereus spp.), a climbing vine of cactus species native to the tropical forest regions of Mexico, Central America, and South America, has gained the attention of many southern Florida growers. Between 2006 and 2010, pitaya production has grown 6-fold. Several factors are responsible for the increased attention being given to this crop; chief among these is the promise of high net returns stemming from the current strong demand for the fruit within an increasing U.S.–Asian population and among mainstream health-conscious U.S. consumers who are lured by the high antioxidant properties and other reported health benefits associated with the fruit. However, the downside risk associated with producing and marketing the fruit needs also to be taken into consideration before dedicating a significant amount of resources to large-scale plantings of pitaya. This article provides information on the financial feasibility of establishing and operating a 5-acre pitaya orchard in southern Florida and assesses the risks of doing so. In conducting our analysis, we use deterministic and stochastic budgeting models, including stochastic yields and prices, to calculate the financial returns. Both the deterministic and simulation risk analysis results suggest that operating a pitaya orchard would likely be profitable over a 20-year planning horizon. Despite the favorable outcome of the analysis, southern Florida growers are advised to proceed with caution, as the market for the crop could easily be oversupplied by domestic and foreign competitors.

Solving the Multi-Period Disassembly-to-Order System Under Stochastic Yields, Limited Supply, and Quantity Discount

Our previous model [1] considered the disassembly-to-order (DTO) system where a variety of returned products were disassembled in order to satisfy the demand for specified number of components in a single period with limited supply and quantity discount. This paper extends the previous model by solving the problem in multiple periods. Lingo 11.0 and spreadsheet analysis are used to solve the problem under stochastic yields, limited supply, and quantity discount. The main objective of the model is to determine the optimal number of take-back end-of-life (EOL) products for the DTO system that maximizes the total profit. In order to meet the customer’s demand for the different components, a wide variety of products and subassemblies are considered for disassembly. Our model is further complicated because the operating conditions of the take-back EOL products are unknown. As a result, heuristic approach is used to transform the stochastic yields into deterministic equivalents. Additionally, several factors are considered before disassembling any product. When solved, the model provides an optimal ordering policy for the multi-period DTO system. An example is considered to illustrate the use of the model.