Florida is typical of many southeastern states in that it exports feeder cattle and imports carcass and boxed beef. The objective of this paper is to estimate the cost of retaining feeder cattle in Florida, feeding these cattle to slaughter weights, slaughtering them, and distributing the meat to retail outlets. A mixed integer programming model is developed. The optimal number and location of feedlots and slaughter plants are determined. The results indicate that at production levels exceeding 600,000 head, the cost of producing carcass beef in the State is comparable to the average for the United States.

Rice production in Arkansas usually involves intensive tillage. No-till rice has been studied, but the focus has been limited to impacts on yields and per acre returns. This study uses mixed integer programming to model optimal machinery selection and evaluate whole-farm profitability of no-till management for rice-soybean farms. Results indicate that lower machinery ownership expenses combined with lower fuel and labor expenses do enhance the profitability of no-till management, but the monetary gains appear to be modest, implying that other incentives may be necessary to entice producers to use the practice.

Although annual rainfall in the Southeast is adequate, its distribution is a potential constraint to agricultural production. Farmers require production information concerning efficient use of irrigation technology adapted to regional growing conditions. Selection of optimal position, size, and number of pivots in center pivot irrigation systems poses special problems on small, irregularly shaped fields. In the southeastern United States, field size and shape are often varied and irregular. A mixed integer programming model was constructed to assist in irrigation investment decisions. The model is illustrated using irrigated peanut production in southeast Alabama. Results indicate the importance of economic engineering considerations.

We address a multi-item capacitated lot-sizing problem with setup times that arises in real-world production planning contexts. Demand cannot be backlogged, but can be totally or partially lost. Safety stock is an objective to reach rather than an industrial constraint to respect. The problem is NP-hard. We propose mixed integer programming heuristics based on a planning horizon decomposition strategy to find a feasible solution. The planning horizon is partitioned into several sub-horizons over which a freezing or a relaxation strategy is applied. Some experimental results showing the effectiveness of the approach on real-world instances are presented. A sensitivity analysis on the parameters of the heuristics is reported.