Managing Operations of a Hog Farm Facing Volatile Markets: Inventory and Selling Strategies
研究养猪场在每周根据存栏量和市场价格，决定出售给肉联厂或公开市场的动态规划问题，提出近似动态规划启发式策略，相比现有实践平均提升约25%利润。
Problem definition: We study a dynamic finishing-stage planning problem of a pork producer who at the beginning of each week gets to see how many market-ready hogs she has available for sale and the current market prices. Then, she must decide which hogs to sell to a meatpacker and on the open market and which hogs to hold until the following week. The farmer is contracted to deliver a fixed quantity of hogs to the meatpacker each week priced according to a contractually predetermined market index. If the farmer underdelivers to the meatpacker, she pays a contractually predetermined unit penalty also linked to a market index. Biosecurity protocols prevent the farmer from buying hogs on the open market and selling them to the meatpacker. The farmer can, however, use the open market to sell hogs for prevailing market prices. Methodology/Results: We treat the problem as a dynamic, multiitem, nonstationary inventory problem with multiple sources of uncertainty. The optimal policy is a threshold policy with multiple price-dependent thresholds. The computational complexity required to evaluate the thresholds is the biggest impediment to using the optimal policy as a decision-support tool. So, we utilize an approximate dynamic programming approach that exploits the optimal policy structure and produces a sharp heuristic that is easy to implement. Managerial implications: Numerical experiments calibrated to a pork producer’s data reveal that the optimal policy with the heuristically estimated thresholds substantially improves the existing practice (around 25% on average). The success of the proposed model is attributed to recognizing the value of holding underweight hogs and effectively hedging supply uncertainty and future prices—an insight missed in the planning actions of the current practice. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1216 .