Department of Economics and Business Economics

An approximate dynamic programming approach for sequential pig marketing decisions at herd level

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One of the most important operations in the production of growing/finishing pigs is the marketing of pigs for slaughter. While pork production can be managed at different levels (animal, pen, section, or herd), it is beneficial to consider the herd level when determining the optimal marketing policy due to inter-dependencies, such as those created by fixed transportation costs and cross-level constraints. In this paper, we consider sequential marketing decisions at herd level. A high-dimensional infinite-horizon Markov decision process (MDP) is formulated which, due to the curse of dimensionality, cannot be solved using standard MDP optimization techniques. Instead, approximate dynamic programming (ADP) is applied to solve the model and find the best marketing policy at herd level. Under the total expected discounted reward criterion, the proposed ADP approach is first compared with a standard solution algorithm for solving an MDP at pen level to show the accuracy of the solution procedure. Next, numerical experiments at herd level are given to confirm how the marketing policy adapts itself to varying costs (e.g., transportation cost) and cross-level constraints. Finally, a sensitivity analysis for some parameters in the model is conducted and the marketing policy found by ADP is compared with other well-known marketing polices, often applied at herd level.

Original languageEnglish
JournalEuropean Journal of Operational Research
Volume276
Issue3
Pages (from-to)1056-1070
Number of pages15
ISSN0377-2217
DOIs
Publication statusPublished - 2019

    Research areas

  • Approximate dynamic programming, Herd management, Markov decision process, OPTIMAL REPLACEMENT, OR in agriculture, Stochastic dynamic programming

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