Fragility-based lot-sizing in veterinary pharmaceutical plants under demand uncertainty

Rafael A. Campos, Aakil M Caunhye, Douglas Alem, Pedro Munari

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We study a production lot-sizing problem inspired by a veterinary pharmaceutical plant in which demands are uncertain. First, we develop a deterministic capacitated lot-sizing model for the production of animal pesticides, performed in three machine-specific stages. Second, we propose a traditional robust optimisation formulation following the popular budget-of-uncertainty approach. Third, we derive a novel fragility-based approach that circumvents well-known issues with traditional robust optimisation approaches, such as the estimation of budgets of uncertainty, the over-conservatism of robust solutions and the sensitivity of solutions to the decision maker's risk attitude. The fragility-based approach is grounded in the idea of minimising violations, over the full uncertainty support, from a user-specified cost target. It avoids the estimation of budgets of uncertainty and produces less conservative solutions via explicit modelling of constraint violation. We demonstrate the effectiveness of our approach on instances built upon real data provided by our industrial partner, a major player in the Brazilian veterinary pharmaceutical sector. The results show that our fragility-based approach reduces average total costs across all instances and maintains greater model stability under different target estimations. It also preserves cost savings when bottlenecks are introduced in production and when inventory costs and capacities are varied.
Original languageEnglish
Pages (from-to)1-37
Number of pages37
JournalInternational Journal of Production Research
Early online date24 Aug 2023
DOIs
Publication statusE-pub ahead of print - 24 Aug 2023

Keywords / Materials (for Non-textual outputs)

  • lot-sizing
  • robust optimisation
  • fragility-based
  • veterinary pharmaceutical industry
  • stochastic programming

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