FIFO Age-Cohort Stochastic MILP for Perishable Inventory Optimization

This paper addresses the perishable inventory optimization problem for fish processing SMEs under compound supply-demand uncertainty. We develop a two-stage stochastic Mixed-Integer Linear Programming (MILP) framework comparing four model variants: two employing the conventional fixed deterioration rate (FDR) approach and two incorporating explicit First-In-First-Out (FIFO) age-cohort tracking, each with and without cold storage investment options. The formulation integrates production scheduling, workforce planning, machine investment, and cold storage decisions over a 12-week horizon under five stochastic scenarios calibrated from empirical data. We prove that the FIFO age-cohort formulation preserves linearity (Proposition 1), establish theoretical dominance of FIFO over FDR under surplus conditions (Proposition 2), and demonstrate feasibility preservation through an adaptive service level constraint (Proposition 3). Computational results on empirical instances show that FIFO models achieve 25.0% expected cost reduction with perfectly stable service levels (70.0% across all scenarios, zero variance) compared to FDR models exhibiting 58.7 percentage point service level volatility. Extended sensitivity analysis across 15 parameter configurations reveals that cold storage value is conditional: marginal (0.046%) under supply-constrained regimes but significant (up to 19.2%) under supply surplus with high expiration costs. Pareto frontier analysis confirms FIFO dominance across the entire cost-service level trade-off space. The Value of Stochastic Solution (VSS) reaches 12.4%, validating the stochastic approach. All configurations solve within 15.1 seconds despite 18,540 variables, with FIFO solving 5.7× faster than FDR due to a tighter constraint structure. Managerial implications include a conditional decision framework linking supply-demand regime identification to optimal investment strategy.

@article{Rachman2026,
  title     = {FIFO Age-Cohort Stochastic MILP for Perishable Inventory Optimization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {4},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Hirman Rachman and Saib Suwilo and Sutarman and Elvina Herawati},
  doi       = {10.14569/IJACSA.2026.0170419},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170419}
}