Food supply by heuristic algorithms

Abstract

Food supply in Latin American SMEs faces technological, operational, and management constraints that lead to inefficiencies, excess inventory, and high costs. The literature supports that periodic review (PRP) models and adaptive heuristics generate robust results, although there is still a discrepancy between theory and practice due to the complexity and resources required. As indicated, “The study focuses on production, inventory, and route planning for perishable products in a two-level production and routing problem” (Wei et al., 2020, p. 4). Methods have proven effective even in small entities, as “this model can be effectively applied in small organizations with limited financial budgets and high perishable raw materials.” (Cabrera-Gala et al., 2021, p. 34). Using the SALSA methodology, 66 articles were analyzed, of which 12 met the inclusion criteria, showing cost reductions of 30% to 45% and service level improvements of 25% to 35% (Wei et al., 2020, p. 4).  Based on the research, a PRP model combined with adaptive heuristics is designed to maximize snack packaging, using real demand data. The model allocates the load among operators, keeping them below the 160-hour monthly limit, backed by evidence that “experiments show that the dynamic approach reduces inventory costs and improves response times compared to conventional policies.” (Minner & Transchel, 2015, p. 986). Simulations and statistical analyses confirm differences in efficiency over traditional methods (p < 0.001), approving operational and economic improvement. The incorporation of input and raw material analysis reinforces the model's consistency with actual production needs.