A Hybrid Optimization-Simulation Approach for Supply Chain Network Design under Uncertainty and Highly Perishable Environments

Abstract

Designing and managing logistics and supply chain networks for perishable products requires careful consideration of product quality degradation over time due to environmental conditions and processing delays. This study proposes a Hybrid Optimization-Simulation (HOS) framework that combines a Mixed-Integer Linear Programming (MILP) model with a Discrete Event Simulation (DES) to design a cost-efficient and quality-preserving distribution network for floral products. The MILP model incorporates a TimeTemperature Sum (TTS) constraint to account for product perishability, while the simulation model evaluates the network’s operational feasibility under dynamic conditions. A case study of the floriculture supply chain, involving growers, hubs, and retailers, is used to validate the proposed approach’s performance. The results show that the deterministic MILP achieves the lowest total cost of $163,418, but does not account for uncertainty. In contrast, the proposed HOS outperforms the typical Simulation-Based Optimization (SBO) by 4.78% in total cost and achieves a much shorter computation time. In addition, the HOS framework can produce logistics and supply chain network configurations that balance cost efficiency and quality assurance, providing a robust tool for supply chain design in highly perishable environments.