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A hybrid optimization-simulation approach for supply chain network design under uncertainty and highly perishable environments: a case study of cut flowers |
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| รหัสดีโอไอ | |
| Title | A hybrid optimization-simulation approach for supply chain network design under uncertainty and highly perishable environments: a case study of cut flowers |
| Creator | Jirawat Chatchaichalermporn |
| Contributor | Navee Chiadamrong, Advisor |
| Publisher | Thammasat University |
| Publication Year | 2568 |
| Keyword | Perishable products, Mixed integer linear programming (MILP), Multi-objective optimization, Simulation, Hybrid approach |
| Abstract | Designing supply chain networks for perishable products is challenging due to quality deterioration caused by time, temperature exposure, and operational uncertainty. This study aims to develop a decision-support framework that simultaneously minimizes total logistics costs and product waste while ensuring required quality levels in a cut-flower supply chain. To achieve this objective, a Multi-Objective Hybrid Optimization–Simulation (MOHOS) framework is proposed that integrates a Mixed-Integer Linear Programming (MILP) model with a Discrete Event Simulation (DES) model. The MILP model determines optimal hub locations, processing assignments, and product flows using Time–Temperature Sum (TTS) constraints to represent quality degradation, while conflicting objectives of cost minimization and waste reduction are balanced using the Zimmermann programming approach. The resulting network designs are then evaluated under operational uncertainty through simulation, and an iterative feedback mechanism is applied to refine quality constraints. The results indicate that deterministic MILP solutions underestimate waste when uncertainty is ignored, whereas simulation-based optimization produces more realistic outcomes but costlier solutions. The proposed hybrid approach achieves a balanced compromise, reducing waste while maintaining feasible costs under stochastic conditions. Sensitivity analysis further shows that increasing uncertainty significantly increases both costs and waste and may lead to infeasible solutions beyond a certain threshold, highlighting the practical value of the MOHOS framework for perishable supply chain decision-making. |
