## 7 Discussion
### Implications for Future Research and Practice  
These findings not only advance academic knowledge but also offer valuable insights for managers aiming to achieve higher agility within their supply chain systems. The UDCGP-HGSA model emerges as a crucial tool for industries dependent on operational success through supply chain management due to its adaptability and effectiveness. Future research could explore applying such models in diverse fields, including global transportation systems and healthcare reforms. Additionally, extending the model into logistics at an operational level or applying it within the manufacturing sector could be beneficial, given the challenges posed by uncertainty. The UDCGP-HGSA model establishes a new standard for future research by encouraging further investigation into methods capable of managing the multifaceted problems of global supply chains. Limitations and Future Directions  
While the integration of goal programming (GP), dependent chance constraints (DCC), and the hunger games search algorithm (HGSA) presents a robust framework for optimizaing supply chain management under uncertainty, it introduces significant computational complexity. This complexity arises from solving large-scale optimization problems involving numerous variables and constraints, which can be computationally intensive. Practical Implications and Implementation Barriers  
* **High computational demands:** Implementing GP, DCC, and HGSA together requires substantial computational resources, especially for large supply chains with numerous nodes and planning periods. This can result in long computation times and high storage costs. * **Scalability issues:** As the size of the supply chain increases, the number of variables and constraints grows exponentially, potentially making the model less practical for very large applications without significant computational infrastructure. * **Real-world application constraints:** Data availability, quality, and real-time processing requirements can pose additional challenges. Implementing such a complex model in a business environment may require advanced IT infrastructure and specialized skills. Future Directions  
To address these constraints and enhance feasibility and scalability, future research could focus on the following areas:  
* **Algorithm optimization:** Focus on optimizing the HGSA algorithm to reduce computational overhead by developing more efficient search strategies, utilizing parallel processing techniques, and leveraging high-performance computing resources. * **Model simplification:** Simplify parts of the model or use approximation techniques to manage complexity. For example, breaking down the supply chain network into smaller sub-problems for easier management and optimization. * **Hybrid approaches:** Combine the model with other optimization techniques, such as machine learning algorithms, to improve prediction accuracy and risk analysis. Hybrid approaches can enhance process efficiency and effectiveness. * **Incremental implementation:** Introduce the model incrementally in business cases, starting with smaller, complex supply chains to identify practical issues and refine the model before full-scale implementation. * **Cloud computing:** Utilize cloud computing platforms to handle large-scale optimization problems, offering necessary computational power and flexibility. Cloud computing can facilitate scaling resources and performing computationally intensive tasks more efficiently. * **Data precision:** Ensure the precision of probabilistic and fuzzy parameters, as model performance is directly related to data quality. Genetic algorithm (GA) optimization requires accurate data for effective functioning in challenging environments. * **Computational resources:** The complexity of the HGSA and UDCGP combination necessitates significant computational resources, which may be costly for small businesses or real-time decision-making scenarios.