# A Survey on Variable Neighborhood Search for Sustainable Logistics
## 7 Conclusions  
The analysis of recent trends in the literature on VNS for sustainable logistics highlights key findings and future directions. Since 2019, there has been a significant increase in publications on VNS, demonstrating its potential to tackle complex logistics challenges and promote sustainability. Most studies focus on routing and scheduling problems, which are critical for reducing costs and environmental impacts. The most frequently used VNS variants are GVNS and BVNS. Additionally, the growing interest in multiobjective optimization reflects the increasing need to achieve a balance between economic, environmental, and social goals in logistics systems. The hybridization of VNS with techniques such as SA, TS, and GA has enhanced both flexibility and solution quality for complex logistics problems. However, significant gaps remain. One key gap is the underrepresentation of uncertainty and dynamism in VNS models, despite their relevance to real-world logistics networks with fluctuating demand and variable travel times. Future research should explore robust optimization methods and dynamic adaptation mechanisms to improve the practical applicability of VNS. Another critical gap is the limited focus on social sustainability, in contrast to the more frequently addressed economic and environmental dimensions. Aspects such as worker well-being, safety, and job creation must be integrated into models to promote socially responsible logistics practices. Moreover, there is significant potential to innovate by combining VNS with emerging technologies, such as machine learning and reinforcement learning, which could help address the complexities of evolving logistics challenges. The lack of standardized benchmarks and performance metrics for VNS in sustainable logistics is another challenge. Establishing common standards would facilitate more meaningful comparisons across studies and support the identification of strengths and weaknesses in VNS applications. Furthermore, while environmental sustainability is frequently addressed, broader integration of metrics such as resource efficiency and waste reduction is essential for achieving comprehensive optimization. New application areas, including renewable energy logistics, last-mile delivery using drones, and sustainable tourism, offer exciting opportunities for advancing VNS research. Adapting VNS methods to address these novel challenges could pave the way for innovation and practical advancements. This study also reveals areas for improvement and caution when implementing VNS. A critical insight is the prevalent focus on minimizing a composite function that combines cost factors and sustainability metrics. While this approach provides a straightforward framework, it often oversimplifies the interrelations and trade-offs inherent in sustainability-focused logistics problems. Treating such problems as genuinely multiobjective rather than relying solely on weighted linear combinations could yield richer solutions. Furthermore, the literature indicates that hybrid approaches involving VNS are more common than standalone applications for multiobjective problems, showcasing the importance of combining VNS with complementary methods for enhanced performance. A recurring issue is the lack of systematic justification for key algorithmic choices in VNS implementations, such as neighborhood structures, their order of application, and the strategy for improvement selection. While expert intuition often guides these decisions, rigorous experimental validation is necessary to ensure the efficiency and effectiveness of the selected structures. Emerging trends, such as the use of adaptive versions, offer promising avenues to enhance VNS. By incorporating AI techniques, these methods dynamically adjust the relevance of neighborhood structures based on performance, paving the way for more robust and automated optimization. However, this adaptability must also be backed by thorough experimentation to validate its impact on solution quality and computational efficiency.