## 8 Conclusions and Future Perspectives  
This paper has discussed a complex and realistic warehouse automated storage and retrieval system (AS/RS) in the steel industry sector. The peculiar characteristics of this sector, notably the considerable size and weight of items and bundles stored and retrieved, alongside stringent quality constraints, constitute a challenge when enhancing the system performance. Traditional approaches such as developing a simulation model or employing classical optimization methods, albeit viable, present limitations to providing optimal solutions within reasonable computational time frames. Hence, this paper advocates a novel hybrid simulation optimization algorithm. It combines the precision of discrete event simulations, crucial in managing time dependencies within the system's sequential operations, with a biased-randomized heuristic. This combination, encapsulated within a multi-start framework, swiftly generates a multitude of promising, high-quality solutions in seconds, outperforming the more traditional simulated annealing (SA) metaheuristic. Although the SA algorithm is a robust approach, it struggles to adequately address the intricate time dependencies among events in the warehouse system. Our approach boasts adaptability, a key advantage for accommodating novel scenarios. Based on simulation principles, it holds the potential to evolve into a simheuristic [28], adept at tackling stochastic versions of the problem. This adaptability extends to various AS/RS configurations, thus illustrating its versatility. Furthermore, the computational experiments conducted confirm the capacity of our approach in providing feasible and high-quality solutions for optimization problems characterized by complex constraints, especially those entailing intricate time dependencies among decisions. From a managerial perspective, the proposed approach can enhance decision-making processes, particularly in managing the complexities of AS/RS operations characterized by time-dependent sequences and stringent constraints. The BR-DES algorithm is specifically tailored to the challenges of steel storage systems, including large item sizes, substantial weights, and strict quality constraints. From a business impact perspective, the algorithm contributes to improving the operational efficiency by rapidly generating high-quality solutions, resulting in a reduced makespan and optimized resource utilization. This, in turn, can lead to savings in labor, energy, and equipment usage. The algorithm's focus on reducing the discrepancy between the proposed solutions and customer specifications ensures an enhanced customer satisfaction, thereby positively impacting customer relations. The following points outline the potential directions for future research: (i) extending our approach to achieve full automation, enabling the algorithm to make decisions autonomously without human intervention; (ii) improving the algorithm's handling of weight and quality constraints, particularly in more constrained scenarios, to enhance its effectiveness even further; and (iii) expanding the approach to encompass scenarios  
Figure 9: Quantity mismatch comparison of different approaches. involving random times and stochastic availability of items, broadening its applicability to diverse, less deterministic environments. Conceptualization, M.B. and M.N. ; methodology, A.A.J. and M.N. ; software, M.N. ; writing--original draft preparation, M.N., A.A.J. and M.B. ; writing--review and editing, A.A.J. and M.B. All authors have read and agreed to the published version of the manuscript. This work was partially funded by the Horizon Europe program (HORIZON-CL4-2022-HUMAN-01-14-101092612 SUN and HORIZON-CLA-2021-TWIN-TRANSITION-01-07-101057294 AIDEAS), as well as by the Generalitat Valenciana (PROMETEO/2021/065). Not applicable.