## 8 Conclusions and Future Work  
This paper has reviewed the concept of biased-randomized discrete-event heuristics, which hybridizes biased-randomization techniques with discrete-event simulation concepts  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Problem** & **Acronym** & **Reference** \\ \hline Dynamic Home Service Routing Problem & DHSRP & [10] \\ Multi-Server Flow-Shop Problems with Machine Re-Entering & MS-FSP & [33] \\ Multi-Vehicle Container Transport & MVCT & [42] \\ Hybrid Flow-Shop Problem with Time Dependencies & HFSP & [34] \\ Hybrid Flow-Shop Problem with Batching and Multiple Paths & HFSP & [32] \\ Omnichannel Vehicle Routing Problem & OVRP & [31] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected optimization problems.  
Figure 8: Gaps between OBS and BKS (baseline 0% gap).  
\begin{table}
\begin{tabular}{c c r r r} \hline \hline
**Reference** & **Problem** & **BKS** & **OBS** & **GAP (\%)** \\ \hline [PERSON] et al. [10] & DHSRP & 895.0 & 897.3 & 0.26\% \\ [PERSON] et al. [33] & MS-FSP & 1336.3 & 1392.7 & 4.22\% \\ [PERSON] et al. [42] & MVCT & 8.4 & 8.4 & 0.00\% \\ [PERSON] et al. [34] & HFSP & 10,079.8 & 9913.6 & \(-1.65\%\) \\ [PERSON] et al. [32] & HFSP & 13,975.1 & 13,844.6 & \(-0.93\%\) \\ [PERSON] et al. [31] & OVRP & 520.2 & 527.2 & 1.36\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information on the selected problems.  
with the purpose of solving dynamic optimization problems with time dependencies or synchronization issues. The manuscript also explains why the BR-NEH approach can effectively solve complex dynamic optimization problems with time dependencies, which can be frequently found in real-life applications. The way in which the different components interact in a BR-DEH has also been discussed, emphasizing the different stages that can contribute to make the approach more efficiently while searching for high-quality solutions that satisfy the synchronization issues.  
Recent applications of BR-DEH algorithms in fields such as logistics and transportation or manufacturing and production have also been analyzed, and a numerical summary of previous works illustrating the capabilities of BR-DEH methods to provide high-quality solutions to different dynamic problems is also provided.  
There are several lines of research that are still open in the field of BR-DEH algorithms. Among these, we can highlight the following ones: (i) the introduction of machine learning methods that allow us to predict the evolution of the system, and, therefore, help us to make better decisions in the long term; (ii) the efficient and easy integration of BR-DEH code developed with modern programming languages with commercial simulators like FlexSim, which currently supports a friendly interaction with Python; and (iii) the extension of BR-DEH algorithms into simineuristics [50; 51], so they can also deal with stochastic versions of the dynamic optimization problems.  
Conceptualization, A.A.J. and M.A.; methodology, J.C. and M.N.; software, J.P.; writing--original draft preparation, J.C., M.N., M.A. and J.P.; writing--review and editing, A.A.J. All authors have read and agreed to the published version of the manuscript.  
This work has been partially funded by the Spanish Ministry of Science (PID2019-111100 RB-C21-C22 /AEI/10.13039/501100011033), the Barcelona City Council and Fundacio \"la Caixa\" under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001) and the Generalitat Valenciana (PROMETEO/2021/065).  
Not applicable.  
The authors declare no conflict of interest.