## 7 Conclusions and Future Work  
In this work, we assessed the performance of methods to solve the per instance algorithm configuration problem (PIAC) in a highly homogeneous instance space. We performed our study in the context of the solutions to the process planning problem in a reconfigurable assembly system (RAS). For this, we followed the formulation of the process planning problem as an integer quadratic programming (IQP) problem and solve each problem instance using Gurobi. For this use case, we compared two drastically different methods for PIAC, namely the state-of-the-art method, Hydra, and a simpler case-based reasoning approach (the CBR method). We focused on PIAC implementations to identify  
Figure 18: Marginal increase in average PAR-10 reduction when using the CBR portfolio method instead of the CBR method. Results are presented per case base type and per case base size. The dotted lines connect the mean values.  
the algorithm configurations that minimize computation time rather than improving solution quality. Furthermore, we assessed the impact on each method's performance with respect to the tuning time budget and/or the number of unique problem instances used for training.  
Our work suggests the suitability of simple PIAC methods such as the CBR method in dealing with homogeneous instance spaces. Our experiments show that the algorithm configurations provided via the CBR method can lead to reductions in PAR-10 (when compared with Gurobi's default configuration) of up to 16% with a case base that contains as few as 100 unique problem instances.  
In contrast to the simpler CBR method, Hydra--a PIAC state-of-the-art method designed for (highly) heterogenous instance spaces--struggles to reach the performance of Gurobi's default configuration. Additional analysis of Hydra's performance suggests that the reason for Hydra's poor performance could be attributed, in part, to the way its underlying portfolios are constructed.  
Another aspect limiting Hydra's performance in homogeneous instance spaces is perhaps its inference mechanism. To test this, we evaluated the portfolios used in Hydra following the same inference policy used in the CBR method, resulting in a portfolio-based CBR method. The CBR portfolio method drastically improved comapred with Hydra, and even in some cases seemed to outperform the CBR method, albeit slightly. Further discussion concludes, however, that the high flexibility, comparative higher robustness, and good performance of the CBR method are key advantages of using it instead of portfolio methods, especially in dynamic production environments such as a RAS.  
Finally, the execution of either of the PIAC methods requires extensive sampling, which provides a big deal of information about the problem at hand. Future work may deal with the incorporation of this knowledge into the feature embedding process for different PIAC methods, or in the portfolio construction process. In addition, future work can also assess the impact of different sampling strategies (for the definition of the set of training problem instances) on the performance and robustness of the PIAC methods.  
Conceptualization, D.G.V. and S.G.; methodology, D.G.V.; software, D.G.V. and M.U.; investigation, D.G.V.; writing--original draft preparation, D.G.V.; writing--review and editing, S.G., B.R. and V.L.; supervision, S.G.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Flanders Make SBO project AssemblyRecon.  
Not applicable.  
The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to [EMAIL_ADDRESS].  
The authors declare no conflicts of interest.