## 1 Introduction  
A reconfigurable assembly system (RAS) has an open architecture that enables it to respond swiftly and economically to sudden market changes [1]. However, besides the physical capabilities of an RAS, effective production planning remains crucial to the productivity of the system [1, 2]. Generally, the design problems involved in production planning are formulated separately in a hierarchical manner and formulated as independent combinatorial mathematical optimization problems [1, 3]. The process of developing and implementing the different algorithms (using either heuristic and/or exact methods) to solve each mathematical problem is usually a time-consuming endeavor. Furthermore, algorithm design decisions and identification of the appropriate algorithm configuration settings are well-recognized bottlenecks that require ample expert knowledge [4]. During this process, developers will typically use a limited number of benchmark instances, \"fixing\" some of the configuration settings based on the performance observed on those benchmark instances and exposing only a few configuration settings to the final user, who will, in many cases, leave them at their default values [4, 5]. However, achieving optimal algorithm performance for each unique problem instance could be crucial in unlocking its maximum potential. Previous research extensively highlights that an algorithm's effectiveness on one particular problem instance does not ensure superior performance across all instances [4; 6; 7]. In fact, certain algorithms outperform others on specific instances while underperforming on others, demonstrating what is commonly known as \"performance complementarity\". This phenomenon is widely observed across virtually all NP-hard decision and optimization problems [8], including those pertinent to production planning. Nevertheless, it is \"generally unknown a priori which of several algorithms should be used to solve a given instance\" [8]. This decision problem is referred to as the per-instance algorithm selection problem (PIAS), or as the per-instance algorithm configuration problem when selection is made over all valid configurations of a single configurable algorithm [8; 9]. Nevertheless, up until very recently, the literature on PIAC has usually dealt with (highly) heterogeneous instance spaces, with state-of-the-art approaches to PIAC and PIAS such as ISAC [10], and Hydra [11; 12] developed as efficient methods to work within such heterogeneous contexts. However, solving the production planning problem in a RAS often requires that a solution be found repeatedly for the same (parametrical) optimization problem, each time with different problem parameters [13]. This often leads to a seemingly homogenous set of problem instances with a similar (if not identical) number of constraints and decision variables. This is not unique to production planning, but also a theme in other planning environments commonly found in industry, such as daily routing of delivery trucks, internal transport planning using automated guided vehicles in manufacturing facilities [14], or container allocation for rail cars leaving a consolidation terminal [15]. Research on PIAC for homogenous instance spaces remains sparse. Consequently, a common approach when dealing with homogeneous instance spaces is based on a \"one-size-fits-all\" approach [8]. This involves searching and evaluating numerous configurations across a (hopefully) representative set of problem instances to identify the best-performing configuration on average for the benchmark set, with no guarantees for other problem instances not in that set [16; 17]. Moreover, some authors have discovered that homogenous instance spaces may exhibit inherent heterogeneous characteristics, making them better suited for PIAC approaches [16]. Additionally, recent findings by [18] suggest that PIAC methods can yield substantial performance improvements even when dealing with highly homogeneous instance spaces.