## 5 Experiments
### Discussion
#### 5.4.2 Evaluation of Vehicle Reassignment  
For the random layouts, most configurations do not indicate significant differences in terms of throughput performance between the reassignment-based methods (keys SMP and LSAP) and the method, which does not facilitate reassignment (STTF). We observe some exceptions to this, where one or both of the reassignment-based methods would outperform the STTF method without, which seems to be linked to high vehicle utilisations. This matches our assumption that with increased fleet utilisation any increase in efficiency becomes relevant to maximise the absolute system performance. Correspondingly, we did measure a clear tendency that the reassignment mechanism would benefit the overall efficiency of the transportation system. Thus, the associated scores were reliably better with reassignment (SMP and LSAP) than they were without (STTF). As soon as the vehicle utilisation is high enough that not every trip starts and ends in the parking position, and the volume of transportation is large and spatially interleaved enough, we expect to see a benefit to reassignment. While we originally expected this benefit to also manifest in the throughput rate, we can explain this by the structure of the problem. The previous evaluations in the literature mainly focused on stations with infinite input/output queues, evaluating performance measures such as _mean load waiting time_ (i.e., the average time it takes to pick something back up), often restricting navigation to a graph (or even one or two loops only) that can be traversed unidirectionally (e.g., Bozer and Yen [20]). Under these conditions, the adoption of a heuristic or cost function based on the shortest travel time to pick-up for dispatching seems sensible. However, both metrics (_mean load waiting time_ and _shortest travel time first_) do not reflect the actual throughput performance especially considering multi-step assembly processes, and, as such, cannot be expected to result in increased throughput performance either. We were able to skew the implicit prioritisation characteristics implicitly by layout design (c.f. pseudorandom random layout), i.e., by co-locating the vehicle parking positions and the input/output storage. A better approach would probably involve multi-attribute cost functions, which is in line with previous research. In addition, we designed a number of specific fixed custom layouts according to certain considerations, in an attempt to reflect actual conditions in realistic environments a bit better. Although the results we obtained with these custom layouts indicated the same tendencies, they were not as clear as we expected. Here, we observed statistical differences in throughput, but we could not make out a clear pattern as to why these would occur. The results of several configurations with the SMP method display both lowest throughput performance and lowest travelled distance. This seems to suggest that the result for the lower throughput performance is not _inefficient_ use of the vehicle fleet, but rather _ineffective_ fleet use. Additionally, with several configurations of the custom layouts the STTF method without reassignment would outperform the others, as well at the throughput but occasionally also at the efficiency scores. We suspect confounding factors play into these effects, e.g., the implicit task prioritisation effect described above, or issues with oscillations of (re-)assignments that are especially prevalent with the more complex custom navigation graphs. Interestingly this pattern does not manifest with the random environments, so we expect this effect to be highly specific to the specific layout configurations. In some cases, we noticed oscillations in the assignments. This issue manifested especially, when we first used the straight-line distance for the dispatching decision, but thevehicles would need to follow the navigation graph instead, and thus multiple vehicles at the parking position would constantly swap assignment.