# Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals
## 4 Conclusions  
This study provides an approach to solving integrated decision problems at container terminals. Earlier studies have often approached such problems by using a mathematical model that aims to optimize the schedule of jobs. Depending on the concept, sometimes the schedule is determined hours before the actual execution of a job, which is an appropriate approach in rather deterministic environments. Another common approach in the literature is to define a policy that is evaluated using a simulation study. The design of experiments--e.g., a full-factorial design with a coarse grid--leads to either very large simulation studies or a selection of experiments biased by the researcher's beliefs. These shortcomings of optimization alone and manually designed large simulation studies are partly overcome by the presented simulation-based optimization approach. This approach uses simulation to evaluate the quality of a given solution, and only in this way can the dynamics of real systems be properly represented. Simulation-based optimization allows for the possibility of illustrating these dynamics, providing an approximated solution to the problem without maintaining a separate mathematical model. Therefore, further decision problems can be integrated into the simulation model and the parameter configuration space with little effort. The authors showed the transferability of meta-heuristics, which originate from the domain of machine learning or have been successfully applied in that area. These methods could be used to optimize discrete event simulation models. A special focus was placed on discrete and continuous parameters that were potentially interdependent. Several optimization runs guided by different meta-heuristics were executed and with a restricted computational budget, promising parameter configuration ranges were identified. This publication focused on examining the results of different optimization runs. For this purpose, the numbers of QCs, YTs, and YBs were modified during different experiments. At the same time, different dispatching policies, as well as QC-YB assignment policies, were investigated. Furthermore, different allocation policies of YBs were applied. In this study, the approximated optima suggest that the pooling of YTs was preferable to free allocation. Furthermore, a YB assignment close to the QCs was considered better than a random one. By choosing more QCs, the number of bays to be served per QC decreased. Thus, a reduction of the makespan could be achieved. A doubling of the number of QCs from 3 to 6 led to a reduction of the makespan by \(57\%\). Due to the NFLT, it is not clear whether these empirical results can be generalized to future studies that use simulation-based optimization. The applicability of meta-heuristics such as TPE or BO needs to be demonstrated by further optimization studies, potentially with various simulation models, different objective functions, and additional meta-heuristics or different fine-tuning of the same meta-heuristic for comparison. Conceptualization, M.K., A.S., C.J., and N.N. ; methodology, M.K., A.S., and N.N. ; software, M.K., A.S., and N.N. ; validation, M.K. ; formal analysis, M.K. ; investigation, N.N.,M.K. ; resources, N.N. ; data curation, M.K. ; writing--original draft preparation, M.K., A.S., and N.N. ; writing--review and editing, A.S., C.J. ; visualization, N.N., M.K. ; supervision, C.J. ; project administration, M.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The data presented in this study are openly available in zenodo at 10.5281/zenodo.4473251. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: