# A Simulation-Based Optimization Method for Warehouse Worker Assignment
## 8 Conclusions  
As a real-life complex system, the warehouse is characterized by high variability in workload arrival. Thus, the optimal worker assignment in such a dynamic system is a non-trivial task. Since the workload may vary on a daily basis, it is important for the warehouse planner to have a decision support tool that can optimally assign workers to different activities. As the exact method is impossible for this problem, we proposed the simulation-based optimization method, which combines the O2 DES framework with a random neighborhood search method. Under dynamic daily workload conditions, the random neighborhood search model was shown to efficiently identify an improved manpower assignment strategy based on the simulation output. A decision support tool based on the proposed method was then verified and validated using the real data from a warehouse located in Singapore. The results show that with a slight decrease of the inbound service level, we can improve the outbound service level by more than 30%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sorting** & **GR** & **Putaway** & **VLM** & **Picking** & **AST** & **Scanning** & **Packing** & **Releasing** \\ \hline
6 & 2 & 2 & 1 & 2 & 2 & 2 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The current assignment used in the warehouse.  
Figure 7: Numerical results for July 2018 data, given the total numbers of inbound/outbound pallets of 1656/1529, and the numbers of completed pallets with the current worker allocation are 1231/621.  
This study provides a practical contribution to the problem of workforce planning in a warehouse. Warehouse planners and managers can benefit from the use of such a manpower allocation tool. The managers or users of the tool will need to provide the upcoming workload forecast as the input, while the tool automatically generates the optimal manpower allocation to activities.  
On the theoretical side, although the literature has advocated the need for implementation of strategies for manpower planning [20; 26], the reported solutions have not been numerous. One of the few examples is from [PERSON] et al. [6], who proposed a two-step mixed integer linear programming model which optimizes worker allocation to activities and different shifts daily. Said optimization model does not capture the arrival time of the daily workload, and assumes that the human resources are always sufficient. Hence, such a model is not capable pf effectively accounting for the industry circumstances. In contrast, our simulation-based optimization model accounts for the workload arrival and queuing, and allows one to achieve a higher service level with the available manpower.  
Future studies may further improve the flexibility of the proposed method by integrating a better selection of initial solution to accelerate the neighborhood search. This method can also be applied to other resource allocation problems in supply chains and logistics.  
Conceptualization, H.L. and O.G.; methodology, K.K. and W.Z.; software, H.L. and K.K.; validation, O.G.; resources, H.L.; data curation, O.G.; writing-original draft preparation, O.G., K.K., and W.Z.; writing-review and editing, O.G. and K.K.; visualization, W.Z. and O.G. All authors have read and agreed to the published version of the manuscript.  
This research has been made possible by the funding support from the Centre for Next Generation of Logistics (C4 NGL), Department of Industrial Systems Engineering and Management, Faculty of Engineering, National University of Singapore. The work has been also supported by Singapore A*STAR IAF-PP fund (Grant No. A1895a0033) under the project \"Digital Twin for Next Generation Warehouse\". The authors appreciate our researchers for their great contributions to the centre and projects.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.