## 6 Conclusions and Future Research  
AM, also known as 3 DP, became an essential method that is capable of streamlining and expediting product development, making prototypes, and fabricating tooling components and final production parts, as well as reducing the time to market, improving product quality, and reducing costs. Aspects requiring further research include process planning and supply chain design, especially printer selection, part orientation, and part-to-printer scheduling. In this study, an integrative multi-objective optimization model and AHP technique were introduced for integrated production and distribution problems in AM. The developed multi-objective optimization model reflected diverse requirements by considering the operating and distribution costs, the load balance of printers, delays, and the remaining parts not assigned and distributed. The AHP was employed to evaluate the objective weights for the non-preemptive approach of the multi-objective solution. The developed model employed ME, SL, and SLS processes; technical aspects were evaluated for printer-time approximation and part-nesting requirements. The model was verified and validated using selected automotive and healthcare components, and the relevant data for parts, printers, and distribution locations were collected to test the model functionalities. The designed experiment for the computation time was evaluated based on three key factors: the number of parts, printers, and distribution locations, each with two levels. Two criteria weight settings, with equal-weight consideration and with the employed AHP method, were also used to analyze the model in this study. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Factors/Levels** & \(\mathbf{Z_{1}}\) & \(\mathbf{Z_{2}}\) & \(\mathbf{Z_{3}}\) & \(\mathbf{Z_{4}}\) & **Multi-Objective** \\ \hline \multirow{2}{*}{Printers} & 5 & 0.79 & 15.33 & 1818.22 & 1.24 & 1942.39 \\  & 10 & 4.01 & 84.88 & 3080.53 & 310.03 & 3420.42 \\ \hline \multirow{2}{*}{Parts} & 100 & 1.08 & 2.60 & 1298.75 & 183.85 & 1782.87 \\  & 500 & 3.72 & 97.61 & 3600 & 127.43 & 3579.94 \\ \hline \multirow{2}{*}{Destinations} & 5 & 2.52 & 4.28 & 2612.64 & 4.02 & 2644.53 \\  & 10 & 2.28 & 95.93 & 2286.11 & 307.26 & 2718.28 \\ \hline \multirow{2}{*}{Grand average} & & 2.40 & 50.11 & 2449.38 & 155.64 & 2681.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Aggregated computation time for a designed experiment (seconds). The results indicated the presence of trade-offs among conflicting criteria of the proposed model. A decision-support tool for AM planning was developed using Excel to aid practitioners not familiar with AHP mathematical notations or the optimization approach. The development of the decision-support tool is needed to reduce the gap between researchers and practitioners in AM. This study provided a practical analysis of the integrated production and distribution problems for AM. As the AM supply chain becomes more complex and involves shared AM printers in a business platform, evaluating how a business can improve its production and process planning to improve the capacity, revenue, and management practices is important. Directions for future research should include evaluating other solution methods in the realm of metaheuristics to improve the computation time due to the NP-hard complexity of the model for practical, effective solutions. Although the illustrated case study was solved in a few seconds, a relatively low number of parts, printers, and delivery locations were included; the computation time will increase as more variables are included. Additionally, this study focused on AM; however, production facilities may aim to use both AM printers and traditional manufacturing equipment to fabricate a variety of parts. Thus, enhancing the scheduling system to incorporate both types of technologies should also be a subject of future study. Lastly, to simplify the complexity of the developed mathematical model, associated assumptions are needed.