## 2 Literature Review
Next, the part projection on the printer platform is selected in the second  
Figure 1: Additive manufacturing (AM) technologies: (**a**) material extrusion (ME), (**b**) stereolithography (SLA), and (**c**) selective laser sintering (SLS) processes. Figure 2: Production and distribution levels in an AM supply chain. stage. [PERSON] et al. [29] proposed a model for the integrated orientation-optimization problem by using a genetic algorithm to search for an optimal combination of the part build orientation. [PERSON] et al. [11] developed an optimization model for part scheduling to ME printers in a production process. However, existing production-related optimization models only consider a particular AM technology, without explicit consideration of other AM printers' characteristics as a whole. Various researchers have investigated the decentralization of the distribution operations for the spare parts industry (e.g., [30; 31; 32; 33]). For example, [PERSON] and [PERSON] [30] identified the potential of AM applications in the spare parts industry and the need for a fully functional AM supply chain to facilitate the exchange of 3 DP services. [PERSON] et al. [31] described and evaluated potential approaches to introducing AM into the spare parts supply chain. [PERSON] and [PERSON] [32] also explored combinations of digital manufacturing, equipment use, and logistics, and they discussed how these combinations might affect the relationship between users, equipment manufacturers, and logistics service providers. Another line of research involved optimization models with cost- and energy-related studies (e.g., [34; 35; 36]). For example, [PERSON] and [PERSON] [34] discussed several cost and time models for different AM processes, proposing that the efficient use of a 3D printer envelope will reduce the cost of an AM part. A significant efficiency factor also lies in the ability to exhaust the available build space. In addition, [PERSON] et al. [37] proposed a cost-driven design methodology in which a multi-objective optimization problem maximizes the design freedom and minimizes the cost. Despite significant development, no model has yet successfully integrated AM production and distribution problems. The integration aspect of these two problems ensures that the sub-optimality causing inefficient planning can be avoided. The above models also lack the perspective and the involvement of a decision-maker. In particular, gaps in prior work and the corresponding aims of this work are highlighted as follows:  
* Researchers typically considered a single economic objective. However, process planning involves multiple conflicting criteria from different stakeholders. Thus, this work employed a multi-objective technique to account for the diverse requirements needed when process planning in AM. * Existing models generally considered problems at either the production or distribution planning level; the lack of integration between these two problems likely causes sub-optimality for the planners. As such, this work aimed to account for production and distribution planning. * Process planning in AM involves various stakeholders with often conflicting requirements involving trade-offs. Thus, an AHP approach was introduced as the tool used to assess the decision analysis under multiple criteria to analyze decision-makers' preferences under the diverse objectives of the multi-objective optimization model. * Rather than investigating a singular AM technology as prior researchers have tended to do, three AM technologies (i.e., ME, SLA, and SLS technologies) were combined and analyzed to review various aspects of AM; in addition, a case study was used to verify and validate the model. In particular, the energy source used and the part nesting or stacking capability of the build chamber were analyzed.