## 1 Introduction  
Three-dimensional printing (3 DP), also known as additive manufacturing (AM), is the technique of manufacturing objects layer-by-layer from digital data using computer-aided design software [1]. Over the past 29 years, the AM industry's worldwide revenue has been estimated to increase approximately 27% for all products and services, with the AM industry reaching roughly $7.4 billion in 2017 [2]. Unlike traditional manufacturing methods that constrain the geometric design of parts, AM provides more design flexibility. Thus, manufacturers in the industry recently recognized AM as a capable method and began implementing 3D printers in their research and development, design, and production processes. Regardless, practical applications of AM technology pose many difficulties, owing to its quality variation, cost inefficiency, and incompetent process and production planning [3; 4; 5; 6; 7; 8; 9; 10].  
Most AM production schedules are designed by human planners, and many orders to be printed are mainly selected based on the intuition and experience of planners. A lack of systematic planning causes under-utilization, thus further damaging the economic impact of implementing 3 DP within an organization [11; 12]. Thus, the feasibility of the production and process planning of complex AM systems with various printers and diverse process requirements, as well as the throughput and capacity in 3D printers, load balances of printers, production times, and operation costs, must be explored [13; 14]. Logistical considerations should also be considered when planning to account for various stakeholders and to ensure that sub-optimality is avoided [15; 16]. A multi-criteria decision analysis (MCDA) technique was employed in this work to consider several criteria when making decisions and supporting judgments [17]. Common MCDA techniques include the data envelopment analysis, analytic hierarchy process (AHP), analytic network process, a technique for order of preference by similarity to the ideal solution, and multi-objective programming (e.g., [11; 18; 19]).  
In this study, the developed multi-objective optimization model aimed to manage production and distribution problems for AM in an integrated way. Three common AM technologies were considered: material extrusion (ME), stereolithography (SLA), and selective laser sintering (SLS) [2]. The tactical decisions required for scheduling a batch of components to multiple printers were considered, in which printed parts were to be delivered to different customers' locations. An AHP process was also used to evaluate the important criteria related to total operational and transportation costs, load balance, lateness, and unassigned parts. The developed model was further illustrated using a decision-support system (DSS) to support practitioners, which is an important aspect to manage the gap between practitioners and researchers. The developed model was then verified through a case study using automotive and healthcare parts. Finally, regarding the computation time, we conducted designed experiments to analyze the important aspects of the integrated problem for the number of parts, printer types, and distribution locations.  
The remainder of this paper is structured as follows. In Section 2, we provide an overview of the relevant literature. The proposed mathematical model is developed in Section 3. Then, the solution method and designed experiment are deliberated in Sections 4 and 5, respectively. Finally, the research conclusions and suggested future research directions are outlined in Section 6.