Purpose The main objective of this paper is to illustrate an analytical view of different methods of 3D bioprinting, variations, formulations and characteristics of biomaterials. This review also aims to discover all the areas of applications and scopes of further improvement of 3D bioprinters in this era of the Fourth Industrial Revolution. Design/methodology/approach This paper reviewed a number of papers that carried evaluations of different 3D bioprinting methods with different biomaterials, using different pumps to print 3D scaffolds, living cells, tissue and organs. All the papers and articles are collected from different journals and conference papers from 2014 to 2022. Findings This paper briefly explains how the concept of a 3D bioprinter was developed from a 3D printer and how it affects the biomedical field and helps to recover the lack of organ donors. It also gives a clear explanation of three basic processes and different strategies of these processes and the criteria of biomaterial selection. This paper gives insights into how 3D bioprinters can be assisted with machine learning to increase their scope of application. Research limitations/implications The chosen research approach may limit the generalizability of the research findings. As a result, researchers are encouraged to test the proposed hypotheses further. Practical implications This paper includes implications for developing 3D bioprinters, developing biomaterials and increasing the printability of 3D bioprinters. Originality/value This paper addresses an identified need by investigating how to enable 3D bioprinting performance.

Queueing theory has grown in prominence as it provides the numerical foundation for decision-making assessment. Queueing models with multiple servers provide extensive decision-making data, which is critical for evaluating a server's performance. The purpose of this study is to formulate multiple server queueing models and provides a performance assessment to evaluate the appropriate models. A three-phased structured approach has been used to model and analyze performance for multiple server queueing models. The "Multiple Server Finite Queue Length Infinite Queue Population Model" is most desirable for a customer who has to wait for less time (approximately 43.14%) in the system as well as in the queue (approximately 62.16%) and reduces the system length by approximately 47.02% and the queue length by approximately 64.76%. However, the "Multiple Server Infinite Queue Length Infinite Queue Population Model" is preferable from a managerial perspective, as the "Multiple Server Finite Queue Length Infinite Queue Population Model" has fewer customers in the system, indicating a loss from a managerial standpoint. When the arrival rate of customers, service rate, and the number of servers are increased, length of the system and queue remain nearly constant, whereas the waiting time in the system nearly doubles for both queueing models. The paper develops a performance evaluation of the “Multiple Server Infinite Queue Length Infinite Queue Population Model” and “Multiple Server Finite Queue Length Infinite Queue Population Model”, which are capable of adapting to an unpredictable decision in any service system. Furthermore, this study provides decision-makers with a perspective-based evaluation of the mentioned servers, with a focus on a manager and a customer.