To efficiently develop an automatic assembly system that can enhance the quality and assembly productivity of the shaft assembly in EV relays, a DMU model was utilized. After modeling each component of the assembly system using the CAD software CATIA, a DMU model of the assembly cells and the entire assembly system was created using the assembly model. Additionally, the DMU Kinematics Workbench was employed to verify and validate the design of the automatic assembly system for the shaft assembly, a key component of the EV relay, before actual construction. This approach helped reduce time and costs by minimizing trial and error.

본 연구에서는 해양플랜트 산업의 가치사슬 및 수명주기 연구를 통해 해양 자원개발 비즈니스의 포괄적인 형상을 파악하였고 해양프로젝트 검증 목적의 시뮬레이션을 위해 조립 및 인간공학 시뮬레이션에 대한 연구를 수행하였다. 구체적으로는 조립 시뮬레이션의 경우 드릴쉽을 대상으로 탑재공정에 대한 조립 시뮬레이션을 통해 공정에 대한 유효성 검증을 수행할 수 있었고, 인간공학 시뮬레이션의 경우 FPSO 플랫폼을 대상으로 작업자 시뮬레이션을 통해 작업환경에서의 문제점을 사전에 도출할 수 있었다.

  This study proposes a new approach which combines Data Envelopment Analysis (DEA) and the Analytic Hierarchy Process (AHP) techniques to effectively evaluate Decision Making Units (DMUs). While DEA evaluates a quantitative data set, employs linear progr

This study proposes a new approach which combines Data Envelopment Analysis (DEA) and the Analytic Hierarchy Process (AHP) techniques to effectively evaluate Decision Making Units (DMUs). While DEA evaluates a quantitative data set, employs linear programming to obtain input and output weights and ranks the performance of DMUs, AHP evaluates the qualitative data retrieved from expert opinions and other managerial information in specifying weights. The objective of this research is to design a decision support process for managers to incorporate positive aspects of DEA's absolute numerical evaluations and AHP's human preference structure values. It is believed that a pragmatic manager will be more receptive to the results that include subjective opinions incorporated into the evaluation of the efficiency of each DMU efficiency when the AHP and DEA are combined simultaneously. The WPDEA method provides better discrimination than the DEA method by reducing the number of efficient units.