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The Earned Value Management (EVM) has been extensively employed in the literature for analysing the schedule and cost performance indexes. However, the effects of risk factors on the project success have been previously ignored in the project management conventional context. In this paper, a well-organized project control and monitoring system is developed by incorporating the EVM basic principles, risk analysis, and utility theory for improving the performance of manufacturing systems. Weight values corresponding to the schedule performance index (SPI), the cost performance index (CPI), and the risk performance index (RPI) are calculated based on expert judgments using Z-number and Analytic Hierarchy Process (Z-AHP). Finally, a Multi-Attribute Utility Theory (MAUT) and Multi-Objective Linear Programming (MOLP) under fuzzy condition are utilized to demonstrate the applicability of the proposed approach. Sensitivity analysis indicated the risk performance is the most sensitive when compared with the schedule and the cost index. The approach given in this paper can be further employed by both academicians and managers in heavy intensive manufacturing systems.

Manufacturing Performance Measurement Using Fuzzy Multi-Attribute Utility Theory and Z-Number