Data Envelopment Analysis Journal > Vol 1 > Issue 2

Engineering Design and Efficiency Measurement: Issues and Future Research Opportunities

Konstantinos P. Triantis, Grado Department of Industrial and Systems Engineering, Virginia Tech, System Performance Laboratory, Northern Virginia Center, USA,
Suggested Citation
Konstantinos P. Triantis (2015), "Engineering Design and Efficiency Measurement: Issues and Future Research Opportunities", Data Envelopment Analysis Journal: Vol. 1: No. 2, pp 81-112.

Publication Date: 30 Jul 2015
© 2015 K. P. Triantis
Business Process Engineering and Design,  Optimization
Data envelopment analysisEngineering designInfrastructure system resilience and sustainabilityMicro-process representationsMessy dataDynamical systems


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In this article:
1. Introduction, Objective, and Context 
2. Engineering Systems Design and Efficiency Performance Measurement 
3. Efficiency Measurement for the Resilience and Sustainability of Engineered Infrastructure Systems 
4. Opening the Engineering Black Box-Physical and Behavioral Considerations: The Mapping of the Virtual to the Engineering Design World 
5. Messy Data that Inform Efficiency Performance Measurement and Engineering Design Decisions 
6. Capturing the Dynamic Characteristics of Engineered Systems 
7. Two Illustrations 
8. Conclusions and Future Research Directions 


The objective of this paper is to discuss unresolved research issues when linking the literatures on efficiency measurement and the design and operation of engineered systems. I focus on five themes that have emerged in the last decade. These include: engineering system design, resilience and sustainability of infrastructure systems, physical and behavioral representations of disaggregated subsystems, messy data, and the dynamic characteristics of engineered systems. I also provide an initial conceptualization of key yet interrelated issues within each of the themes. Along with the themes I suggest existing modeling formulations in the efficiency measurement literature (DEA, network DEA, fuzzy clustering, and the Dynamic Productive Efficiency Model) that serve as an initial starting point for modeling the efficiency performance of engineered systems. As illustrations, I present two examples, one from infrastructure management and one from transportation engineering.