Research Papers

Prioritizing Design for Environment Strategies Using a Stochastic Analytic Hierarchy Process

[+] Author and Article Information
Devarajan Ramanujan

School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: dramanuj@purdue.edu

William Z. Bernstein, Karthik Ramani

School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907

Jun-Ki Choi

Department of Mechanical and
Aerospace Engineering,
University of Dayton,
Dayton, OH 45469

Mikko Koho

Department of Production Engineering,
Tampere University of Technology,
Tampere FI-33720, Finland

Fu Zhao

School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47909

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 18, 2012; final manuscript received October 8, 2013; published online April 28, 2014. Assoc. Editor: Jonathan Cagan.

J. Mech. Des 136(7), 071002 (Apr 28, 2014) (10 pages) Paper No: MD-12-1120; doi: 10.1115/1.4025701 History: Received February 18, 2012; Revised October 08, 2013

This paper describes a framework for applying design for environment (DfE) within an industry setting. Our aim is to couple implicit design knowledge such as redesign/process constraints with quantitative measures of environmental performance to enable informed decision making. We do so by integrating life cycle assessment (LCA) and multicriteria decision analysis (MCDA). Specifically, the analytic hierarchy process (AHP) is used for prioritizing various levels of DfE strategies. The AHP network is formulated so as to improve the environmental performance of a product while considering business-related performance. Moreover, in a realistic industry setting, the onus of decision making often rests with a group, rather than an individual decision maker (DM). While conducting independent evaluations, experts often do not perfectly agree and no individual expert can be considered representative of the ground truth. Hence, we integrate a stochastic simulation module within the MCDA for assessing the variability in preferences among DMs. This variability in judgments is used as a metric for quantifying judgment reliability. A sensitivity analysis is also incorporated to explore the dependence of decisions on specific input preferences. Finally, the paper discusses the results of applying the proposed framework in a real-world case.

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Fig. 1

Schematic diagram of the proposed framework for integrating an sAHP based MCDA with a traditional LCA

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Fig. 3

Structure of the pairwise comparison matrix of a deterministic AHP and the proposed sAHP

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Fig. 5

Structure of the overall AHP network used for prioritization of DfE strategies

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Fig. 6

A snapshot of example results from the sAHP framework

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Fig. 7

Comparison of the normalized preference values of the sAHP with the deterministic AHP

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Fig. 9

Sensitivity of alternatives for an example sAHP input

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Fig. 10

Recommendations for adopting LCA strategies based on DfE rankings

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Fig. 8

Results of the hypothesis testing

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Fig. 4

Figure outlining the significance of use and maintenance phase in the LCA of “Product 1”

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Fig. 2

List of DfE strategies in a typical product life cycle [8]




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