Material and process selection in product design using decision-making technique (AHP)

Desai, Salil; Bidanda, Bopaya; Lovell, Michael R.

doi:10.1504/ejie.2012.046666

Cited by 34 publications

(15 citation statements)

References 22 publications

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“…AHP is a reliable method for calculating the weight of each criterion based on the decision-maker's viewpoint. A unique feature of AHP is the possibility of calculating the compatibility/incompatibility of decisions made by the decision maker (Desai et al, 2012). Pairwise comparisons are carried out using Saaty's 9-point preference scale.…”

Section: Analytic Hierarchy Process (Ahp) Methodsmentioning

confidence: 99%

Comparative Analysis of Risk Management Strategies for Additive Manufacturing Supply Chains

Altubaishe¹,

Clarke²,

McWilliams³

et al. 2019

American Journal of Applied Sciences

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Supplier selection is an important strategic decision to be made in additive manufacturing supply chains. This paper presents a hybrid Analytic Hierarchy Process-Preference Ranking Organization Method for Enrichment of Evaluations (AHP-PROMETHEE) algorithms-based Failure Mode and Effects Analysis (FMEA) for supplier selection in a risk-based environment. Our approach is contrasted with an FMEA based on a Fuzzy Analytic Hierarchy Process (FAHP) for modeling assessment and mitigation of risk. In the FAHP method, the selection of the best supplier is based on the value of the weighted RPN whereas, in the hybrid AHP-PROMETHEE method, the selection of the best supplier is based on the value of outranking. An illustrative case study is used for a comparative analysis between the two methods. The AHP-PROMETHEE algorithmsbased FMEA was the preferred method based on ease of implementation, accuracy of results and higher decision-maker involvement as compared to the FAHP method. This research develops a hybrid AHP-PROMETHEE algorithms-based FMEA to incorporate risk criteria toward systematic evaluation of suppliers for supply chain management.

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Section: Analytic Hierarchy Process (Ahp) Methodsmentioning

confidence: 99%

Comparative Analysis of Risk Management Strategies for Additive Manufacturing Supply Chains

Altubaishe¹,

Clarke²,

McWilliams³

et al. 2019

American Journal of Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other methods could also be allowed to handle this multicriteria approach, for example, the ELECTRE methods (Bouyssou et al, 2006), the Analytical Hierarchy Process (Desai, Bidanda, & Lovell, 2012;Saaty, 2013), MACBETH, or Measuring Attractiveness Through a Categorical-Based Evaluation Technique (Cliville, Berrah, & Mauris, 2007;Costa, De Corte, & Vansnick, 2012), and TOPSIS, or Technique for Order of Preference by Similarity to Ideal Solution (Kaya & Kahraman, 2011;Sakthivel, Ilangkumaran, Nagarajan, & Shanmugam, 2013), to name some. However, these other methods do not clearly state the advantages aforementioned, and the AQ is difficult to interpret.…”

Section: Multicriteria Analysismentioning

confidence: 99%

Risk-based ROI, capital budgeting, and portfolio optimization in the Department of Defense

Mun

2020

Defense ARJ

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This article shows a reusable, extensible, adaptable, and comprehensive advanced analytical modeling process to help the U.S. Department of Defense (DoD) with risk-based capital budgeting and optimizing of acquisition and program portfolios with multiple stakeholders while subject to budgetary, risk, schedule, and strategic constraints. The article covers traditional capital budgeting methodologies in industry and explains how these methods can be applied in the DoD by using DoD-centric, noneconomic, logistic, readiness, capabilities, and requirements variables. Portfolio optimization for the purposes of selecting the best combination of programs and capabilities is also addressed, as are alternative methods such as average ranking, risk metrics, lexicographic methods, PROMETHEE, ELECTRE, and others. Finally, an illustration from the Program Executive Office Integrated Warfare Systems (PEO-IWS) and Naval Sea Systems Command (NAVSEA) showcases the methodology’s application in developing a comprehensive and analytically robust case study that senior leadership at the DoD may utilize to make optimal decisions.

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“…All the literature discussed above focused on the integrated approach with more emphasis on modification of DFM for AM and using a combination of the design criteria (e.g., function, cost and environment) and the DFM/DfAM guidelines for successful generation and utilization of the design requirements and attributes. In case of MPS problem which is also an integral decision-making aspect of DFM itself, a lot of work has been done on traditional domain with researches involving cost per unit property methods [35] , material and process selection charts [36] , casebased reasoning [37] , material selection programs [38] , knowledgebased systems [39] , AHP [40] , Technique of Ranking Preferences by Similarity to Ideal Solution (TOPSIS) [41] , and ELECTRE III [42] , but very little in the AM area. For example, the AHP was used by Mancanares et al [43] to select AM processes based on the requirements generated from a part.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated product-process design: Material and manufacturing process selection for additive manufacturing using multi-criteria decision making

Zaman

Rivette

Siadat

et al. 2018

Robotics and Computer-Integrated Manufacturing

111

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Material and process selection in product design using decision-making technique (AHP)

Cited by 34 publications

References 22 publications

Comparative Analysis of Risk Management Strategies for Additive Manufacturing Supply Chains

Comparative Analysis of Risk Management Strategies for Additive Manufacturing Supply Chains

Risk-based ROI, capital budgeting, and portfolio optimization in the Department of Defense

Integrated product-process design: Material and manufacturing process selection for additive manufacturing using multi-criteria decision making

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