Modeling Uncertainty in Through-Life Costing at the Early Design Stages: I

Saravi, Mohammad Ebrahimi; Goh, Yee Mey; Newnes, Linda; Mileham, A R; Morton, Karen; Beedall, Duncan

doi:10.1115/detc2011-47863

Cited by 3 publications

(2 citation statements)

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“…The difference between the information required to accomplish a task and the information currently residing with the actor charged with performing it Suss and Thompson (2012) 4 Lack of definition, lack of knowledge and lack of trust in knowledge Wynn et al 2011; Afshari and Peng (2015b) In the PD research, uncertainties are classified with different perspectives. One of the common classifications is to divide uncertainty into Aleatoric and Epistemic (Engelhardt et al, 2011;Saravi et al, 2011). The aleatoric uncertainty stems from the stochastic effects such as the random noise and measurement error.…”

Section: Effects Of Uncertainties On Product and System Designmentioning

confidence: 99%

Design Optimization for Sustainable Products Under Users' Preference Changes

Afshari

Peng

2016

Journal of Computing and Information Science in Engineering

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Decisions made in the early design phase enormously contribute to the performance of a product during its life cycle. Since users' preferences may change over time, a product design should be revised under the preference change. Providing accurate data for designers ensures an optimal decision for product design; this research presents a new method to assess effects of the quantified changes on product cost and development time. In addition, two models to optimize design under unexpected disturbances are proposed. Normally, optimal parameters require several search iterations in design process before finalizing a product. The design time in terms of number of iterations can be reduced by adding resources in each iteration using modern control engineering methods. However, adding resources will increase the design cost. The proposed method in this research minimizes the total product design cost and environmental impacts under changes of users' preferences. The method is validated using an example of the smartphone design. The research novelty is a method of applying quantified changes of external disturbances (such as changes in users' preferences) in the design process, addressing a real problem in industry, and proposing optimal models of products for reduced cost and environmental impacts.

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Section: Effects Of Uncertainties On Product and System Designmentioning

confidence: 99%

Design Optimization for Sustainable Products Under Users' Preference Changes

Afshari

Peng

2016

Journal of Computing and Information Science in Engineering

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“…In contrast to determinism, uncertainty may indicate probability, likelihood, or frequency of occurrence [4]. In one classification, uncertainty can be considered aleatory and epistemic [5]. Aleatory uncertainty is caused by intrinsic variability, whereas epistemic uncertainty results from a lack of knowledge [6].…”

Section: Introductionmentioning

confidence: 99%

Techno-Economic and Life Cycle Cost Analysis through the Lens of Uncertainty: A Scoping Review

Barahmand

Eikeland

2022

Sustainability

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Researchers have long been interested in developing new economic assessment methods to provide credible information and facilitate the sustainable development of new technologies and products. The techno-economic analysis (TEA) and the life cycle cost analysis (LCCA) are the most widely used approaches for modeling and calculating processes’ economic impacts. A simulation-based TEA is a cost-benefit analysis that simultaneously considers technical and economic factors. In addition, the method facilitates the development of the entire project and provides a systematic approach for examining the interrelationships between economic and technological aspects. When it comes to economic studies, it is intimately bonded with uncertainty. There are numerous uncertainty sources, classified in various ways. The uncertainty reflects “an inability to determine the precise value of one or more parameters affecting a system.” The variability refers to the different values a given parameter may take. This implies that a probability density function (PDF), for instance, can be employed to estimate and quantify the variability of a given parameter. The bias refers to “assumptions that skew an analysis in a certain direction while ignoring other legitimate alternatives, factors, or data.” The present study identifies the frequency with which TEA/LCCA studies address uncertainty and gaps within the selected papers through a scoping review. The results indicate that the uncertainty associated with economic factors and model uncertainties were the main sources of uncertainty in TEA and LCCA. Moreover, possibilistic approaches such as the Monte Carlo methodology were the most frequently used tool to cope with the uncertainties associated with LCCA and TEA.

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Improving the Resilience of Energy Flow Exchanges in Eco-Industrial Parks: Optimization Under Uncertainty

Afshari

Farel²,

Peng

2017

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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Eco-Industrial parks (EIPs) and industrial symbioses (IS) provide cost-effective and environmental friendly solutions for industries. They bring benefits from industrial plants to industrial parks and neighborhood areas. The exchange of materials, water, and energy is the goal of IS to reduce wastes, by-products, and energy consumption among a cluster of industries. However, although the IS design looks for the best set of flow exchanges among industries at a network level, the lack of access to accurate data challenges the optimal design of a new EIP. IS solutions face uncertainties. Considering the huge cost and long establishment time of IS, the existing studies cannot provide a robust model to investigate effects of uncertainty on the optimal symbioses design. This paper introduces a framework to investigate uncertainties in the EIP design. A multi-objective model is proposed to decide the optimal network of symbiotic exchanges among firms. The model minimizes the costs of multiple product exchanges and environmental impacts of flow exchanges. Moreover, this paper integrates the analysis of uncertainties effects on synergies into the modeling process. The presented models are depicted through optimizing energy synergies of an industrial zone in France. The efficiency of single and multiple objective models is analyzed for the effects of the identified uncertainties. In addition, the presented deterministic and robust models are compared to investigate how the uncertainties affect the performance and configuration of an optimal network. It is believed that the models could improve an EIP's resilience under uncertainties.

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Modeling Uncertainty in Through-Life Costing at the Early Design Stages: I

Cited by 3 publications

References 0 publications

Design Optimization for Sustainable Products Under Users' Preference Changes

Design Optimization for Sustainable Products Under Users' Preference Changes

Techno-Economic and Life Cycle Cost Analysis through the Lens of Uncertainty: A Scoping Review

Improving the Resilience of Energy Flow Exchanges in Eco-Industrial Parks: Optimization Under Uncertainty

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