Probability of User Fit for Spatially Optimized Products

2011 ◽  
Author(s):  
Christopher Garneau ◽  
Matthew Parkinson
Keyword(s):  
Decision Making ◽  
Design Space ◽  
Cost Effective ◽  
Target Population ◽  
Product Performance ◽  
User Preference ◽  
Body Dimensions ◽  
Performance Metric ◽  
Target User ◽  
Parametric Studies

This study offers a new method for understanding the likelihood of acceptable fit for users of adjustable products and environments and is a useful tool for aiding the designer in making decisions about problems involving human variability. Accommodation, which describes the ability of a user to interact with a device or environment in a preferred way, is a key product performance metric. Methods that offer a better understanding of accommodation of broad user populations would allow for the design of products that are more cost-effective, safer, and/or lead to greater levels of customer satisfaction. This work uses parametric studies to explore the characteristics of a target user population and the probability of accommodating individuals of a given body size. Performance regions are identified in both the problem’s design space (the product dimensions under consideration) and the anthropometry space of the target population (the relevant body dimensions of product users). The existence of probability contours is a result of outcome uncertainty due to anthropometry-independent user preference, and the analysis is achieved by assessing binary accommodation of individuals using a “virtual fit” method with many iterations. Two case studies, one univariate and one bivariate in both performance and anthropometry spaces, are presented. An important outcome of the decision making framework described in this work is the ability to intuitively gauge who in the population of target users will be disaccommodated by a design and how to improve overall accommodation.

Visual Analysis of User Accommodation

2010 ◽  
Author(s):  
Christopher J. Garneau ◽  
Matthew B. Parkinson
Keyword(s):  
Visual Analysis ◽  
Cost Effective ◽  
Complex Problem ◽  
User Preference ◽  
Design Decision ◽  
Seat Height ◽  
Performance Metric ◽  
Target User ◽  
Anthropometric Variability

This study presents a novel, quantitative tool for design decision-making for products designed for human variability. Accommodation, which describes the ability of a user to interact with a device or environment in a preferred way, is a key product performance metric. Methods that offer a better understanding of accommodation of broad user populations would allow for the design of products that are more cost-effective, safer, and/or lead to greater levels of customer satisfaction. Target user populations are often characterized by measures of anthropometry, or body dimensions. A methodology is proposed that uses a visual analysis method for understanding and exploring accommodation across the variability in anthropometry of a target user population. This is achieved by assessing binary accommodation of individuals using a “virtual fit” method and examining trends in binary accommodation across the range of anthropometric variability, referred to as the “anthropometry space”. Various factors influencing accommodation, such as user preference independent of anthropometry and the quality of a design, are also discussed and are an important contribution of the work. Two demonstration studies are presented that illustrate the methodology and provide opportunity for discussion of its impact. The first study investigates the simple univariate problem of dimensionally optimizing the seat height and range of adjustability of an exercise cycle. The second study investigates the more complex problem of optimally configuring the driver package of a commercial truck.

Including Preference in Anthropometry-Driven Models for Design

2009 ◽  
Vol 131 (10) ◽  
Author(s):  
Christopher J. Garneau ◽  
Matthew B. Parkinson
Keyword(s):  
Cost Effective ◽  
Residual Variance ◽  
Design Decision ◽  
Body Dimensions ◽  
Target User ◽  
Simulation Based ◽  
Spatial Dimensions ◽  
Similar Body ◽  
Automation Tools ◽  
Anthropometric Variability

In the design of artifacts that interact with people, the spatial dimensions of the target user population are often used to determine the requirements of the engineered artifact. The expected variability in body dimensions (called “anthropometry”) is used to indicate how much adjustability or how many sizes are required to accommodate the intended user population. However, the quantification of anthropometric variability alone is not sufficient to make these kinds of assessments in many situations. For example, two vehicle drivers with similar body dimensions might have different preferred locations for the seat. In these situations, preference can be broken down into two components: that explained by body size and the variability that remains. By quantifying the magnitude of both sources, preference can be included in modeling strategies and design decision-making. This improves the accuracy of models and predictions, and can facilitate the application of design automation tools such as optimization and robust design methodologies, resulting in products that are safer, cost effective, and more accessible to broader populations (including people with disabilities). In contrast, failure to include variability in preference that is not attributable to anthropometry can produce misleading results that under- or over-approximate accommodation and prescribe inappropriate amounts of adjustability. A simulation-based approach for modeling both sources of variability and conducting designing for human variability (DfHV) assessments is presented. A stochastic component based on the residual variance in regression analysis relating body dimensions to experimental data is included in the predictive model. This ensures that a distribution of preferred configurations is produced for any given set of body dimensions. The effect of including both components of preference is quantified by comparing this approach to two traditional DfHV approaches in the context of a simple, univariate case study to determine the appropriate allocation of adjustability to achieve a desired accommodation level.

scholarly journals Bayesian maintenance decision optimisation based on computing the information value from condition inspections

2021 ◽  
pp. 1748006X2097812
Author(s):  
Guang Zou ◽  
Kian Banisoleiman ◽  
Arturo González
Keyword(s):  
Decision Making ◽  
Structural Integrity ◽  
Fatigue Cracking ◽  
Cost Effective ◽  
Offshore Structures ◽  
Information Value ◽  
Bayesian Decision ◽  
Maintenance Strategies ◽  
Optimal Maintenance ◽  
Maintenance Decision

A challenge in marine and offshore engineering is structural integrity management (SIM) of assets such as ships, offshore structures, mooring systems, etc. Due to harsh marine environments, fatigue cracking and corrosion present persistent threats to structural integrity. SIM for such assets is complicated because of a very large number of rewelded plates and joints, for which condition inspections and maintenance are difficult and expensive tasks. Marine SIM needs to take into account uncertainty in material properties, loading characteristics, fatigue models, detection capacities of inspection methods, etc. Optimising inspection and maintenance strategies under uncertainty is therefore vital for effective SIM and cost reductions. This paper proposes a value of information (VoI) computation and Bayesian decision optimisation (BDO) approach to optimal maintenance planning of typical fatigue-prone structural systems under uncertainty. It is shown that the approach can yield optimal maintenance strategies reliably in various maintenance decision making problems or contexts, which are characterized by different cost ratios. It is also shown that there are decision making contexts where inspection information doesn’t add value, and condition based maintenance (CBM) is not cost-effective. The CBM strategy is optimal only in the decision making contexts where VoI > 0. The proposed approach overcomes the limitation of CBM strategy and highlights the importance of VoI computation (to confirm VoI > 0) before adopting inspections and CBM.

scholarly journals Public Officials’ Knowledge of Advances and Gaps for Implementing the Ecosystem Approach to Fisheries in Chile

2021 ◽  
Vol 13 (5) ◽  
pp. 2703
Author(s):  
Rodrigo A. Estévez ◽  
Stefan Gelcich
Keyword(s):  
Decision Making ◽  
Fisheries Management ◽  
Cost Effective ◽  
Ecosystem Approach ◽  
Social Dimensions ◽  
Public Officials ◽  
Government Officials ◽  
Effective Implementation ◽  
Mandatory Requirement ◽  
Support Decision Making

The United Nations calls on the international community to implement an ecosystem approach to fisheries (EAF) that considers the complex interrelationships between fisheries and marine and coastal ecosystems, including social and economic dimensions. However, countries experience significant national challenges for the application of the EAF. In this article, we used public officials’ knowledge to understand advances, gaps, and priorities for the implementation of the EAF in Chile. For this, we relied on the valuable information held by fisheries managers and government officials to support decision-making. In Chile, the EAF was established as a mandatory requirement for fisheries management in 2013. Key positive aspects include the promotion of fishers’ participation in inter-sectorial Management Committees to administrate fisheries and the regulation of bycatch and trawling on seamounts. Likewise, Scientific Committees formal roles in management allow the participation of scientists by setting catch limits for each fishery. However, important gaps were also identified. Officials highlighted serious difficulties to integrate social dimensions in fisheries management, and low effective coordination among the institutions to implement the EAF. We concluded that establishing clear protocols to systematize and generate formal instances to build upon government officials’ knowledge seems a clear and cost effective way to advance in the effective implementation of the EAF.

scholarly journals Design space exploration for flexibility assessment and decision making support in integrated industrial building design

2021 ◽  
Author(s):  
Julia Reisinger ◽  
Maximilian Knoll ◽  
Iva Kovacic
Keyword(s):  
Decision Making ◽  
Design Process ◽  
Design Space Exploration ◽  
Design Space ◽  
Parametric Design ◽  
Space Exploration ◽  
Building Design ◽  
Assessment Tools ◽  
Industrial Building ◽  
Industrial Buildings

AbstractIndustrial buildings play a major role in sustainable development, producing and expending a significant amount of resources, energy and waste. Due to product individualization and accelerating technological advances in manufacturing, industrial buildings strive for highly flexible building structures to accommodate constantly evolving production processes. However, common sustainability assessment tools do not respect flexibility metrics and manufacturing and building design processes run sequentially, neglecting discipline-specific interaction, leading to inflexible solutions. In integrated industrial building design (IIBD), incorporating manufacturing and building disciplines simultaneously, design teams are faced with the choice of multiple conflicting criteria and complex design decisions, opening up a huge design space. To address these issues, this paper presents a parametric design process for efficient design space exploration in IIBD. A state-of-the-art survey and multiple case study are conducted to define four novel flexibility metrics and to develop a unified design space, respecting both building and manufacturing requirements. Based on these results, a parametric design process for automated structural optimization and quantitative flexibility assessment is developed, guiding the decision-making process towards increased sustainability. The proposed framework is tested on a pilot-project of a food and hygiene production, evaluating the design space representation and validating the flexibility metrics. Results confirmed the efficiency of the process that an evolutionary multi-objective optimization algorithm can be implemented in future research to enable multidisciplinary design optimization for flexible industrial building solutions.

scholarly journals Development of Nonlinear Finite Element Models of Mortar-Free Interlocked Single Block Column Subjected to Lateral Loading

2021 ◽  
Author(s):  
Furqan Qamar ◽  
Shunde Qin
Keyword(s):  
Finite Element ◽  
Failure Load ◽  
Cost Effective ◽  
Nonlinear Finite Element ◽  
World Population ◽  
Bond Failure ◽  
Lateral Resistance ◽  
Lateral Strength ◽  
Parametric Studies ◽  
Single Block

AbstractAround the globe, the need for additional housing, due to the increase in world population, has led to the exploration of more cost effective and environmentally friendly forms of construction. Out of many technologies found, mortar-free interlocked masonry systems were developed to eliminate the deficiency of traditional masonry. For such systems against earthquakes, lateral resistance can be enhanced with plaster. But there is a need to further improve the performance of plaster in mortar-free interlocking walls for better ductility. The objective of this study is to develop nonlinear finite element (NLFE) models to explore the likely failure mechanism (e.g. bond failure) of such systems and to do parametric studies more cheaply than constructing many walls. Lateral failure load, load–displacement curves and crack patterns were compared with the experimental results. Parametric studies involving variation in block and plaster compressive strength and plaster thickness were undertaken using TNO DIANA NLFE models. A 150% increase in thickness of plaster only resulted in 28% increase in failure load, and column thickness can be reduced to theoretical 25 mm of blocks with 8 mm of plaster and yet exceed the lateral strength of a 150-mm-thick unplastered column. A cost analysis was also carried out, based on NLFE models, and showed that fibrous plastered column with 25-mm-thickness blocks gave equivalent performance to the 150-mm-thick unplastered column with 67% cost saving.

scholarly journals Economic Development Based on a Mathematical Model: An Optimal Solution Method for the Fuel Supply of International Road Transport Activity

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2963
Author(s):  
Melinda Timea Fülöp ◽  
Miklós Gubán ◽  
György Kovács ◽  
Mihály Avornicului
Keyword(s):  
Decision Making ◽  
Ad Hoc ◽  
Market Competition ◽  
Optimal Solution ◽  
Cost Savings ◽  
Cost Effective ◽  
Optimization Method ◽  
Optimal Decision ◽  
Environmental Damage ◽  
Fuel Cost

Due to globalization and increased market competition, forwarding companies must focus on the optimization of their international transport activities and on cost reduction. The minimization of the amount and cost of fuel results in increased competition and profitability of the companies as well as the reduction of environmental damage. Nowadays, these aspects are particularly important. This research aims to develop a new optimization method for road freight transport costs in order to reduce the fuel costs and determine optimal fueling stations and to calculate the optimal quantity of fuel to refill. The mathematical method developed in this research has two phases. In the first phase the optimal, most cost-effective fuel station is determined based on the potential fuel stations. The specific fuel prices differ per fuel station, and the stations are located at different distances from the main transport way. The method developed in this study supports drivers’ decision-making regarding whether to refuel at a farther but cheaper fuel station or at a nearer but more expensive fuel station based on the more economical choice. Thereafter, it is necessary to determine the optimal fuel volume, i.e., the exact volume required including a safe amount to cover stochastic incidents (e.g., road closures). This aspect of the optimization method supports drivers’ optimal decision-making regarding optimal fuel stations and how much fuel to obtain in order to reduce the fuel cost. Therefore, the application of this new method instead of the recently applied ad-hoc individual decision-making of the drivers results in significant fuel cost savings. A case study confirmed the efficiency of the proposed method.

scholarly journals ANN-Based Decision Making in Station Keeping for Geotechnical Drilling Vessel

2021 ◽  
Vol 9 (6) ◽  
pp. 596
Author(s):  
Murugan Ramasamy ◽  
Mohammed Abdul Hannan ◽  
Yaseen Adnan Ahmed ◽  
Arun Kr Dev
Keyword(s):  
Decision Making ◽  
Water Depth ◽  
Business Models ◽  
Cost Benefit ◽  
Cost Effective ◽  
Station Keeping ◽  
Operational Information ◽  
Effective Option ◽  
The Cost ◽  
Cost Factors

Offshore vessels (OVs) often require precise station-keeping and some vessels, for example, vessels involved in geotechnical drilling, generally use Spread Mooring (SM) or Dynamic Positioning (DP) systems. Most of these vessels are equipped with both systems to cover all ranges of water depths. However, determining which system to use for a particular operational scenario depends on many factors and requires significant balancing in terms of cost-benefit. Therefore, this research aims to develop a platform that will determine the cost factors for both the SM and DP station-keeping systems. Operational information and cost data are collected for several field operations, and Artificial Neural Networks (ANN) are trained using those data samples. After that, the trained ANN is used to predict the components of cost for any given environmental situation, fieldwork duration and water depth. Later, the total cost is investigated against water depth for both DP and SM systems to determine the most cost-effective option. The results are validated using two operational scenarios for a specific geotechnical vessel. This decision-making algorithm can be further developed by adding up more operational data for various vessels and can be applied in the development of sustainable decision-making business models for OVs operators.

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