Design Decision-making Research Articles

A Consensus Reaching Process for Product Design Decision-Making by Integrating Intuitionistic Fuzzy Sets and Trust Network

A Consensus Reaching Process for Product Design Decision-Making by Integrating Intuitionistic Fuzzy Sets and Trust Network

Optimization of underground mining production layouts considering geological uncertainty using deep reinforcement learning

Mineral extraction plays a key role in the global raw materials supply chain, however the exhaustion of shallow deposits and typical scarcity of sampled data during exploration activities creates challenges in mine planning and design, where decision-making is highly sensitive to uncertainty in geology and mineral grade prediction. Geostatistical techniques are commonly used to generate a set of equiprobable simulated numerical models to capture these uncertainties, however incorporating these simulated models within a mine planning and design framework remains a major challenge. The purpose of this paper is to propose a novel approach to decision-making in underground mine design that can use information from an ensemble of numerical realizations of a mineral resource to improve the financial performance of the asset. A deep reinforcement learning (DRL) framework, using the proximal policy optimization (PPO) algorithm, is developed for the design of underground mining production level layouts. A case study is presented using a gold mineral resource characterized by an ensemble of 100 numerical realizations to verify the advantages of the proposed method, considering a baseline consisting of an industry standard automated design method. The DRL approach achieved an 8.3% higher expected profit, a 3.4% more gold mined than the baseline, and has the added functionality of considering uncertainty in mineral grades.

Stirring digital innovation in wood waste upcycling: system dynamics as a circular design decision-making methodology

Abstract The urgency to reduce climate-changing emissions and natural resources exploitation is pushing architecture and industrial design to explore viable options to innovate current practices on material use. In forest-based industries, in particular, wood upcycling is emerging as a pivotal investigation field, with a specific research agenda aimed at reducing the impacts generated by the rising demand of timber products and the actual incineration and landfilling rates. Indeed, the development of new technological solutions needs to account for energy and material flows, as well as for the environmental impacts and social and economic dynamics. Such a new complexity pushes towards a design approach that links digital technologies to circularity goals for a more comprehensive understanding of the forest-based resource management and the evalutation of alternative scenarios. In this paper, we propose System Dynamics (SD) as a methodology to provide a qualitative and quantitative framework to promote digital circular innovation in the wood industry. SD is a dynamic, multi-scale, multi-dimension and site-specific decision-making support tool wich helps designers, researchers and industrial stakeholders to understand and simulate the dynamic behavior and performances of complex systems within specific time and space parameters. The proposed SD for wood waste upcycling allows to model “stock and flow” scenarios, to simulate and compare the consequences of technological options on the system and to target their development in time - and space-based projections. This study outlines the first advances for the SD application to the Italian wood industry, and examines the consequences of the application of different circular strategies on a “business-as-usual” scenario.

Evaluate user satisfaction for urban design of railway station areas: An assessment framework using agent-based simulation

AimsRailway station areas can play a crucial role in promoting sustainable development if integrated with cities and be fluctuation-responsive through effective urban design. However, during the design stage, assessing the station areas' performance, of which user satisfaction is indicative, is challenging due to methodological limitations. Agent-based simulation (ABS) is promising as it can link spatial features with agents' behavior features. This research questions to what extent ABS can help assess the urban design of station areas. MethodsThis paper adopts the user pyramid as the theoretical framework, which outlines five types of user needs: safety, speed, ease, comfort, and experience. The paper selects indicators linking satisfaction and spatial features at the district and building levels. These indicators are measured in the simulation of the station system using digital tools, including MassMotion and Python scripts. The theory, indicators, and tools, in combination, serve as an assessment framework. Rotterdam Central Station is used as a case to demonstrate how the framework works. ResultsThe framework is capable of assessing design alternatives by identifying changes in user satisfaction. It can be applied on the district level (at a scale of 250 m) with substantial details to inform design decision-making, and it is useful during the design stage when only limited data is available. This paper strengthens the scientific knowledge of railway station areas through the multidisciplinary literature review that translates user needs for urban design use, and it advances the digital means to visualize user satisfaction affected by design.

Assessing Vulnerabilities in Line Length Parameterization and the Per-Unit-Length Paradigm for Phase Modulation and Figure-of-Merit Evaluation in 60 GHz Liquid Crystal Phase Shifters

The figure-of-merit (FoM) is a crucial metric in evaluating liquid crystal (LC) phase shifters, significantly influencing the selection of superior device candidates. This paper identifies, for the first time, a fundamental limitation in the widely-used High-Frequency Structure Simulator (HFSS), a closed-source commercial tool, when modeling reconfigurable delay line phase shifters (RDLPS) based on LC at millimeter-wave (mmW) frequencies for Beyond 5G (B5G) and Sixth-Generation (6G) applications. Specifically, the study reveals unreliable predictions of differential phase shifts (DPS) when using the line length parameterization (LLP) approach, with an accuracy of only 47.22%. These LLP-induced inaccuracies lead to misleading FoM calculations, potentially skewing comparative analyses against phase shifters implemented with different geometries or advanced technologies. Additionally, the per-unit-length (PUL) paradigm, commonly employed by microwave circuit engineers for evaluating and optimizing microwave transmission line designs, is also found to have limitations in the context of mmW RDLPS based on LC. The PUL methodology underestimates the FoM by 1.38206°/dB for an LC coaxial RDLPS at 60 GHz. These findings underscore a critical symmetry implication, where the assumed symmetry in phase shift response is violated, resulting in inconsistent performance assessments. To address these challenges, a remediation strategy based on a scenario-based “Length-for-π” (LFP) framework is proposed, offering more accurate performance characterization and enabling better-informed decision-making in mmW phase shifter design.

Innovative silica aerogel fiberglass walls and roofs: A path to lower carbon emissions and higher energy savings

ABSTRACT Buildings account for a significant portion of global energy use and consumption. Buildings have substantial energy consumption due to the use of heating, ventilation, and cooling systems. It is evident that the insulation materials used in the design of building envelopes can efficiently reduce the cost of air conditioning by reducing external heat gains and losses. Additionally, the use of environmentally friendly and cost-effective can be beneficial from an environmental standpoint. This paper aims to evaluate the potential for cost savings in the air conditioning of buildings incorporated with silica aerogel fiberglass insulation in hollow concrete bricks (wall) and hollow roof tile (roof). Silica aerogel fiberglass insulation is extensively used for heat insulation purposes, including building insulations, due to its superior thermal insulation and flame retardant properties. The thermo-physical properties of silica fiberglass insulation and building materials were determined experimentally as per ASTM D 5334 standards. Twenty distinct building design concepts are investigated in this study, each featuring insulation-integrated concrete blocks composed of silica aerogel fiberglass: (WEM1) insulation layer in the outer side, (WEM2) insulation layer in the center, (WEM3) insulation layer in the inner side, (WEM4) insulation layers in the outer-middle, (WEM5) insulation layers in the middle-inner, and (WEM6) insulation layers in the outer-inner sides. Additionally, two arrangements of insulation-stuffed roof tiles, REM1 above the RCC and REM2 below the RCC. In addition, novel building designs integrated with insulation were analyzed numerically, related to environmental conditions that refer to two different scenarios in India (hot dry and composite climates). Thermo-economic analysis of novel wall and roof designs with silica fiberglass insulation materials on air-conditioning costs, carbon dioxide emissions, and payback time are compared with conventional roof and wall patterns. The thermo-economic analysis was carried out based on the number of degree-hours of heating and cooling to determine the building’s annual energy usage. The building model arrangement (WEM6-REM1), which consists of an insulation composite wall (WEM6) and insulation roof tile (REM1), demonstrated the greatest annual air conditioning cost savings of 3.4 $/m2/year and 1.74 $/m2/year when compared to conventional building design for climatic conditions of Kurnool and Gauhati. The building model arrangement (WEM6-REM1) composed of silica fiberglass aerogel insulation exhibits the highest reduction in carbon emissions of 65 kg/kWh and 33 kg/kWh, when compared to conventional building design analyzed in both hot-arid; (Kurnool) and composite (Gauhati) climatic conditions. The placement of insulation in the roof alone (REM1) has the shortest payback duration of 2.15 years. The results of this study point to a viable approach to enhancing building design decision-making and narrowing the performance gap in sustainable and energy-efficient strategies.

Heteroscedastic Gaussian Process Regression for material structure–property relationship modeling

Uncertainty quantification is a critical aspect of machine learning models for material property predictions. Gaussian Process Regression (GPR) is a popular technique for capturing uncertainties, but most existing models assume homoscedastic aleatoric uncertainty (noise), which may not adequately represent the heteroscedastic behavior observed in real-world datasets. Heteroscedasticity arises from various factors, such as measurement errors and inherent variability in material properties. Ignoring heteroscedasticity can lead to lower model performance, biased uncertainty estimates, and inaccurate predictions. Existing Heteroscedastic Gaussian Process Regression (HGPR) models often employ complicated structures to capture input-dependent noise but may lack interpretability. In this paper, we propose an HGPR approach that combines GPR with polynomial regression-based noise modeling to capture and quantify uncertainties in material property predictions while providing interpretable noise models. We demonstrate the effectiveness of our approach on both synthetic and physics-based simulation datasets, including mechanical properties (effective stress) of porous materials. We also introduce an approximated expected log predictive density method for model selection, which eliminates the need for retraining the model during leave-one-out cross-validation, allowing for efficient hyperparameter tuning and model evaluation. By capturing heteroscedastic behavior, enhancing interpretability, and improving model selection, our approach contributes to the development of more robust and reliable machine learning models for material property predictions, enabling informed decision-making in material design and optimization.

Assessment, quantification and propagation of uncertainty in seismic life-cycle cost analysis

Seismic life-cycle cost analysis (SLCCA) is a comprehensive tool to estimate the expected lifetime economic losses due to seismic events in terms of present-day value and aids in lifetime investment planning and design decision-making. The state-of-the-art framework for SLCCA incorporates three essential components: seismic hazard analysis, seismic fragility curves and damage cost estimates. Due to different sources of uncertainty prevailing in each component, the SLCC estimates may vary substantially. However, most of the existing literature primarily focuses on the expected value of the SLCC, and uncertainties stemming from multiple sources are unaccounted for. To address this drawback, this study proposes a comprehensive approach to quantify and propagate multiple uncertainties and offers a visualization of the shape and nature of SLCC distribution. Furthermore, the application of the framework is demonstrated using a case study example of the reinforced concrete (RC) frame building. Results reveal that the SLCC distribution is multimodal due to multiple uncertainty sources and the expected SLCC values also shift significantly compared to the state-of-the-art approaches, highlighting the criticality of the uncertainty propagation. Lastly, a novel framework is proposed to rank uncertainty sources based on their relative influence that will aid stakeholders in informed decision-making under uncertainty.

76. Automating immunogenomic tumor board decision-making for neoantigen cancer vaccine design

76. Automating immunogenomic tumor board decision-making for neoantigen cancer vaccine design

Preliminary investigation of a wearable device for evaluating muscular effort and fatigue at workplace

AbstractControls and prevention activities for musculoskeletal disorders are increasingly encouraged in the industrial context. Several ergonomic assessment techniques exist. However, they primarily focus on the biomechanical and postural load requirements of job tasks and rely on self-report and observational methods rather than direct measurements, which are often too intrusive. This study investigates whether a commercially available low-intrusive and low-cost wearable sensor can be utilised to assess muscular effort and fatigue, thereby improving decision-making in work and workstation design. For this purpose, the potential of the Myo™ armband has been explored. The electrical activity of the upper limbs (arm and forearm) was evaluated through 15 physically tasks (manual handling and assembly) conducted in laboratory conditions. Satisfactory results were obtained from the correlation and significance analysis, considering the EMG signal, the Borg scale, and the activity level (e.g., load, distance, n° of repetitions). Further experimentation was conducted to validate the proposed approach in a real production facility.

Reclamation potential in the built environment: A method and metric for assessing environmental benefits beyond first use

Direct reuse and recycling of materials can significantly reduce the net environmental impact of the global construction sector. The feasibility of reuse and recyclability of building systems is affected by the materials used and the interfaces between constituent components. Yet there is a lack of quantitative methods for assessing the environmental benefits of alternative recovery strategies for multi-component and multi-material systems over the building lifetime. In this work, a novel assessment method was developed to enable a systematic and quantitative evaluation of the transient environmental reclamation potential (RP). The reclamation potential is a measure of the ability to disassemble and reuse recovered building systems at their end-of-life and is influenced by the constituent components and the interfaces between components. The proposed method accounts for the technical service lifetimes of components, including performance degradation over time, and can thus inform decisions on the most suitable recovery route for new and existing designs. The graphical outputs from the RP assessment are a network diagram which highlights the system components and connections between components, and an RP-graph which illustrates the embodied environmental impact and reclamation potential over time of alternative reuse/recycling strategies. The methodology is demonstrated on a glazed double-skin façade where the influence of component service lifetimes and replacements over time is quantified in terms of embodied energy and embodied carbon. The outcomes of the assessment can guide decision-making in design for disassembly (DfD) strategies and/or aid in the identification of high-value material recovery strategies at the end-of-life stage.

Synergistic integration of isogeometric analysis and data-driven modeling for enhanced strip footing design on two-layered clays: Advancing geotechnical engineering practices

This study innovatively combines Isogeometric Analysis (IGA) with Machine Learning (ML) to assess strip footing bearing capacity on dual clayey layers. Overcoming limitations of conventional methods with small sample sizes, our research generates a dataset of 10,000 samples, allowing a thorough exploration of diverse soil profiles. Facilitated by ML, 10,000 IGA analyses using upper bound limit analysis unveil intricate patterns and relationships previously obscured. The key innovation lies in harnessing big data and employing advanced data visualization, particularly 2D and 3D Partial Dependency Plots (PDPs). These PDPs visually showcase the impact of factors such as upper layer thickness, cohesion ratios, shear strength profiles, footing depth, and foundation roughness on bearing capacity. Offering intuitive insights, these visualization tools enhance comprehension, aiding informed decision-making in design and construction. Engineers and geotechnical experts receive a precise predictive tool, optimizing strip footing performance on clayey soil layers. Moreover, this research contributes to advancing geotechnical engineering by enriching fundamental knowledge of load-bearing characteristics. In summary, the fusion of big data, advanced visualization, and upper bound limit analysis, exemplified by PDPs, signifies a substantial leap in geotechnical engineering, impacting design, construction, and infrastructure development.

AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning

Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems.To address this issue, we developed AAontology—a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations—using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.

A decision framework for Chinese-style cruise ship design based on informativeness weight method and group consensus reaching model

In recent years, the European cruise ship design style has failed to meet the cultural needs of Chinese users, leading to a growing demand for cruise ships incorporating Chinese elements. The richness and uniqueness of Chinese culture make most existing cruise ship design decision-making methods inapplicable for Chinese-style cruise ship design. This article introduces a decision-making framework for Chinese-style cruise ships to address this gap based on the informativeness weight method and the group consensus achievement model. This framework determines decision criteria based on the characteristics of Chinese-style cruise ships and Cultural Hierarchy Theory (CHT), and it employs trapezoidal fuzzy set theory to manage the fuzziness of the decision-making environment, resulting in decision matrices. Furthermore, a bilateral negotiation model for group consensus achievement is introduced to reach group consensus among decision-makers (DMs), adjust the decision matrix, and calculate the weight of each expert. The informativeness weight method is used to determine the weight of each criterion, and the TOPSIS method is applied to establish option scores and rankings. By analyzing three Chinese-style cruise ship design options, the paper validates the effectiveness of this decision-making method. A comparative analysis of the informativeness weight method and the bilateral negotiation consensus achievement model further demonstrates the superiority of this decision-making framework.

Integration of Principal Component Analysis with AHP-QFD for Improved Product Design Decision-Making

The complexity of quality function deployment (QFD) matrices often hinders efficient decision-making in product design, leading to missed opportunities and extended development times. This study explores the integration of principal component analysis (PCA) with analytic hierarchy process-QFD (AHP-QFD) to address these challenges. PCA, a machine learning technique, was applied to QFD matrices from product design research to reduce complexity and enhance prioritization efficiency. The integrated method was tested with a product design team across various industries, including logistics, healthcare, and consumer electronics. The analysis demonstrated that PCA effectively reduced matrix complexity, optimizing feature prioritization. In the logistics sector, PCA explained 99.2% of the variance with the first five components, while in consumer electronics, it accounted for 86.9% with the first four components. However, PCA showed limitations in the healthcare sector due to evenly distributed variance among components. Expert feedback highlighted the practical benefits of the integrated approach: 75% of logistics experts and 62.5% of consumer electronics experts found the method clearer. For speed, 100% of logistics and 87.5% of consumer electronics experts preferred the method for quicker evaluations. For accuracy, 75% of logistics and 62.5% of consumer electronics experts deemed the method more accurate. Overall, the PCA-AHP-QFD method simplifies decision-making processes and reduces development time, particularly in industries where feature prioritization is crucial. These findings underscore the potential of the integrated approach to enhance product development efficiency and feature prioritization, with suitability varying based on industry characteristics.

A Case Study on the Operational Performance Evaluation of a Manufacturing Process and System (MaPS) Sustainability Analysis Tool for Engineering Education

As the future product and process design decision-makers, students in engineering should have knowledge of sustainability and its application in industry. However, educators are challenged in training students in these concepts. Existing analysis tools are deficient in supporting sustainability assessment by nonexperts. To address this gap, the Manufacturing Process and System (MaPS) Sustainability Analysis tool was developed under the Constructionism in Learning: Sustainable Life Cycle Engineering (CooL:SLiCE) project. The technical aspects of developing a framework and its associated proof-of-concept MaPS Sustainability Analysis tool are reported in a separate study. Herein, the educational aspects of the MaPS Sustainability Analysis tool and its operational performance in terms of ease of use and usefulness to assess its suitability for the classroom are investigated. Undergraduate and graduate engineering students at Tampere University and Oregon State University were recruited to use and evaluate the tool. Through application of the Technology Acceptance Model (TAM), it was determined that the 24 study participants found the tool easy to use and useful in analyzing product design, manufacturing process, and supply chain sustainability performance. To move this proof of concept toward general use, key software operational characteristics and a GUI should be investigated to improve efficiency, effectiveness, satisfaction, and learnability of the MaPS Sustainability Analysis tool.

Behavioral design in video games

Game design has an impact on health and well-being. Due to their evolving capacities, young people and children are especially sensitive to the negative and positive impacts of media design choices. To protect vulnerable people, ethical and responsible game design practices are necessary. But what constitutes “ethical” and responsible game design? Defining “behavioral design” as video game design decisions that elicit, either accidentally or purposefully, self-negative or self-beneficial behavior in the gamer, we draw from expertise in public health, game design, and legal scholarship to analyze current game design practices. Our work illustrates the centrality of changing business models with game design and the resulting tension within games. In practice, monetization strategies regularly guide decision-making in game design, facilitating risks for gamer welfare and finances.

Fuzzy Relationship between Kansei Images: A Grey Decision-Making Method for Product Form

Current design decision-making methods ignore the fuzzy relationship between Kansei images, and the use of constant weights reduces the accuracy of cognitive evaluation results. To solve these problems, this paper proposes a grey decision-making method for product form driven by the fuzzy relationship between Kansei images. First, according to the initial weight of the Kansei images, variable weight theory is used to determine the Kansei image variable weights of the samples, and the variable weight comprehensive evaluation results for each sample are obtained. Then, based on the correlation and angle of the Kansei images, a cobweb diagram is drawn to represent the fuzzy relationship between the Kansei images of each sample. Combined with the cobweb grey target decision-making model (CGTDM) for multiple Kansei images, decision coefficients are calculated. The decision coefficients are compared and ranked to determine the relatively optimal design reference sample. Finally, the constructed model is compared with the CGTDM for multiple Kansei images and TOPSIS. The results show that the difference coefficient of the proposed method is the largest, and it can reflect the decision-making thinking of the designers and improve the discrimination among the decision-making results to a certain extent.

Enhancing multi-facility manufacturing resilience through integrated product and supply chain network design

This paper presents an integrated approach for joint decision-making in reliable product design and resilient supply chain network design within a two-echelon capacitated network. Our methodology simultaneously addresses two crucial decisions faced by manufacturing industry by integrating the design processes of modular products and their supply chain networks, allowing for multiple production facilities and product variants. Through redundancy allocation, we optimize product reliability within production facilities while considering component sourcing from multiple suppliers. Our approach employs three resilience strategies—multi-sourcing, supplier fortification, and backup supplier contracting—to enhance supply chain resilience against disruptions. A case study solved using a genetic algorithm demonstrates the effectiveness of different resilience strategy combinations in achieving various levels of production resilience. This research offers insights into integrated decision-making for enhancing product reliability and supply chain resilience, thereby providing valuable guidance for industry practitioners. Furthermore, the sensitivity analysis highlights the framework’s capability to minimize total costs by prioritizing resilient designs and strategically investing in resilience strategies as costs of production shortage increase. This analysis underscores the interconnected nature of product and supply chain network design decisions in mitigating disruptions and enhancing production resilience.

Research on Multistage Heterogeneous Information Fusion of Product Design Decision-Making Based on Axiomatic Design

Research on Multistage Heterogeneous Information Fusion of Product Design Decision-Making Based on Axiomatic Design