Set Of Design Alternatives Research Articles

A Treatise on Reconnoitering the Suitability of Fuzzy MARCOS for Assessment of Conceptual Designs

The development of an equipment starts from an effective design activity. The concept selection process is an activity that is entailed in the design stage, and its relevance in the design process cannot be overemphasized because it informs the choice of optimal conceptual design from a set of alternative designs. Hence, there is a need to accrue efforts to the concept selection process because of its importance. This article presents the identification of optimal conceptual design as a multicriteria decision-making model by assessing the suitability of fuzzy Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS). The fuzzy MARCOS model was developed to access four alternative conceptual designs of briquetting machines considering eight design features with several sub-features. The results obtained from the decision analysis showed that the fuzzy MARCOS model was able to rank the designs based on their performance and the final values of the overall utility function. The overall utility function is based on the utility degree of the conceptual design alternatives in terms of the best and worst designs identified by the model. The utility degree created a platform for comparison on how the design alternatives varied from the best and worst designs. The results obtained from the MARCOS method were validated using the TOPSIS method and modified TOPSIS method, and the results obtained showed that the MARCOS method is in conformity with the validation results.

Socio-technical systems integration and design: a multi-objective optimisation method based on integrative preference maximisation

Current systems design optimisation methodologies are one-sided, as these ignore the socio-technical integration between stakeholder preferences (‘what a human wants’) and the capability of technical assets (‘what a system can deliver’). Moreover, classical multi-objective optimisation methods contain fundamental mathematical flaws. Also, the often-used classical Pareto front does not provide a single best-fit design configuration, but rather a set of design alternatives. This leaves designers without a unique solution to their problems. Finally, current multi-objective optimisation processes are not well aligned with design practices, because they do not sufficiently involve decision makers and do not translate their interests into a single common preference domain to find an overall group optimum. This paper introduces a new Open Design Systems (Odesys) methodology and a new Integrative Maximisation of Aggregated Preferences (IMAP) method, implemented in the Preferendus tool. Its added value and use are exemplified in two infrastructure design applications, which show how to achieve the pure best-fit for common-purpose design results.

Extending the application of fuzzy COPRAS to optimal product design

The availability of customized and multifunctional designs of a particular product in the market due to varieties in users’ requirement and demand necessitates that product design must be properly done before progressing to developmental stage. This implies that the importance of decision making in the selection of optimal conceptual design cannot be overstressed. The main contribution of this article is the implementation of Fuzzy COPRAS (COmplex PRoportional ASsessment) model as a decision tool for identification optimal design concept. The weights of the design features and sub-features were determined in the form linguistic terms generated from a triangular fuzzy number in order to avoid apportioning of crisp values in the decision process. An analytical approach was applied by creating a sub-aggregate matrix for each design feature, considering all the design alternatives in order to develop a sub-decision matrix. The relative significance or priorities of the design alternatives obtained from the maximization and minimization indices are defuzzified by applying the minimum degree of possibility in order to rank the design alternatives. The findings from the application of the Fuzzy COPRAS methodology to four design alternatives of pipe bending machines using eight design features and several sub-features indicates that the methodology can identify an optimal design concept from a set of alternative designs. The application of the methodology also shows that there are slight differences in the priority values of design alternatives next to the optimal design concept. The implication and recommendation from the application is that choosing any of the design alternatives other than the optimal design concept will provide the same risk

Best Fit for Common Purpose: A Multi-Stakeholder Design Optimization Methodology for Construction Management

Within traditional design processes, decisions are often made based on individualistic values and late-stage assessments conducted on a predefined set of design alternatives potentially leading to suboptimal design decisions and conflicts. The issues are further amplified by the growing complexity of construction project management where an increasing number of stakeholders are involved. To address those issues, a shift is needed towards a collaborative early stage optimization-based design process. The current optimization-based design approaches are not appropriately integrating stakeholders and their preferences in the optimization process and thereby not reflecting the real-life design and construction process. In this study, we present a pioneering multi-stakeholder design methodology combining preference function modeling theory and a priori optimization enabling stakeholders to find the group-optimal design fairly representing their preferences. The application of the developed methodology is demonstrated on a real-life multi-storey building design case. The study provides a novel approach for managing design and construction projects for academic and industry stakeholders. It also sets the foundation for the further development of stakeholder-oriented optimization-based design.

Analysing paradoxes in design decisions: the case of “multiple-district” paradox

In early design stages, a team of designers may often express conflicting preferences on a set of design alternatives, formulating individual rankings that must then be aggregated into a collective one. The scientific literature encompasses a variety of models to perform this aggregation, showing strengths and weaknesses. In particular situations, some of these models can lead to paradoxical results, i.e., contrary to logic and common sense. This article focuses on one of these paradoxes, known as multiple-district paradox, providing a new methodology aimed at identifying the reason of its potential triggering. This methodology can be a valid support for several decision problems. Some examples accompany the description.

Influence of Systematic Hull Shape Variations on Ship Stability Performances in Waves

Abstract The sensitivity of ship stability performance in waves to geometric variation has been investigated by means of a simulation-based design framework. The study was devoted to assess the influence of hull geometry variations on some stability failure modes, namely, parametric roll (PR) and pure loss of stability (PLS). The application has been developed by using a representative model of a postpanamax container vessel. PR and PLS phenomena have been investigated by the application of second-generation intact stability criteria (SGISc). The initial multidimensional design space has been filled by 500 design configurations identified by means of a design of experiments approach. A method developed in-house, combining the subdivision surface and free-form deformation approaches, has been used to create the whole set of design alternatives. The generated design configurations have been assessed analyzing the results derived from application of the first- and the second-level SGIS vulnerability criteria for both the selected stability failure modes. To strengthen the correlation behaviors, the design space has then been further explored by using 10k design configurations exploiting the capabilities of a surrogate model–based approximation, relying on a Gaussian process formulation. The study has been focused on the correlations among the variables and the response functions, i.e., the outcomes of the SGIS vulnerability criteria. The significance, in terms of effects, of each geometry shape variable has been investigated. Results have been discussed in the light of the SGISc structure, to provide further insight into this innovative safety framework for a modern approach to intact stability. Introduction In the last 10 years, the development of the so-called second-generation intact stability criteria (SGISc) has been one of the most engaging topics addressed by the Sub-Committee on Safety Design and Construction (SDC) of the International Maritime Organization (IMO).

Stability Metrics for Enhancing the Evaluation of Outcome-Based Business Process Predictive Monitoring

Outcome-based predictive process monitoring deals with predicting the outcomes of running cases in a business process using feature vectors extracted from completed traces in an event log. Traditionally, in outcome-based predictive monitoring, a different model is developed using a bucket containing different types of feature vectors. This allows us to extend the traditional evaluation of the quality of process outcome predictions models beyond simply measuring the overall performance, developing a quality assessment framework based on three metrics: one considering the overall performance on all feature vectors, one considering the different levels of performance achieved on feature vectors belonging to individual buckets, i.e., the stability of the performance across buckets, and one considering the stability of the individual predictions obtained, accounting for how close the predicted probabilities are to the cutoff thresholds used to determine the predicted labels. The proposed metrics allow to evaluate, given a set of alternative designs, i.e., combinations of classifier and bucketing method, the quality of the predictions of each alternative. For this evaluation, we suggest using either the concept of Pareto-optimality or a scenario-based scoring method. We discuss an evaluation of the proposed framework conducted with real-life event logs.

Design concept evaluation technique via functional link matrix and fuzzy VIKOR based on left and right scores

ABSTRACT This article presents a new technique for determining the weights of design features by searching for functional links between their sub-features. The method further applies fuzzy VIKOR based on left and right scores to determine the optimal design concept from a decision matrix obtained from three experts view. The application of this technique to the design of a Reconfigurable Assembly Fixture (RAF) shows that it is a viable method for determining the weights of design features and identifying the optimal design concept from a set of alternative designs. In order to support the viability of this technique, the results obtained from its application to the design of a RAF was compared with the results of other Multi-Criteria Decision-Making (MCDM) methods when applied to the design of the RAF. The comparison shows that the technique is feasible and can be employed to determine the weights of design features in order to identify optimal design at the conceptual phase.

Data Visualisation as a Tool for Informed Structural Design

The architectural design process is characterised by continuously comparing different design alternatives throughout the whole design process. Collaborators from different fields of expertise need to work closely together and they are expected to give quick but sufficient feedback on the available design alternatives in order to proceed in the collaborative process. However, for structural engineers making these comparisons on the structural behaviour and efficiency is hard to do. The lack of appropriate tools disables the structural engineer to gain quickly and qualitatively insight into a set of design alternatives. This paper discusses a novel mind-set for structural designers, that fills this gap by informed decision-making based on data visualisation. This kind of data exploration is only rarely used in the structural design field. Since visual displays of data allow for better decision-making, this opportunity should be leveraged by structural designers.Five different bow-string bridge geometries are compared as a case study with the help of data visualisation. As a consequence, this paper focuses on the comparing practice of structural parametric models of the same structural typology. This research illustrates that the use of data visualisations can help designers to quickly understand the structural behaviour and performance of different design alternatives. This way, informed decision-making through comparing is facilitated.

Product development resilience through set‐based design

AbstractOften during a system Product Development program, external factors or requirements change, forcing system design change. This uncertainty adversely affects program outcome, often adding to development time and cost, production cost, and can compromise system performance. We present a development approach that minimizes the impacts, by proactively considering the possibility of changes in the external factors and the implications of mid‐course design changes. The approach considers the set of alternative designs and the burdens of a mid‐course change from one design to another in determining the relative value of a specific design through the set‐based design methodology. The approach considers and plans parallel (redundant) development of alternative designs with progressive selection of options, including time‐versus‐cost tradeoffs and the impact change‐costs. The approach includes a framework of the development process addressing design and integration lead‐times, their relationship to the time‐order of design decisions, and the time‐dependent burden of design changes. We also compare set‐based and single point design schemes.

Energy-optimized versus cost-optimized design of high-performing dwellings: The case of multifamily buildings

Living in multifamily buildings is very common in Italy, and it is important to optimize the design strategies to minimize the energy demand of these buildings and their related operational costs. This is particularly important for low-income tenants and is pursued by many social housing developments where a good energy performance design is reached. In this work, a simulation-based optimization methodology that combines the use of TRNSYS with GenOpt is applied to minimize two objective functions—the annual primary energy demand and the operational energy cost—in different system technology scenarios, and to verify the differences between energy-optimized design and cost-optimized design in a Northern Italy climate. The study is performed on a typical floor of a real multifamily building for social housing. The envelope optimization demonstrates a potential reduction of the energy demand and cost for heating and cooling of more than 35%. The relationship between optimal solutions, system technology scenarios, and optimization objectives is deeply analyzed. It is possible to find a set of design solutions that is optimal for all analyzed scenarios. This provides a set of design alternatives that is close to the environmental optimum and is able to reduce low-income tenants' vulnerability.

Fundamental study on design concept extraction by neural network from a set of design alternatives

Fundamental study on design concept extraction by neural network from a set of design alternatives

Development of product platforms: Theory and methodology

There is a trend toward increased customization of goods to satisfy a wide range of customers using product platforms. However, there is an erroneous notion that product platforms can only be used to provide economic viability in production thanks to the reuse of physical components among a family of products. Yet, this is a limited perception of the potential of a product platform. In this article, an object-oriented approach to support the development of product platforms is proposed to increase efficiency through reuse and flexibility of designs among a family of products. Two modes of the platform development process are addressed: platform preparation and platform execution. Platform preparation prescribes the methods needed to model platform objects, using enhanced function-means models and set-based concurrent engineering processes. During the platform execution process, sets of design alternatives can be configured concurrently throughout the conceptual, system, and detailed phases of the platform development. Three cases illustrate how the same approach may be used in different design scenarios: design space exploration and extension, supply-chain collaboration, and configure-to-order. The approach supports system architects and design engineers in making design decisions that propel the platform development work by enabling analysis in stages where designs are immature and evaluating the goodness of the alternatives early. Ultimately, product platforms can be efficiently developed for modularity and scalability to find feasible product variants and meet the needs of a multitude of customers.

Low-cost multi-objective optimization and experimental validation of UWB MIMO antenna

Purpose – The purpose of this paper is to validate methodologies for expedited multi-objective design optimization of complex antenna structures both numerically and experimentally. Design/methodology/approach – The task of identifying the best possible trade-offs between the antenna size and its electrical performance is formulated as multi-objective optimization problem. Algorithmic frameworks are described for finding Pareto-optimal designs using auxiliary surrogate models and two alternative approaches to design refinement: response correction techniques and co-kriging. Numerical and experimental case studies are provided to demonstrate feasibility of solving real-world and complex antenna design tasks. Findings – It is possible, through appropriate combination of the surrogate modeling techniques (both data driven and physics based) and response correction methods, to find the set of alternative designs representing the best possible trade-offs between conflicting design objectives, here, electrical performance and size. Design optimization can be performed at practically feasible computational costs. Research limitations/implications – The study demonstrates feasibility of automated multi-objective design optimization of antennas at low computational cost. The presented techniques reach beyond the commonly used design approaches based on parameter sweeps and similar hands-on methods, particularly in terms of automation, reliability and reduction of the computational costs of the design processes. Originality/value – Multi-objective design of antenna structures is very challenging when high-fidelity electromagnetic simulations are utilized for performance evaluation of the structure at hand. The proposed design framework permits rapid optimization of complex structures (here, MIMO antenna), which is hardly possible using conventional methods.

Strategies for computationally feasible multi-objective simulation-driven design of compact RF/microwave components

Purpose – Strategies for accelerated multi-objective optimization of compact microwave and RF structures are investigated, including the possibility of exploiting surrogate modeling techniques for electromagnetic (EM)-driven design speedup for such components. The paper aims to discuss these issues. Design/methodology/approach – Two algorithmic frameworks are described that are based on fast response surface approximation models, structure decomposition, and Pareto front refinement. Numerical case studies are provided demonstrating feasibility of solving real-world problems involving multi-objective optimization of miniaturized microwave passives and expensive EM-simulation models of such structures. Findings – It is possible, through appropriate combination of the surrogate modeling techniques and response correction methods, to identify the set of alternative designs representing the best possible trade-offs between conflicting design objectives in a realistic time frame corresponding to a few dozen of high-fidelity EM simulations of the respective structures. Research limitations/implications – The present study sets a direction for further studied on expedited optimization of computationally expensive simulation models for miniaturized microwave components. Originality/value – The proposed algorithmic framework proved useful for fast design of microwave structures, which is extremely challenging when using conventional methods. To the authors’ knowledge, this is one of the first attempts to surrogate-assisted multi-objective optimization of compact components at the EM-simulation level.

Selecting the Best Simulated Design With the Expected Opportunity Cost Bound

In many applications, we wish to select the best simulated design among a set of design alternatives. While most work in the literature evaluates the quality of a selection procedure by the probability of correct selection of the best design, the expected opportunity cost of a potentially incorrect selection is also an important and useful measure, especially when the design performance reflects economic value. In this technical note, we consider the selection problem with an expected opportunity cost constraint. Based on some approximations, we derive for the selection problem an asymptotic closed-form allocation rule, which is easy to compute and implement. The numerical testing shows that the proposed selection procedure can enhance the simulation efficiency significantly.

Precision Die Design by The Die Expansion Method

A new method for the design of precision dies used in cold forging, extrusion and drawing processes is presented. It is based on die expansion. It attempts to provide a clear-cut theoretical basis for the selection of critical die dimensions for this group of precision dies when the tolerance on product diameter (or thickness) is specified. It also presents a procedure for selecting the minimum-production-cost die from a set of design alternatives. The results show that as external die diameter increases, die expansion decreases while die strength increases. Also, as the degree of die compounding increases, die expansion decreases and die strength increases. Comparison of theoretical and experimental results shows good agreement especially at full die cavity filling stage. Die selection from a set of multi-layer dies is based on the die that has minimum Design Effectiveness Index (DEI) which gives the minimum-production-cost die. It is hoped that the design method, apart from providing solutions to simple design problems, can be applied as a first approximation to the solution of more complex problems, based on a clear-cut theory rather than the usual rule-of-thumb method. Thereafter, more sophisticated computer-based methods may be applied for refinement.

Methods to Assess Cost-Effectiveness and Value of Further Research When Data Are Sparse

Health care resources are scarce, and decisions have to be made about how to allocate funds. Often, these decisions are based on sparse or imperfect evidence. One such example is negative-pressure wound therapy (NPWT), which is a widely used treatment for severe pressure ulcers; however, there is currently no robust evidence that it is effective or cost-effective. This work considers the decision to adopt NPWT given a range of alternative treatments, using a decision analytic modeling approach. Literature searches were conducted to identify existing evidence on model parameters. Given the limited evidence base, a second source of evidence, beliefs elicited from experts, was used. Judgments from experts on relevant (uncertain) quantities were obtained through a formal elicitation exercise. Additionally, data derived from a pilot trial were also used to inform the model. The 3 sources of evidence were collated, and the impact of each on cost-effectiveness was evaluated. An analysis of the value of further information indicated that a randomized controlled trial may be worthwhile in reducing decision uncertainty, where from a set of alternative designs, a 3-arm trial with longer follow-up was estimated to be the most efficient. The analyses presented demonstrate how allocation decisions about medical technologies can be explicitly informed when data are sparse and how this kind of analyses can be used to guide future research prioritization, not only indicating whether further research is worthwhile but what type of research is needed and how it should be designed.

Design of an Infrastructure Project Using a Point-Based Methodology

AbstractOur experience in engineering education and practice indicates that methodologies for architecture, engineering, and construction (AEC) design management are characterized only to a limited extent. In domains other than AEC such methodologies are well-established and contribute to improving design performance. A step towards advancing AEC design management is to characterize design practices and explore the methodologies on which they may be based. In this vein, the authors describe a design practice observed while participating in the development of a design-build-operate-and-maintain (DBOM) proposal for an infrastructure megaproject. The development team’s design process followed a point-based methodology, i.e., early selection from a small set of design alternatives. The team was structured as a three-party joint venture, but, as observed, integration of efforts and collaboration among its design engineers and contractors were difficult and infrequent. The point-based design methodology used sh...

Multi-objective optimization of sustainable single-effect water/Lithium Bromide absorption cycle

A rigorous mathematical approach is developed for optimization of sustainable single-effect water/Lithium Bromide (LiBr) absorption cooling cycles. The multi-objective formulation accounts for minimization of the chiller area as well as the environmental impact associated with the operation of the absorption cycle. The environmental impact is quantified based on the global warming potential and the Eco-indicator 99, both of which follow principles of life cycle assessment. The design task is formulated as a bi-criterion non-linear programming problem, the solution of which is defined by a set of Pareto points that represent the optimal compromise between the total area of the chiller and global warming potential. These Pareto sets are obtained via the epsilon constraint method. A set of design alternatives are provided for the absorption cycles rather than a single design; the best design can be chosen from this set based on the major constraints and benefits in a given application. The proposed approach is illustrated design of a typical absorption cooling cycle.