scholarly journals An Exploratory Study on Optimal Iterative Design Schedules with the Consideration of Design Quality and Resource Constraints

2021 ◽  
Vol 13 (8) ◽  
pp. 4584
Author(s):  
Sou-Sen Leu ◽  
Theresia Daisy Nattali Suparman ◽  
Cathy Chang-Wei Hung
Keyword(s):  
Design Process ◽  
Completion Time ◽  
Resource Constraints ◽  
Design Quality ◽  
Concurrent Process ◽  
Dependency Structure ◽  
Iterative Design ◽  
Scheduling Model ◽  
Design Schedule ◽  
Dependency Structure Matrix

The classical dependency structure matrix (DSM) can effectively deal with iterative schedules that are highly coupled and interdependent, such as the design process and the concurrent process. Classical DSM generally follows the assumption that the least iteration occurs to achieve the shortest completion time. Nevertheless, the assumption may not hold because tasks ought to be re-visited several times if the design qualities do not meet the requirements. This research proposed a novel iterative scheduling model that combines the classical DSM concept with quality equations. The quality equations were used to determine the number of tasks that ought to be re-visited for fulfilling quality requirements during the iterative design process. Moreover, resources for concurrent activities are generally limited in the real world. Resource allocation should be incorporated in scheduling to avoid the waste and shortage of resources on a design project. This research proposed a new iterative scheduling model based on the classical DSM to optimize the iterative activities’ structure in terms of minimizing completion time with the consideration of design quality under resource constraints. A practical design schedule was introduced to demonstrate the applicability of the proposed DSM algorithm.

Determining Optimal Model Complexity in an Iterative Design Process

1990 ◽  
Author(s):  
David G. Bell ◽  
Dean L. Taylor
Keyword(s):  
Design Process ◽  
Design Theory ◽  
Quantitative Methods ◽  
Computational Models ◽  
Resource Constraints ◽  
Cost Savings ◽  
Model Complexity ◽  
Optimal Model ◽  
Iterative Design ◽  
Trade Offs

Abstract In design, trade-offs are made between quality, cost and schedule. As the versatility of design tools has increased, so has the difficulty of making these trade-offs. This is especially true when choosing appropriate complexities for analytical and computational models. A fact known to designers and analysts is that more complex models yield more accurate results, but take longer to create and analyze. This paper shows that in an iterative design process there exists an optimal model complexity for each iteration, and alternatively, one optimal for the process as a whole. Quantitative methods for determining these complexities are developed, and three strategies for controlling the design modelling process are contrasted. Dependent on the stopping criteria, the best strategy will either minimize resource expenditure subject to quality requirements, or conversely, will maximize quality subject to resource constraints. These concepts are formalized with a view towards the design theory and methodology research community providing a first step in quantifying model trade-offs. An illustrative shape-optimal design example is shown wherein cost savings up to 84% were achieved. These savings were seen using several standard optimization algorithms. A customized optimization algorithm that incorporates this work should produce even better results.

The design of modular oil tank: A new design process model

2020 ◽  
Vol 15 ◽  
Author(s):  
Jin Li ◽  
Xingsheng Jiang ◽  
Jingye Li ◽  
Yadong Zhao ◽  
Xuexing Li
Keyword(s):  
Product Design ◽  
Design Process ◽  
Process Model ◽  
Mechanical Design ◽  
Computer Software ◽  
Reference Value ◽  
Fuel Tank ◽  
Design Quality ◽  
Design Time ◽  
Design Process Model

Background: In the whole design process of modular fuel tank, there are some unreasonable phenomena. As a result, there are some defects in the design of modular fuel tank, and the function does not meet the requirements in advance. This paper studies this problem. Objective: Through on-the-spot investigation of the factory, a mechanical design process model is designed. The model can provide reference for product design participants on product design time and design quality, and can effectively solve the problem of low product design quality caused by unreasonable product design time arrangement. Methods: After sorting out the data from the factory investigation, computer software is used to program, simulate the information input of mechanical design process, and the final reference value is got. Results: This mechanical design process model is used to guide the design and production of a new project, nearly 3 months ahead of the original project completion time. Conclusion: This mechanical design process model can effectively guide the product design process, which is of great significance to the whole mechanical design field.

A Perioperative Care Display for Understanding High Acuity Patients

2021 ◽  
Vol 12 (01) ◽  
pp. 164-169
Author(s):  
Laurie Lovett Novak ◽  
Jonathan Wanderer ◽  
David A. Owens ◽  
Daniel Fabbri ◽  
Julian Z. Genkins ◽  
...  
Keyword(s):  
Data Visualization ◽  
Design Process ◽  
Cognitive Task ◽  
Development Project ◽  
Clinical Informatics ◽  
Data Display ◽  
Data Types ◽  
Iterative Design ◽  
High Acuity ◽  
Depth Interviews

Abstract Background The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.

Mechanism Design and Simulation of the ULTRA Spine: A Tensegrity Robot

2015 ◽  
Author(s):  
Andrew P. Sabelhaus ◽  
Hao Ji ◽  
Patrick Hylton ◽  
Yakshu Madaan ◽  
ChanWoo Yang ◽  
...  
Keyword(s):  
Mechanism Design ◽  
Design Process ◽  
Inverse Kinematics ◽  
Gear Ratio ◽  
Forward Kinematics ◽  
Robot Design ◽  
Link Structure ◽  
Iterative Design ◽  
Quadruped Robots ◽  
Ongoing Effort

The Underactuated Lightweight Tensegrity Robotic Assistive Spine (ULTRA Spine) project is an ongoing effort to create a compliant, cable-driven, 3-degree-of-freedom, underactuated tensegrity core for quadruped robots. This work presents simulations and preliminary mechanism designs of that robot. Design goals and the iterative design process for an ULTRA Spine prototype are discussed. Inverse kinematics simulations are used to develop engineering characteristics for the robot, and forward kinematics simulations are used to verify these parameters. Then, multiple novel mechanism designs are presented that address challenges for this structure, in the context of design for prototyping and assembly. These include the spine robot’s multiple-gear-ratio actuators, spine link structure, spine link assembly locks, and the multiple-spring cable compliance system.

A Remote Synthetic Testbed For Human-Robot Teaming: An Iterative Design Process

2021 ◽  
Vol 65 (1) ◽  
pp. 781-785
Author(s):  
Margaret Wong ◽  
Akudasuo Ezenyilimba ◽  
Alexandra Wolff ◽  
Tyrell Anderson ◽  
Erin Chiou ◽  
...  
Keyword(s):  
Design Process ◽  
Information Exchange ◽  
Task Environment ◽  
Research Administration ◽  
Urban Search And Rescue ◽  
Pilot Studies ◽  
Iterative Design ◽  
Hazardous Environments ◽  
User Friendly ◽  
Real Time Information

Urban Search and Rescue (USAR) missions often involve a need to complete tasks in hazardous environments. In such situations, human-robot teams (HRT) may be essential tools for future USAR missions. Transparency and explanation are two information exchange processes where transparency is real-time information exchange and explanation is not. For effective HRTs, certain levels of transparency and explanation must be met, but how can these modes of team communication be operationalized? During the COVID-19 pandemic, our approach to answering this question involved an iterative design process that factored in our research objectives as inputs and pilot studies with remote participants. Our final research testbed design resulted in converting an in-person task environment to a completely remote study and task environment. Changes to the study environment included: utilizing user-friendly video conferencing tools such as Zoom and a custom-built application for research administration tasks and improved modes of HRT communication that helped us avoid confounding our performance measures.

Developing Clinical Decision Support System for Sleep Staging Tasks by Formulating Explanations from Artificial Intelligence: User-Centered Design and Evaluation. (Preprint)

2021 ◽  
Author(s):  
Jeonghwan Hwang ◽  
Taeheon Lee ◽  
Honggu Lee ◽  
Seonjeong Byun
Keyword(s):  
Artificial Intelligence ◽  
Decision Support ◽  
Design Process ◽  
Clinical Decision Support ◽  
User Study ◽  
Clinical Decision ◽  
Sleep Staging ◽  
User Centered Design ◽  
Iterative Design ◽  
User Centered

BACKGROUND Despite the unprecedented performances of deep learning algorithms in clinical domains, full reviews of algorithmic predictions by human experts remain mandatory. Under these circumstances, artificial intelligence (AI) models are primarily designed as clinical decision support systems (CDSSs). However, from the perspective of clinical practitioners, the lack of clinical interpretability and user-centered interfaces block the adoption of these AI systems in practice. OBJECTIVE The aim of this study was to develop an AI-based CDSS for assisting polysomnographic technicians in reviewing AI-predicted sleep staging results. This study proposed and evaluated a CDSS that provides clinically sound explanations for AI predictions in a user-centered fashion. METHODS User needs for the system were identified during interviews with polysomnographic technicians. User observation sessions were conducted to understand the workflow of the practitioners during sleep scoring. Iterative design process was performed to ensure easy integration of the tool into clinical workflows. Then, we evaluated the system with polysomnographic technicians. We measured the improvements in sleep staging accuracies after adopting our tool and assessed qualitatively how the participants perceived and used the tool. RESULTS The user study revealed that technicians desire explanations relevant to key electroencephalogram (EEG) patterns for sleep staging when assessing the correctness of the AI predictions. Here, technicians could evaluate whether AI models properly locate and use those patterns during prediction. Based on this, information in AI models that is closely related to sleep EEG patterns was formulated and visualized during the iterative design process. Furthermore, we developed a different visualization strategy for each pattern based on the way the technicians interpreted the EEG recordings with these patterns during their workflows. Generally, the tool evaluation results from the nine polysomnographic technicians were positive. Quantitatively, technicians achieved better classification performances after reviewing the AI-generated predictions with the proposed system; classification accuracies measured with Macro-F1 scores improved from 60.20 to 62.71. Qualitatively, participants reported that the provided information from the tool effectively supported them, and they were able to develop notable adoption strategies for the tool. CONCLUSIONS Our findings indicate that formulating clinical explanations for automated predictions using the information in the AI with a user-centered design process is an effective strategy for developing a CDSS for sleep staging.

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