Multi-Attribute Tradespace Exploration Research Articles

Multi-attribute assessment of a river electromobility concept in the Amazon region

In this study, a water electro-mobility concept has been analyzed in the Amazon using the case study of the native communities in the Yarinacocha district in Peru. Multi-Attribute Tradespace Exploration (MATE) methodology is applied to identify the various stakeholders' objectives, generate the designs, and evaluate them. This methodology captured the intention of assessing not only technical aspects but also environmental and economic sustainability. Six monohull designs for each of the three navigation routes identified, plus an aluminum catamaran, resulted from the combination of the three materials (wooden, fiber reinforced plastic, and aluminum) of canoe-type monohulls with two types of electric propulsion systems: outboard pod propeller and long-tail shaft coupled with permanent magnet brushless DC motors. It was found that the Amazon canoe hulls studied require propulsion units of 3 kW to navigate in calm waters and 10 kW in rivers. The silicon-based solar photovoltaic systems are equipped with lithium iron phosphate storage batteries. Each design is valued according to the project's stakeholders' preferences, guiding the decision-maker to focus on those that offer higher value at a lower expense. Most solar boats on the two long routes had higher internal rates of return than the current design. The best options generate rates of 29.62% versus 11% and 10.79% versus 5.26%. It could be demonstrated that electric propulsion is competitive to fossil fuel-based propulsion for this case study.

Holistic System Design Evaluation Through Expanded MATE

According to Buede and Miller, not applying a holistic simulation and modeling approach to early design evaluation practices and trade studies results in greater developmental costs, time to market, and unforeseeable system-level design consequences. Completed research is presented for an innovative methodology that enhances the capacity of conventional design evaluations by integrating new types of simulations consistently into the existing Multi-attribute Tradespace Exploration (MATE) design evaluation architecture. This enhancement is particularly important for any envisioned software-intensive system that introduces greater system complexity. According to Ross et al., MATE was originally developed to guide engineering design and initial product selection by exploring candidate designs to deliver utility-based solutions. Unfortunately, the MATE methodology does not support software design evaluation and integration into complex systems. The results presented validate MATE improvements that are scalable and extensible to any software-based system of varying complexity and application. The subject research provides proof of this concept by focusing on sensor systems as an initial practical demonstration due to their rapidly expanding commercial use and Internet of Things (IoT) applications. Project engineers and especially software developers in any product field will benefit from this design method by leveraging its holistic evaluation of software and hardware system design performance for more efficient product design identification and implementation.

Artificial intelligence analytics with Multi-Attribute Tradespace Exploration and Set-Based Design

Data-driven design approaches such as Multi-Attribute Tradespace Exploration and Set-Based Design are increasing in popularity due to their ability to capture broader decision spaces than traditional point-based design. These methods share many of the same features and have complementary goals. Artificial intelligence offers a way to process the large amounts of data created by these methods in a fast and objective manner, supporting the insights of subject matter experts. This paper discusses the intersection of these three research areas and demonstrates an approach for combining these techniques to rapidly identify the most value-driving decisions available to the design team.

Multi-Attribute Tradespace Exploration for Survivability

Multi-attribute tradespace exploration for survivability is a system design and analysis methodology that incorporates survivability considerations into the tradespace exploration process (i.e., a solution-generating and decision-making framework that applies decision theory to model-based design). During the concept-generation phase of tradespace exploration, the methodology applies 17 empirically validated survivability design principles spanning susceptibility reduction, vulnerability reduction, and resilience enhancement. During subsequent concept evaluation, the methodology adds value-based survivability metrics to traditional architectural evaluation criteria of mission utility and life-cycle cost. Applied to a satellite radar mission, the methodology allowed operational survivability to be statistically evaluated across representative distributions of naturally occurring disturbances in the space environment and for survivability to be incorporated as a decision factor earlier in the design process. Constellations in the illustrative example are shown to be the most survivable, mitigating disturbances architecturally rather than through additive features.

Design for Affordability in Complex Systems and Programs Using Tradespace-based Affordability Analysis

With growing emphasis on affordability, the conceptual design of complex systems and programs is no longer confined to maximizing technical performance, but also to minimizing cost and schedule related attributes. By defining affordability as the property of becoming or remaining feasible relative to resource needs and resource constraints over time, Multi-Attribute Tradespace Exploration and Epoch-Era Analysis can be used to find affordable solutions. Single-epoch, multi-epoch and single- era analysis were conducted for a Space Tug program case study to demonstrate the application of these tradespace-based methods and a design with the best tradeoffs among performance, cost and schedule factors was obtained.

Incorporating Systems Engineering Methodologies to Increase the Transferability of Journey Planners

One characteristic that is highly desired in transportation-related applications, and particularly journey planners, is transferability – i.e., the capacity to be used with minimal modification in different locations. To achieve transferability, the initial design must take into account all factors that may diverge between locations, including existing modes of transport, the availability of required data, the technological habits of users, etc. In consequence, a highly transferable system is difficult and expensive to develop and maintain. A very flexible initial design, one ensuring low-cost adaptability of the system for different cities, regions, or countries, might not be cost-effective. On the other hand, a rigid design, tailored for a specific location, might act as a barrier to implementing the system elsewhere. This dilemma has motivated researchers to seek a structured process for selecting the most promising design, one that will realize the benefits of transferability while minimizing development costs.One of the fundamental building blocks of structured design in SE is requirements-design exploration. This paper evaluates the use of Multi-Attribute Tradespace Exploration (MATE), a leading design exploration process, for the effective design of journey planners.We examine the process of changeability assessment (e.g., transferability) in light of the goals of journey planning from the point of view of different stakeholders: travelers, private developers, and transport authorities. The analysis demonstrates how tradespace exploration can also be used to identify specific designs that bridge the gap between the public and private sectors and provide value over time to all parties. Moreover, when specific concerns of public authorities are not met, tradespace exploration can reveal measures the public sector can take (financial or others) for making their preferred design attractive to the private sector as well.

Combining Attributes for Systems of Systems in Multi‐Attribute Tradespace Exploration

One of the principal value propositions for the creation of Systems of Systems (SoS) is the ability to generate stakeholder value beyond that which can be delivered by a single system or even a collection of systems. The dynamic interactions among the component systems in a SoS make conceptual design decisions for SoS more complex compared to traditional system design. There are several heuristics and qualitative guidelines for designing SoS in the literature, but there is a lack of practical quantitative methods for SoS concept exploration. Development of quantitative methods for SoS conceptual design will greatly improve the ability of decision makers to select SoS designs in the concept design phase that will be value robust over time, by allowing them to consider a larger and more complete set of possible alternative SoS designs than is possible with qualitative methods alone. Multi-Attribute Tradespace Exploration has been used in the past to compare large numbers of system alternatives on a common cost-utility basis. In this method, the designer elicits the decision maker’s needs and formulates these as quantified attributes. The systems are then analyzed in terms of their ability to achieve the desired levels of attribute metrics. SoS-level attribute calculations must reflect component system interactions and emergent SoS-level value, as well as the added costs and benefits in the SoS as compared to that of the component systems operating alone. This paper introduces techniques for the SoS attribute combination modeling within the Multi-Attribute Tradespace Exploration method when considering SoS with heterogeneous component systems. Combining the attributes for the SoS-level must take into account the nature of the attributes provided by the component systems as well as the concept of operations for SoS. The techniques used for SoS attribute combination are classified according to the level of coordination between the component systems in the SoS. Using the information about the complexity of the method required along with knowledge about the control structure in the SoS, the SoS architect can estimate the added SoS integration costs. The ability to combine attributes contributes an essential constituent for the extension of the tradespace exploration methodology from the single system to the SoS level, allowing for the comparison of many SoS alternatives on a common cost-utility basis.

Multi-Attribute Tradespace Exploration as Front End for Effective Space System Design

Covers advancements in spacecraft and tactical and strategic missile systems, including subsystem design and application, mission design and analysis, materials and structures, developments in space sciences, space processing and manufacturing, space operations, and applications of space technologies to other fields.

A Knowledge Based Approach to Facilitate Enginering Design

This paper presents a knowledge-based approach to facilitate the engineering design process relating to spacecrafts. Because the design evolves over a long time and typically involves individuals working at different locations and frequently for different organizations, the degree of collaboration across temporal and spatial boundaries plays a major role in determining the aggregate time and cost involved in each instance of spacecraft design. A major aspect of such collaboration is the issue of communications - the ability to clearly and efficiently explicate and record the detailed needs of every stakeholder in the process, as well as the major design decisions and the rationale behind these decisions. The approach described in this paper provides a framework for facilitating the decision-making process in engineering design, by eliciting and capturing the goals and requirements of every stakeholder in the design process through utility and expense functions. An interactive system has been designed that incorporates a four faceted knowledge-based framework of knowledge acquisition, knowledge discovery, knowledge management and knowledge dissemination to provide designers and stakeholders with the capability to develop an evolving knowledge repository about all aspects of the design process. This interactive system includes the ability to capture succeeding versions of the detailed design, with zero or minimal human involvement; the capability is provided by a set of algorithms collectively named as SSPARCy. A complimentary tool, called MIST, facilitates the Multi-Attribute Tradespace Exploration process by enabling stakeholders to express their goals and preferences in a formalized manner. The combination of MIST and SSPARCy paradigms enables one to transform crucial applications that are today contingent on geographical proximity to occur with equal or superior effectiveness in a virtual world. While this paper analyzes a situation involving engineering design of spacecrafts, the proposed knowledge-based approach can be readily adopted to facilitate other applications that involve sustained collaboration across geographic and corporate boundaries.