An Examination of the Trade Study Process at NASA

Abstract:

Engineering managers increasingly rely on trade study processes to design optimum system architectures. Opinions on what constitutes a quality trade study differ, and this can hinder the engineering manager in determining what trade study processes to follow. This article contributes to the manager's body of knowledge by identifying trade study best practices through reviews of published research and interviews with engineers who conducted successful trade studies supporting the design of complex space systems.

A checklist of trade study best practices was developed. Three completed NASA trade studies were then evaluated against the checklist in conjunction with interviews of engineers involved in the studies. Through one-on-one interviews with engineers, assessments were made on the value, appropriateness, and effectiveness of each trade study. Key issues, taken from the checklist, were addressed and the lessons learned were identified. The goal of the study was to establish the groundwork for a more detailed analysis of trade study processes. The engineering manager can use these preliminary findings to ensure that their trade study processes are timely, set up properly, and contain needed checks and balances.     

Introduction To Probability and Statistical Decision Theory

A nonparametric sequential rank correlation test for independence consisting of a generalized sequential probability ratio test is investigated through the Monte Carlo method. The test is found to be satisfactory in terms of the power function. Comparison of the expected sample size of the test and three other tests including the fixed sample size test suggests that the sequential rank test is also efficient.     

An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines

One of the major design problems in the context of manufacturing systems is the well-known Buffer Allocation Problem (BAP). This problem arises from the cost involved in terms of space requirements on the production floor and the need to keep in mind the decoupling impact of buffers in increasing the throughput of the line. Production line designers often need to solve the Buffer Allocation Problem (BAP), but this can be difficult, especially for large production lines, because the task is currently highly time consuming. Designers would be interested in a tool that would rapidly provide the solution to the BAP, even if only a near optimal solution is found, especially when they have to make their decisions at an operational level (e.g. hours). For decisions at a strategic level (e.g. years), such a tool would provide preliminary results that would be useful, before attempting to find the optimal solution with a specific search algorithm.     

An extensive simplex method for mapping global feasibility

Understanding the global feasibility of engineering decision-making problems is fundamental to the synthesis of rational engineering decisions. An Extensive Simplex Method is presented to solve the global feasibility for a linear decision model relating multiple decision variables to multiple performance measures, and constrained by corresponding limits. The developed algorithm effectively traverses all extreme points in the feasible space and establishes the graph structure reflecting the active constraints and their connectivity. The algorithm demarcates basic and nonbasic variables at each extreme point, which is exploited to traverse the active constraints and merge the degenerate extreme points. Finally, a random model generator is presented with the capability to control the matrix sparseness and the model degeneracy for an arbitrary number of decision variables and performance measures. The results indicate that all these model properties are significant factors which affect the total number of extreme points, their connected graph, and the global feasibility.     

A Sequential Scheme for Estimating Average Quality in a Mixed Product Production Process

A production process is assumed to produce k types of items in varying proportions and with varying defect rates. This paper proposes a sequential inspection plan which allows one to optimally allocate inspection resources so that one obtains a good estimate of average quality. The plan is fairly easy to put into practice and allows for easy modification should production characteristics change.     

Flight path optimization passing through waypoints for autonomous flight control systems

Trajectory optimization is performed to generate a flight path passing specified waypoints. To deal with the unspecified time of passing through a waypoint, an auxiliary variable is introduced. Normalization of the time variable by the auxiliary variable transforms the waypoint optimization problem into the conventional optimization problem. The condition for passing through the waypoints can be relaxed, so that the vehicle passes specified waypoints within a certain acceptable range. Sequential quadratic programming is used to solve the optimization problem. As a numerical example, six degree-of-freedom vehicle dynamics is considered. Two-dimensional and three-dimensional trajectory optimization problems with several waypoints are solved to verify the effectiveness of the proposed algorithm.     

Measuring and comparing the distribution of decision-making power in regulatory arrangements of the telecommunication sector in Latin America

In this paper, I measure and compare the distribution of decision-making power in regulatory arrangements in the telecommunication sectors of four Latin American countries. In particular, I measure the coordination of the decision-making process and the concentration of regulatory influence. Additionally, I measure the relative influence of each actor involved in the regulatory arrangement. To perform these measurements, I introduce, refine and apply a recently developed methodology. I found that there are significant differences across countries and across types of regulation. Furthermore, I found that the sector regulator is not necessarily the most important actor in every country.     

Conformance and nonconformance in segmentation-free X-ray computed tomography geometric inspection

Additive Manufacturing (AM) is changing the manufacturing paradigm as it makes it possible to generate complex geometries that are impossible using conventional technologies. However, conventional GPS/GD&T practices are inadequate both at specifying and verifying geometric tolerances. In both cases, they lack the required flexibility. Applying volumetric instead of surface representations helps to solve the problem of specifying tolerances and coheres with topological optimization. The verification paradigm must be modified, too, as AM allows an increase in part complexity without a corresponding increase of cost. Among measurement techniques, only X-ray computed tomography (XCT), which is volumetric, is capable of easily measure complex parts. Leaving the discussion of volumetric tolerance specifications to the future, the aim of this work is exploring a part geometric accuracy verification by direct comparison between its nominal geometry and geometric tolerance volumetric representation, and an XCT volumetric image of it. Unlike the conventional use of XCT for geometric verification, this is a segmentation-free verification. The method is based on the “mutual information” of the two, i.e. information shared by the measured and nominal representations. The output is a conformance statement that does rely on a measurement but nor on a specific measured value not rely on a measurement result. This makes defining a decision rule considering consumer's and producer's risks difficult: uncertainty does not exist in this case. Statistic and simulation techniques make it possible to estimate these risks, defining a numerical model of the distribution of the gray values in a specific portion of the XCT image. Finally, an additive manufacturing case study validates the methodology.     

Discrete-time mixed-integer programming models and solution methods for production scheduling in multistage facilities

We address the problem of production scheduling in multi-product multi-stage batch plants. Unlike most of the previous works, which propose continuous-time models, we study discrete-time mixed-integer programming models and solution methods. Specifically, we discuss two models based on network representations of the facility and develop two new models inspired by the Resource-Constrained Project Scheduling Problem. Furthermore, we propose different solution methods, including tightening methods based on processing unit availability, a reformulation based on processing unit occupancy, and an algorithm to refine approximate solutions for large-scale instances. Finally, we present a comprehensive computational study which shows that speedups of up to four orders of magnitude in are observed when our models and methods are compared to existing approaches.     

A novel approach to improve the planning of adaptive and interactive sessions for the treatment of Major Depression

Human Computer Interaction (HCI) is a research field which aims to improve the relationship between users and interactive computer systems. A main objective of this research area is to make the user experience more pleasant and efficient, minimizing the barrier between the users׳ cognition of what they want to accomplish and the computer׳s understanding of the user׳s tasks, by means of user-friendly, useful and usable designs. A bad HCI design is one of the main reasons behind user rejection of computer-based applications, which in turn produces loss of productivity and economy in industrial environments.In the eHealth domain, user rejection of computer-based systems is a major barrier to exploiting the maximum benefit from those applications developed to support the treatment of diseases, and in the worst cases a poor design in these systems may cause deterioration in the clinical condition of the patient. Thus, a high level of personalisation of the system according to users׳ needs is extremely important, making it easy to use and contributing to the system׳s efficacy, which in turn facilitates the empowerment of the target users. Ideally, the content offered through the interactive sessions in these applications should be continuously assessed and adapted to the changing condition of the patient. A good HCI design and development can improve the acceptance of these applications and contribute to promoting better adherence levels to the treatment, preventing the patient from further relapses.In this work, we present a mechanism to provide personalised and adaptive daily interactive sessions focused on the treatment of patients with Major Depression. These sessions are able to automatically adapt the content and length of the sessions to obtain personalised and varied sessions in order to encourage the continuous and long-term use of the system. The tailored adaptation of session content is supported by decision-making processes based on: (i) clinical requirements; (ii) the patient’s historical data; and (iii) current responses from the patient. We have evaluated our system through two different methodologies: the first one performing a set of simulations producing different sessions from changing input conditions, in order to assess different levels of adaptability and variability of the session content offered by the system. The second evaluation process involved a set of patients who used the system for 14–28 days and answered a questionnaire to provide feedback about the perceived level of adaptability and variability produced by the system. The obtained results in both evaluations indicated good levels of adaptability and variability in the content of the sessions according to the input conditions.