Two-dimensional Pareto frontier forecasting for technology planning and roadmapping

Technology evolution forecasting based on historical data processing is a useful tool for quantitative analysis in technology planning and roadmapping. While previous efforts focused mainly on one-dimensional forecasting, real technical systems require the evaluation of multiple and conflicting figures of merit at the same time, such as cost and performance. This paper presents a methodology for technology forecasting based on Pareto (efficient) frontier estimation algorithms and multiple regressions in presence of at least two conflicting figures of merits. A tool was developed on the basis of the approach presented in this paper. The methodology is illustrated with a case study from the automotive industry. The paper also shows the validation of the methodology and the estimation of the forecast accuracy adopting a backward testing procedure

METHODS OF CHOICE THE SOFTWARE TESTING AND ALGORITHMIC REDUNDANCY PARAMETERS TO ACHIEVE RELIABILITY REQUIREMENTS FOR THE REAL-TIME PROCESSING SYSTEMS

We propose methods for the evaluation software reliability parameter of the real-time processing systems at complex application software testing and algorithmic redundancy. A formulation of the problem of choice the software testing and algorithmic redundancy parameters for achieving reliability requirements for the realtime processing systems is given as a problem of the mixed (integer and continuous) programming with the nonlinear constraints. The problem is solved by brute-force and mesh adaptive direct search algorithms

Comparative analysis of two-dimensional data-driven efficient frontier estimation algorithms

In this paper we show how the mathematical apparatus developed originally in the field of econometrics and portfolio optimization can be utilized for purposes of conceptual design, requirements engineering and technology roadmapping. We compare popular frontier estimation models and propose an efficient and robust nonparametric estimation algorithm for twodimensional frontier approximation. The proposed model allows to relax the convexity assumptions and thus enable estimating a broader range of possible technology frontier shapes compared to the state of the art. Using simulated datasets we show how the accuracy and the robustness of alternative methods such as Data Envelopment Analysis and nonparametric and parametric statistical models depend on the size of the dataset and on the shape of the frontier

Model-based approaches for technology planning and roadmapping: Technology forecasting and game-theoretic modeling

This paper proposes a novel model-based approach to technology planning and roadmapping, consisting of two complementary steps: technology forecasting and game-theoretic planning. The inherent uncertainty of target technology performances, timelines and risks impact the roadmapping process. Reducing this uncertainty is a major challenge and allows elaborating different options and scenarios. A formal methodology is proposed for quantitative forecasting in a multi-dimensional space (different performance metrics and time) based on past technology development trend information. The method adopts concepts and approaches from econometrics and is formulated as a convex optimization problem with different constraints on the frontier’s shape. It provides useful product line assessment benchmarks and helps to set reasonable goals for future technology developments. Game-theoretic planning allows addressing the strategic decisions to take, considering the technology land-scape, markets, and competition. The strategic decisions affect in turn other companies as well, which is the basis for the application of game theory, in the form of best-response functions to determine the subsequent reactions and movements of rivals in a technological landscape. The result is a simulation of a sequential game in tech-nology space, allowing evaluating possible technological development pathways and determining optimal models on the Pareto frontiers, potential targets for technology roadmapping

Two-dimensional Pareto frontier forecasting for technology planning and roadmapping

International audienceTechnology evolution forecasting based on historical data processing is a useful tool for quantitative analysis in technology planning and roadmapping. While previous efforts focused mainly on one-dimensional forecasting, real technical systems require the evaluation of multiple and conflicting figures of merit at the same time, such as cost and performance. This paper presents a methodology for technology forecasting based on Pareto (efficient) frontier estimation algorithms and multiple regressions in presence of at least two conflicting figures ofmerits. A tool was developed on the basis of the approach presented in this paper. The methodology is illustrated with a case study from the automotive industry. The paper also shows the validation of the methodology and the estimation of the forecast accuracy adopting a backward testing procedure

A discrete-event simulation model for driver performance assessment: application to autonomous vehicle cockpit design optimization

International audienceThe latest advances in the design of vehicles with the adaptive level of automation pose new challenges in the vehicle-driver interaction. Safety requirements underline the need to explore optimal cockpit architectures with regard to driver cognitive and perceptual workload, eyes-off-the-road time and situation awareness. We propose to integrate existing task analysis approaches into system architecture evaluation for the early-stage design optimization. We built the discrete-event simulation tool and applied it within the multi-sensory (sight, sound, touch) cockpit design industrial project