Jeffrey Larson

A Distributed Framework for Coordinated Heavy-Duty Vehicle Platooning

Heavy-duty vehicles (HDVs) traveling in single file with small intervehicle distances experience reduced aerodynamic drag and, therefore, have improved fuel economy. In this paper, we attempt to maximize the amount of fuel saved by coordinating platoon formation using a distributed network of controllers. These virtual controllers, placed at major intersections in a road network, help coordinate the velocity of approaching vehicles so they arrive at the junction simultaneously and can therefore platoon. This control is initiated only if the cost of forming the platoon is smaller than the savings incurred from platooning. In a large-scale simulation of the German Autobahn network, we observe that savings surpassing 5% when only a few thousand vehicles participate in the system. These results are corroborated by an analysis of real-world HDV data that show significant platooning opportunities currently exist, suggesting that a slightly invasive network of distributed controllers, such as the one proposed in this paper, can yield considerable savings.

Coordinated route optimization for heavy-duty vehicle platoons

Heavy-duty vehicles traveling in platoons consume fuel at a reduced rate. In this paper, we attempt to maximize this benefit by introducing local controllers throughout a road network to facilitate platoon formations with minimal information. By knowing a vehicles position, speed, and destination, the local controller can quickly decide how its speed should be possibly adjusted to platoon with others in the near future. We solve this optimal control and routing problem exactly for small numbers of vehicles, and present a fast heuristic algorithm for real-time use. We then implement such a distributed control system through a large-scale simulation of the German autobahn road network containing thousands of vehicles. The simulation shows fuel savings from 1-9%, with savings exceeding 5% when only a few thousand vehicles participate in the system. We assume no vehicles will travel more than the time required for their shortest paths for the majority of the paper. We conclude the results by analyzing how a relaxation of this assumption can further reduce fuel use.

Derivative-Free Optimization of Expensive Functions with Computational Error Using Weighted Regression

We propose a derivative-free algorithm for optimizing computationally expensive functions with computational error. The algorithm is based on the trust region regression method by Conn, Scheinberg, and Vicente [A. R. Conn, K. Scheinberg, and L. N. Vicente, IMA J. Numer. Anal., 28 (2008), pp. 721--748] but uses weighted regression to obtain more accurate model functions at each trust region iteration. A heuristic weighting scheme is proposed that simultaneously handles (i) differing levels of uncertainty in function evaluations and (ii) errors induced by poor model fidelity. We also extend the theory of

Stochastic derivative-free optimization using a trust region framework

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Constructing schedules for sports leagues with divisional and round-robin tournaments

-poisedness and strong

Manifold Sampling for

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-poisedness to weighted regression. We report computational results comparing interpolation, regression, and weighted regression methods on a collection of benchmark problems. Weighted regression appears to outperform interpolation and regression models on nondifferentiable functions and functions with deterministic noise.

Nonconvex Optimization

This paper presents a trust region algorithm to minimize a function f when one has access only to noise-corrupted function values

Scheduling double round-robin tournaments with divisional play using constraint programming

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