The Experts below are selected from a list of 129 Experts worldwide ranked by ideXlab platform

Sonia Martinez - One of the best experts on this subject based on the ideXlab platform.

  • Data-driven Variable Speed Limit Design with Performance Guarantees for Highways
    arXiv: Optimization and Control, 2019
    Co-Authors: Dariush Fooladivanda, Sonia Martinez
    Abstract:

    This paper studies the data-driven Design of variable speed Limits for highways subject to uncertainty, including unknown driver actions as well as vehicle arrivals and departures. With accessibility to sample measurements of the uncertain variables, we aim to find the set of speed Limits that prevents traffic congestion and an optimum vehicle throughput with high probability. This results into the formulation of a stochastic optimization problem (P), which is intractable due to the unknown distribution of the uncertainty variables. By developing a distributionally robust optimization framework, we present an equivalent and yet tractable reformulation of (P). Further, we propose an efficient algorithm that provides suboptimal data-driven solutions and guarantees congestion-free conditions with high probability. We employ the resulting control method on a traffic simulator to illustrate the effectiveness of this approach.

  • data driven variable speed Limit Design for highways via distributionally robust optimization
    European Control Conference, 2019
    Co-Authors: Dariush Fooladivanda, Sonia Martinez
    Abstract:

    This paper introduces an optimization problem and a solution strategy to Design variable-speed-Limit controls for a highway that is subject to traffic congestion and uncertain vehicle arrivals and departures. By employing a finite data-set of samples of the uncertain variables, we find a data-driven solution that has a guaranteed out-of-sample performance. In principle, such formulation leads to an intractable problem as the distribution of the uncertainty variable is unknown. By adopting a distributionally robust optimization approach, this work presents a tractable reformulation and an efficient algorithm that provides a suboptimal solution retaining the out-of-sample performance guarantee. Finally, we demonstrate the effectiveness of our algorithm numerically.

  • ECC - Data-Driven Variable Speed Limit Design for Highways via Distributionally Robust Optimization
    2019 18th European Control Conference (ECC), 2019
    Co-Authors: Dariush Fooladivanda, Sonia Martinez
    Abstract:

    This paper introduces an optimization problem and a solution strategy to Design variable-speed-Limit controls for a highway that is subject to traffic congestion and uncertain vehicle arrivals and departures. By employing a finite data-set of samples of the uncertain variables, we find a data-driven solution that has a guaranteed out-of-sample performance. In principle, such formulation leads to an intractable problem as the distribution of the uncertainty variable is unknown. By adopting a distributionally robust optimization approach, this work presents a tractable reformulation and an efficient algorithm that provides a suboptimal solution retaining the out-of-sample performance guarantee. Finally, we demonstrate the effectiveness of our algorithm numerically.

  • data driven variable speed Limit Design for highways via distributionally robust optimization
    arXiv: Optimization and Control, 2018
    Co-Authors: Dariush Fooladivanda, Sonia Martinez
    Abstract:

    This paper introduces an optimization problem (P) and a solution strategy to Design variable-speed-Limit controls for a highway that is subject to traffic congestion and uncertain vehicle arrival and departure. By employing a finite data-set of samples of the uncertain variables, we aim to find a data-driven solution that has a guaranteed out-of-sample performance. In principle, such formulation leads to an intractable problem (P) as the distribution of the uncertainty variable is unknown. By adopting a distributionally robust optimization approach, this work presents a tractable reformulation of (P) and an efficient algorithm that provides a suboptimal solution that retains the out-of-sample performance guarantee. A simulation illustrates the effectiveness of this method.

Mohammad Waqar Ali Asad - One of the best experts on this subject based on the ideXlab platform.

  • Production phase and ultimate pit Limit Design under commodity price uncertainty
    European Journal of Operational Research, 2016
    Co-Authors: Snehamoy Chatterjee, Manas Ranjan Sethi, Mohammad Waqar Ali Asad
    Abstract:

    Open pit mine Design optimization under uncertainty is one of the most critical and challenging tasks in the mine planning process. This paper describes the implementation of a minimum cut network flow algorithm for the optimal production phase and ultimate pit Limit Design under commodity price or market uncertainty. A new smoothing splines algorithm with sequential Gaussian simulation generates multiple commodity price scenarios, and a computationally efficient stochastic framework accommodates the joint representation and processing of the mining block economic values that result from these commodity price scenarios. A case study at an existing iron mining operation demonstrates the performance of the proposed method, and a comparison with conventional deterministic approach shows a higher cumulative metal production coupled with a 48% increase in the net present value (NPV) of the operation.

Dariush Fooladivanda - One of the best experts on this subject based on the ideXlab platform.

  • Data-driven Variable Speed Limit Design with Performance Guarantees for Highways
    arXiv: Optimization and Control, 2019
    Co-Authors: Dariush Fooladivanda, Sonia Martinez
    Abstract:

    This paper studies the data-driven Design of variable speed Limits for highways subject to uncertainty, including unknown driver actions as well as vehicle arrivals and departures. With accessibility to sample measurements of the uncertain variables, we aim to find the set of speed Limits that prevents traffic congestion and an optimum vehicle throughput with high probability. This results into the formulation of a stochastic optimization problem (P), which is intractable due to the unknown distribution of the uncertainty variables. By developing a distributionally robust optimization framework, we present an equivalent and yet tractable reformulation of (P). Further, we propose an efficient algorithm that provides suboptimal data-driven solutions and guarantees congestion-free conditions with high probability. We employ the resulting control method on a traffic simulator to illustrate the effectiveness of this approach.

  • data driven variable speed Limit Design for highways via distributionally robust optimization
    European Control Conference, 2019
    Co-Authors: Dariush Fooladivanda, Sonia Martinez
    Abstract:

    This paper introduces an optimization problem and a solution strategy to Design variable-speed-Limit controls for a highway that is subject to traffic congestion and uncertain vehicle arrivals and departures. By employing a finite data-set of samples of the uncertain variables, we find a data-driven solution that has a guaranteed out-of-sample performance. In principle, such formulation leads to an intractable problem as the distribution of the uncertainty variable is unknown. By adopting a distributionally robust optimization approach, this work presents a tractable reformulation and an efficient algorithm that provides a suboptimal solution retaining the out-of-sample performance guarantee. Finally, we demonstrate the effectiveness of our algorithm numerically.

  • ECC - Data-Driven Variable Speed Limit Design for Highways via Distributionally Robust Optimization
    2019 18th European Control Conference (ECC), 2019
    Co-Authors: Dariush Fooladivanda, Sonia Martinez
    Abstract:

    This paper introduces an optimization problem and a solution strategy to Design variable-speed-Limit controls for a highway that is subject to traffic congestion and uncertain vehicle arrivals and departures. By employing a finite data-set of samples of the uncertain variables, we find a data-driven solution that has a guaranteed out-of-sample performance. In principle, such formulation leads to an intractable problem as the distribution of the uncertainty variable is unknown. By adopting a distributionally robust optimization approach, this work presents a tractable reformulation and an efficient algorithm that provides a suboptimal solution retaining the out-of-sample performance guarantee. Finally, we demonstrate the effectiveness of our algorithm numerically.

  • data driven variable speed Limit Design for highways via distributionally robust optimization
    arXiv: Optimization and Control, 2018
    Co-Authors: Dariush Fooladivanda, Sonia Martinez
    Abstract:

    This paper introduces an optimization problem (P) and a solution strategy to Design variable-speed-Limit controls for a highway that is subject to traffic congestion and uncertain vehicle arrival and departure. By employing a finite data-set of samples of the uncertain variables, we aim to find a data-driven solution that has a guaranteed out-of-sample performance. In principle, such formulation leads to an intractable problem (P) as the distribution of the uncertainty variable is unknown. By adopting a distributionally robust optimization approach, this work presents a tractable reformulation of (P) and an efficient algorithm that provides a suboptimal solution that retains the out-of-sample performance guarantee. A simulation illustrates the effectiveness of this method.

Snehamoy Chatterjee - One of the best experts on this subject based on the ideXlab platform.

  • Production phase and ultimate pit Limit Design under commodity price uncertainty
    European Journal of Operational Research, 2016
    Co-Authors: Snehamoy Chatterjee, Manas Ranjan Sethi, Mohammad Waqar Ali Asad
    Abstract:

    Open pit mine Design optimization under uncertainty is one of the most critical and challenging tasks in the mine planning process. This paper describes the implementation of a minimum cut network flow algorithm for the optimal production phase and ultimate pit Limit Design under commodity price or market uncertainty. A new smoothing splines algorithm with sequential Gaussian simulation generates multiple commodity price scenarios, and a computationally efficient stochastic framework accommodates the joint representation and processing of the mining block economic values that result from these commodity price scenarios. A case study at an existing iron mining operation demonstrates the performance of the proposed method, and a comparison with conventional deterministic approach shows a higher cumulative metal production coupled with a 48% increase in the net present value (NPV) of the operation.

J.r.jagannatha Rao - One of the best experts on this subject based on the ideXlab platform.

  • New models for optimal truss topology in Limit Design based on unified elastic/plastic analysis
    Computer Methods in Applied Mechanics and Engineering, 1997
    Co-Authors: R. Muralidhar, J.r.jagannatha Rao
    Abstract:

    Abstract This paper presents several equivalent formulations for a structural Design problem where the load-carrying capacity is maximized for a prescribed volume subject to bounds on complementary energy and stresses. This Limit Design model covers the full range of strictly elastic, elastic/plastic and strictly plastic Designs and is based on the unified analysis model of Ben-Tal and Taylor [1]. While this Design model is convex, it is nonlinearly constrained and is of a very high dimension for topology Design problems. Application of duality principles leads to several simpler but nonsmooth equivalent models. In particular, for the case when the Design variables do not have explicit bounds, the dual models reduce to a minimization, subject to a single linear constraint, of a pointwise maximum of a finite number of convex functions. More importantly, these simpler Design models are of greatly reduced size, since they contain only nodal variables. Further, the two cases of strictly plastic as well as strictly elastic Limit Design models can be reduced to linear programs both of which, unexpectedly, are shown to be equivalent to the more widely studied model for minimum compliance topology Design of elastic trusses. Several numerical examples illustrate the usefulness of these new dual formulations.

  • MULTILEVEL FORMULATIONS IN THE Limit ANALYSIS AND Design OF STRUCTURES WITH BILATERAL CONTACT CONSTRAINTS
    International Journal for Numerical Methods in Engineering, 1996
    Co-Authors: R. Muralidhar, J.r.jagannatha Rao, K. Badhrinath, A. Kalagatla
    Abstract:

    In this paper, we study the rich class of formulations that arise in the Limit analysis and Design of elastic/plastic structures in the presence of contact constraints. It is well-known that in the absence of contacts, both the Limit analysis and Limit Design problems can be written as linear programs. However, when contact constraints are present, the structure effectively exhibits both softening and stiffening behaviour under monotonically increasing loading. The resulting Limit analysis and Limit Design problems are non-convex and are difficult to solve due to the presence of complementary type of equality constraints. We show that by using a mixed form of the minimum principle, we can restate the Limit analysis and Limit Design problems as two- and three-level formulations, respectively. Further, under a strong assumption on the problem and solution data, we can take advantage of the underlying convexity to reduce both these multilevel formulations to equivalent linear programs. While it may not be possible to always verify this assumption in practice, we show that a two-step iterative procedure is effective in reaching a solution to the Limit Design problem.