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Abstract PurposeAs a challenging but important optimization problem, the inverse planning for volumetric modulated arc therapy (VMAT) has attracted much research attention. The column generation (CG) type method is so far one of the most effective solution schemes. However, it often relies on simplifications leading to significant gaps between the output and the actual feasible plan. This paper presents a novel column generation (NCG) approach to push the planning results substantially closer to practice. MethodsThe proposed NCG algorithm is equipped with multiple new quality‐enhancing and computation‐facilitating modules as below: (1) Flexible constraints are enabled on both dose rates and treatment time to adapt to machine capabilities as well as planner's preferences, respectively; (2) a cross‐control‐point intermediate aperture simulation is incorporated to better conform to the underlying physics; (3) new pricing and pruning subroutines are adopted to achieve better optimization outputs. To evaluate the effectiveness of this NCG, five VMAT plans, that is, three prostate cases and two head‐and‐neck cases, were computed using proposed NCG. The planning results were compared with those yielded by a historical benchmark planning scheme. ResultsThe NCG generated plans of significantly better quality than the benchmark planning algorithm. For prostate cases, NCG plans satisfied all planning target volume (PTV) criteria whereas CG plans failed on D10% criteria of PTVs for over 9 Gy or more on all cases. For head‐and‐neck cases, again, NCG plans satisfied all PTVs criteria while CG plans failed on D10% criteria of PTVs for over 3 Gy or more on all cases as well as the max dose criteria of both cord and brain stem for over 13 Gy on one case. Moreover, the pruning scheme was found to be effective in enhancing the optimization quality. ConclusionsThe proposed NCG inherits the computational advantages of the traditional CG, while capturing a more realistic characterization of the machine capability and underlying physics. The output solutions of the NCG are substantially closer to practical implementation.
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How to Assess Uncertainty-Aware Frameworks for Power System Planning?
Computational advances along with the profound impact of uncertainty on power system investments have motivated the creation of power system planning frameworks that handle long-run uncertainty, large number of alternative plans, and multiple objectives. Planning agencies seek guidance to assess such frameworks. This article addresses this need in two ways. First, we augment previously proposed criteria for assessing planning frameworks by including new criteria such as stakeholder acceptance to make the assessments more comprehensive, while enhancing the practical applicability of assessment criteria by offering criterion-specific themes and questions. Second, using the proposed criteria, we compare two widely used but fundamentally distinct frameworks: an ‘agree-on-plans’ framework, Robust Decision Making (RDM), and an ‘agree-on-assumptions’ framework, centered around Stochastic Programming (SP). By comparing for the first time head-to-head the two distinct frameworks for an electricity supply planning problem under uncertainties in Bangladesh, we conclude that RDM relies on a large number of simulations to provide ample information to decision makers and stakeholders, and to facilitate updating of subjective inputs. In contrast, SP is a highly dimensional optimization problem that identifies plans with relatively good probability-weighted performance in a single step, but even with computational advances remains subject to the curse of dimensionality.
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- Award ID(s):
- 2330450
- PAR ID:
- 10558323
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Energy Markets, Policy and Regulation
- ISSN:
- 2771-9626
- Page Range / eLocation ID:
- 1 to 13
- Subject(s) / Keyword(s):
- Robust Decision Making, Stochastic Programming, Power System Planning, Deep Uncertainty, Risk Analysis
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TEMPR.2024.3365977
