Dataset Information

Real-time energy-saving metro train rescheduling with primary delay identification.

ABSTRACT: This paper aims to reschedule online metro trains in delay scenarios. A graph representation and a mixed integer programming model are proposed to formulate the optimization problem. The solution approach is a two-stage optimization method. In the first stage, based on a proposed train state graph and system analysis, the primary and flow-on delays are specifically analyzed and identified with a critical path algorithm. For the second stage a hybrid genetic algorithm is designed to optimize the schedule, with the delay identification results as input. Then, based on the infrastructure data of Beijing Subway Line 4 of China, case studies are presented to demonstrate the effectiveness and efficiency of the solution approach. The results show that the algorithm can quickly and accurately identify primary delays among different types of delays. The economic cost of energy consumption and total delay is considerably reduced (by more than 10% in each case). The computation time of the Hybrid-GA is low enough for rescheduling online. Sensitivity analyses further demonstrate that the proposed approach can be used as a decision-making support tool for operators.

SUBMITTER: Huang H

PROVIDER: S-EPMC5825068 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Real-time energy-saving metro train rescheduling with primary delay identification.

Huang Hangfei H Li Keping K Schonfeld Paul P

PloS one 20180223 2

This paper aims to reschedule online metro trains in delay scenarios. A graph representation and a mixed integer programming model are proposed to formulate the optimization problem. The solution approach is a two-stage optimization method. In the first stage, based on a proposed train state graph and system analysis, the primary and flow-on delays are specifically analyzed and identified with a critical path algorithm. For the second stage a hybrid genetic algorithm is designed to optimize the ...[more]

PMID: 29474471

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Project description:Reduction of radiation dose in x-ray imaging has been recognized as a high priority in the medical community. Here the authors show that a regional adaptive exposure method can reduce dose-area product (DAP) in x-ray fluoroscopy. The authors' method is particularly geared toward providing dose savings for the pediatric population.The scanning beam digital x-ray system uses a large-area x-ray source with 8000 focal spots in combination with a small photon-counting detector. An imaging frame is obtained by acquiring and reconstructing up to 8000 detector images, each viewing only a small portion of the patient. Regional adaptive exposure was implemented by varying the exposure of the detector images depending on the local opacity of the object. A family of phantoms ranging in size from infant to obese adult was imaged in anteroposterior view with and without adaptive exposure. The DAP delivered to each phantom was measured in each case, and noise performance was compared by generating noise arrays to represent regional noise in the images. These noise arrays were generated by dividing the image into regions of about 6 mm(2), calculating the relative noise in each region, and placing the relative noise value of each region in a one-dimensional array (noise array) sorted from highest to lowest. Dose-area product savings were calculated as the difference between the ratio of DAP with adaptive exposure to DAP without adaptive exposure. The authors modified this value by a correction factor that matches the noise arrays where relative noise is the highest to report a final dose-area product savings.The average dose-area product saving across the phantom family was (42 ± 8)% with the highest dose-area product saving in the child-sized phantom (50%) and the lowest in the phantom mimicking an obese adult (23%).Phantom measurements indicate that a regional adaptive exposure method can produce large DAP savings without compromising the noise performance in the image regions with highest noise.

Dataset Information

Real-time energy-saving metro train rescheduling with primary delay identification.

Publications

Real-time energy-saving metro train rescheduling with primary delay identification.

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