Fuzzy random cost–profit tradeoff location model for a vehicle inspection station with regional constraints

Tian, Guangdong; Jia, Hongfei; Li, Zhongkai

doi:10.1002/tee.22305

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…For example, the priority level of a fire point might change when some factors such as wind force, fuel types, and terrain slope vary over the emergency-scheduling horizon, extending the studied problem to a time-dependent one by considering time-dependent priorities of fire points is an important research direction and deserves more and deep study. Effective problem-specific heuristic algorithms should be developed for them [34][35][36][37][38][39][40]. Moreover, we may also extend the proposed model to multiobjective ones by simultaneously considering multiple objectives.…”

Section: Resultsmentioning

confidence: 99%

Resource‐Constrained Emergency Scheduling for Forest Fires with Priority Areas: An Efficient Integer‐Programming Approach

Cheng

Feng

2018

IEEJ Transactions Elec Engng

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This article deals with an emergency‐scheduling problem for forest fires with limited rescue team resources and priority disaster areas. It aims to determine optimal fire‐fighting routes of limited fire‐fighting teams for forest fires with multiple points subject to their priority levels so as to minimize the total travel distance of all rescue teams. For this problem, an integer linear‐programming (ILP) model is developed to generate its optimal schedule scheme, which is exactly solved by commercial software CPLEX. Moreover, the complexity of the problem is proved to be NP‐hard. Computational results show that benchmark and larger‐scale instances (with up to 100 fire points and 30 fire‐fighting teams) can be exactly solved within about 1 min and the proposed approach outperforms the state‐of‐the‐art one in terms of solution quality and computational time. The experimental results suggest that the proposed approach is potentially suitable for facilitating fast decision‐making when dealing with emergency scheduling for forest fires subject to limited rescue resources. Moreover, a new mixed‐integer program is developed to address the resource‐constrained emergency scheduling for forest fires with priority areas to minimize the total rescue time. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Section: Resultsmentioning

confidence: 99%

Resource‐Constrained Emergency Scheduling for Forest Fires with Priority Areas: An Efficient Integer‐Programming Approach

Cheng

Feng

2018

IEEJ Transactions Elec Engng

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“…Multi-information interaction and multi-task collaboration abilities, which mainly include perceptual information, decision making and operational ability, are directly related to driving fatigue. At present, some significant works on intelligent transportation technologies have attracted many concerns [1][2][3][4][5][6][7]. Research on driving fatigue mainly identifies fatigue status using the corresponding changes in physiological indicators such as EEG, electrocardiogram (ECG), electro-myogram (EMG), electrodermal activity (EDA) and so on.…”

Section: Introductionmentioning

confidence: 99%

A Feature Fusion Method for Driving Fatigue of Shield Machine Drivers Based on Multiple Physiological Signals and Auto-Encoder

Feng

Jiang

et al. 2023

Sustainability

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The driving fatigue state of shield machine drivers directly affects the safe operation and tunneling efficiency of shield machines during metro construction. To cope with the problem that it is challenging to simulate the working conditions and operation process of shield machine drivers using driving simulation platforms and that the existing fatigue feature fusion methods usually show low recognition accuracy, shield machine drivers at Shenyang metro line 4 in China were taken as the research subjects, and a multi-modal physiological feature fusion method based on an L2-regularized stacked auto-encoder was designed. First, the ErgoLAB cloud platform was used to extract the combined energy feature (E), the reaction time, the HRV (heart rate variability) time-domain SDNN (standard deviation of normal-to-normal intervals) index, the HRV frequency-domain LF/HF (energy ratio of low frequency to high frequency) index and the pupil diameter index from EEG (electroencephalogram) signals, skin signals, pulse signals and eye movement data, respectively. Second, the physiological signal characteristics were extracted based on the WPT (wavelet packet transform) method and time–frequency analysis. Then, a method for driving fatigue feature fusion based on an auto-encoder was designed aiming at the characteristics of the L2-regularization method to solve the over-fitting problem of small sample data sets in the process of model training. The optimal hyper-parameters of the model were verified with the experimental method of the control variable, which reduces the loss of multi-modal feature data in compression fusion and the information loss rate of the fused index. The results show that the method proposed outperforms its competitors in recognition accuracy and can effectively reduce the loss rate of deep features in existing decision-making-level fusion.

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“…Wang et al [32] deal with the optimal location and arrangement of park-and-ride facilities to maximize their profit and minimize their cost. The works [33], [34] establish fuzzy stochastic programs to optimize the location of vehicle inspection facilities by considering the fuzziness of customer demands. Jia et.…”

Section: Introductionmentioning

confidence: 99%

Determining the Optimal Location of Vehicle Inspection Facilities Under Uncertainty via New Optimization Approaches

Jin

Zhou

et al. 2020

IEEE Access

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This study addresses the problem of optimally locating vehicle inspection facilities under uncertain customer demand and varying velocity considering regional constraints. The objective is to simultaneously minimize the transportation time of all customers and their transportation cost, while ensuring consumers to reach their desired destinations within their expected time and cost. We study two variants of the problem: vehicle inspection location with complete probability distributions of customer demand and vehicle velocity, and that with partial information of customer demand and vehicle velocity, i.e., only the supports and means of the stochastic variables are known. For the former problem, an expected value model and a chance-constrained program are first formulated. Then based on explored problem properties, the expected value program is equivalently reformulated as a deterministic non-linear program. To efficiently deal with the chance-constrained program, a sample average approximation (SAA)-based approach is proposed. For the latter one, we develop a new distribution-free model. Computational results for benchmark examples demonstrate that: i) for the former, the proposed deterministic program reformulation-based approach and SAA algorithm outperform the state-of-the-art approaches; ii) for the latter, the proposed distribution-free model can effectively deal with the problem with partial demand and velocity information.

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Fuzzy random cost–profit tradeoff location model for a vehicle inspection station with regional constraints

Cited by 5 publications

References 52 publications

Resource‐Constrained Emergency Scheduling for Forest Fires with Priority Areas: An Efficient Integer‐Programming Approach

Resource‐Constrained Emergency Scheduling for Forest Fires with Priority Areas: An Efficient Integer‐Programming Approach

A Feature Fusion Method for Driving Fatigue of Shield Machine Drivers Based on Multiple Physiological Signals and Auto-Encoder

Determining the Optimal Location of Vehicle Inspection Facilities Under Uncertainty via New Optimization Approaches

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