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A review on airport gate assignment problems: Single versus multi objective approaches

OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2020年第0期92卷 102146-000页

作者： Das, Gulesin Sena Gzara, Fatma Stutzle, Thomas Kirikkale Univ Dept Ind Engn Kirikkale Turkey Univ Waterloo Dept Management Sci Waterloo ON Canada ULB IRIDIA Brussels Belgium

Assigning aircraft to gates is an important decision problem that airport professionals face every day. The solution of this problem has raised a significant research effort and many variants of this problem have been studied. In this paper, we review past work with a focus on identifying types of formulations, classifying objectives, and categorising solution methods. The review indicates that there is no standard formulation, that passenger oriented objectives are most common, and that more recent work are multi-objective. In terms of solution methods, heuristic and metaheuristic approaches are dominant which provides an opportunity to develop exact and approximate approaches both for the single and multi-objective problems. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Gate assignment problem objective functions Multi-objective problem

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Risk-based many-objective configuration of power system fault current limiters utilising NSGA-III

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第23期14卷 5646-5654页

作者： Guo, Chao Ye, Chengjin Ding, Yi Lin, Zhenzhi Wang, Peng Zhejiang Univ Dept Elect Engn Hangzhou 310058 Zhejiang Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Nanyang Ave Singapore 638798 Singapore

The fault current limiter (FCL) has been envisaged as a feasible solution to defend against out-of-range short-circuit currents (SCCs) in power systems. However, the existing studies mainly sited and sized FCLs under deterministic fault scenarios. Moreover, the influences of FCLs on power systems are complex including dynamic aspects, which makes it extremely difficult to configure FCLs optimally. This paper proposes a risk-based many-objective FCL configuration framework. Specifically, a data-driven Proportional Hazard Model (PHM) is developed to evaluate the nodal long-term short-circuit probability considering equipment aging, weather statistics, and surrounding conditions. The risks in terms of out-of-range SCC, abnormal voltage sag, and transient instability are formulated as the objective functions of the FCL configuration model, as well as the overall FCL investment. The time-domain dynamic response process of FCLs is considered in both the SCC limiting and the transient stability simulation process, mainly in the form of a time-varying resistance. The state-of-the-art NSGA-III is deployed to search the Pareto-optimal FCL configuration solutions of the established many-objective FCL configuration problem. The numerical results verify that the risk-based model configures FCLs more economically with a 7.5% decrease of average FCL investment compared with the deterministic model.

关键词： power grids power system transient stability Pareto optimisation power supply quality fault current limiters power system faults genetic algorithms risk-based many-objective configuration deterministic fault scenarios short-circuit faults diversified functions risk-based many-objective FCL configuration framework data-driven proportional hazard model nodal long-term short-circuit probability out-of-range SCC objective functions FCL configuration model time-domain dynamic response process SCC limiting transient stability simulation process nondominated sorting genetic algorithm III Pareto-optimal FCL configuration solutions many-objective FCL configuration problem IEEE 39-bus system risk-based model average FCL investment deterministic model power system fault current limiters NSGA-III equipment ageing weather statistics surrounding conditions abnormal voltage sag transient instability time-varying resistance Eastern China Grid data

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Diblock Sodium Alginate Grafted Poly (N-vinylimidazole) in blank copolymeric beads and immobilized algal beads for water treatment

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2020年 153卷 603-612页

作者： Fares, Mohammad M. Abu Al-Rub, Fahmi A. Talafha, Tareq Jordan Univ Sci & Technol Fac Sci & Arts Dept Chem Sci POB 3030 Irbid 22110 Jordan Jordan Univ Sci & Technol Dept Chem Engn POB 3030 Irbid 22110 Jordan

Synthesis of diblock sodium alginate grafted poly (N-vinylimidazole), SA-g-PNVI, was investigated as preliminary step toward preparation of blank copolymeric beads (BCB) and immobilized algal beads (IAB) for the removal of phenol from aqueous solutions. The diblock SA-g-PNVI was self-crosslinked via calcium ions to make BCB, and crosslinked with dead green algae (Chlorella pyrenoidosa) to make the IAB. The diblock SA-g-PNVI was fully characterized using spectroscopic, thermal and optical means. Adsorption optimization parameters like effect of solution pH, dynamics, and adsorption isotherms were investigated;Optimum pH value for phenol removal was found at pH = 5.0 for blank copolymer beads (BCB) and immobilized algal beads (IAB). The adsorption kinetics demonstrated pseudo-second order phenol removal with major contribution of intraparticle diffusion mechanism. Adsorption isotherms were fitted to equilibrium data in linear and nonlinear regressions using different objective functions. The low cost, natural origin, biodegradability and environmentally friendly characteristics of the diblock SA-g-PNVI sorbents adapt them to play promising role in recovery of missing ecology and in wastewater management. (C) 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Diblock sorbents SA-g-PNVI Phenol removal Kinetics Linear and nonlinear regressions objective functions

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Direct optimization of dose-volume histogram metrics in radiation therapy treatment planning

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BIOMEDICAL PHYSICS & ENGINEERING EXPRESS 2020年第6期6卷 065018-065018页

作者： Zhang, Tianfang Bokrantz, Rasmus Olsson, Jimmy KTH Royal Inst Technol Dept Math Math Stat SE-10044 Stockholm Sweden RaySearch Labs Sveavagen 44 SE-10365 Stockholm Sweden

We present a method of directly optimizing on deviations in clinical goal values in radiation therapy treatment planning. Using a new mathematical framework in which metrics derived from the dose-volume histogram are regarded as functionals of an auxiliary random variable, we are able to obtain volume-at-dose and dose-at-volume as infinitely differentiable functions of the dose distribution with easily evaluable function values and gradients. Motivated by the connection to risk measures in finance, which is formalized in this framework, we also derive closed-form formulas for mean-tail-dose and demonstrate its capability of reducing extreme dose values in tail distributions. Numerical experiments performed on a prostate and a head-and-neck patient case show that the direct optimization of dose-volume histogram metrics produced marginally better results than or outperformed conventional planning objectives in terms of clinical goal fulfilment, control of low- and high-dose tails of target distributions and general plan quality defined by a pre-specified evaluation measure. The proposed framework eliminates the disconnect between optimization functions and evaluation metrics and may thus reduce the need for repetitive user interaction associated with conventional treatment planning. The method also has the potential of enhancing plan optimization in other settings such as multicriteria optimization and automated treatment planning.

关键词： dose-volume histogram clinical goals mean-tail-dose objective functions smooth approximation inverse planning

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Virtual Populations for Quantitative Systems Pharmacology Models

Virtual Populations for Quantitative Systems Pharmacology Mo...

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作者： Yougan Cheng Ronny Straube Abed E. Alnaif Lu Huang Tarek A. Leil Brian J. Schmidt

Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, autoimmune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a u

关键词： Sensitivity analysis Virtual population Virtual patient In silico trial Virtual patient cohort objective functions Prevalence weight

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Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications

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ENERGY 2021年 231卷 120911-120911页

作者： Ahmad, Tanveer Zhang, Dongdong Huang, Chao Univ Macao State Key Lab Internet Things Smart City Macau 999078 Peoples R China Univ Macao Dept Elect & Comp Engn Macau 999078 Peoples R China Jinan Univ Int Energy Coll Energy & Elect Res Ctr Zhuhai 519070 Guangdong Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 10083 Peoples R China Univ Sci & Technol Beijing Shunde Grad Sch Beijing 528399 Guangdong Peoples R China

Anomalous seasons such as low-wind summers and extremely cold winters can seriously disrupt energy reliability and productivity. Better short/medium- term forecasts that provide reliable and strategic planning insights will allow the energy industry to plan for these extremes. In order to efficiently quantify uncertainty, this study proposes a Gaussian stochastic-based machine learning process model (GPR) for short/medium-term energy, solar, and wind (ESW) power forecasts using two different temporal resolutions of data. Four experimental steps (EXMS) were designed. Each EXMS is designed with four distinct fitting and predicting methods, and the GPR model uses seven kernel covariance functions for hyperparameter optimization. Real-time data is used for the forecasting analysis at three different locations. The forecasting results are validated using three existing models. The percent coefficient of variation of CVGPR1 and CVGPR2 of EXMS-1 and EXMS-3 for ESW power forecasts is 0.017%, 0.057%, 0.025%, and 0.223%, 0.225%, 0.170%, respectively. Accuracy has shown that the proposed model can predict ESW power simultaneously at two different temporal resolution data. The GPR accuracy with four EXMS methodologies is promising by addressing ESW power forecasts under the GPR framework of significant utilities, independent power producers, and public interest. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Renewable energy forecasting Stochastic Gaussian process model Machine learning Artificial neural networks objective functions Multi-objective optimization

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Multiobjective optimization based on self-organizing Particle Swarm Optimization algorithm for massive MIMO 5G wireless network

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第4期34卷 e4725-e4725页

作者： Purushothaman, Kesavalu Elumalai Nagarajan, Velmurugan Adhiparasakthi Engn Coll Dept Elect & Commun Engn Melmaruvathur 603319 India

The development of future fifth-generation (5G) wireless networks (WNs) is an active research area worldwide. The 5G network grants significantly upgraded necessities contrasted with those in present wireless systems. Although massive MIMO (mMIMO) incorporation in WN empowers one to encounter 5G network technical necessities, it should handle different challenges to increase the performance. In this paper, a novel multiple objective self-organizing particle swarm optimizer (SOMPSO) is used to solve multiple objective functions such as user data rate, energy efficiency, spectral efficiency, and average area rate of 5G WN with mMIMO. Furthermore, a fuzzy decision maker is utilized to select a solution vector for getting the best compromising result. Our experimental outputs demonstrate that this SOMPSO is an efficient and promising method to solve multiple objective problems in 5G networks.

关键词： massive MIMO multiple objective optimization objective functions self‐ organizing map spectral efficiency

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Box-Cox sparse measures: A new family of sparse measures constructed from kurtosis and negative entropy

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 160卷 107930-107930页

作者： Wang, Dong Zhong, Jingjing Li, Chuan Peng, Zhike Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Dept Ind Engn & Management Shanghai 200240 Peoples R China Dongguan Univ Technol Coll Mech Engn Dongguan Peoples R China

Sparse measures have attracted lots of interests from many fundamental research domains to be as objective functions of signal processing algorithms, health indices of degradation modeling and input features to machine learning algorithms. Among them, kurtosis and negative entropy are the most two popular sparse measures to characterize the sparsity of signals. For example, kurtosis and negative entropy are used in machine condition monitoring to quantify the sparsity of repetitive transients caused by localized rotating machine faults and to indicate an onset of early rotating faults. When kurtosis and negative entropy are decomposed into the sum of weighted normalized square envelope, the main difference between kurtosis and negative entropy is whether the logarithm transformation is applied to normalized square envelope to form a weight. In this paper, Box-Cox transformation as generalized power transformation is introduced to generalize the weights used in kurtosis and negative entropy and subsequently a new family of sparse measures, coined as Box-Cox sparse measures (BCSM), are proposed. The only parameter in the proposed BCSM is a transformation parameter 2 P 0. The contributions of this paper are summarized as follows. Firstly, this paper provides new propositions for intuitive sparse attributes of the proposed BCSM, which theoretically prove that the proposed BCSM satisfies all six intuitive sparse attributes. Secondly, in numerical and experimental studies, it is shown that (1) the proposed BCSM converges when the length of a signal increases;(2) only when a distribution is quite sparse, the proposed BCSM with 2 > 1 can indicate the sparsity of the distribution. Being different form the performance of the proposed BCSM with 2 > 1, the proposed BCSM with 0 5 2 5 1 can steadily indicate that a distribution is getting sparser, which indicates that negative entropy (2 = 0) is the best choice among all the proposed sparse measures and it is better than kurtosis

关键词： Sparse measures Machine learning Degradation modeling objective functions Health indices Box-Cox transformation

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Conceptual Model Modification and the Millennium Drought of Southeastern Australia

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WATER 2021年第5期13卷 669页

作者： Hughes, Justin Potter, Nick Zhang, Lu Bridgart, Robert CSIRO Land & Water Canberra ACT 2601 Australia CSIRO Land & Water Clayton Vic 3169 Australia

Long-term droughts observed in southern Australia have changed relationships between annual rainfall and runoff and tested some of the assumptions implicit in rainfall-runoff models used in these areas. Predictive confidence across these periods is when low using the more commonly used rainfall-runoff models. Here we modified the GR4J model to better represent surface water-groundwater connection and its role in runoff generation. The modified model (GR7J) was tested in 137 catchments in south-east Australia. Models were calibrated during "wetter" periods and simulation across drought periods was assessed against observations. GR7J performed better than GR4J in evaluation during drought periods where bias was significantly lower and showed improved fit across the flow duration curve especially at low flows. The largest improvements in predictive performance were for catchments where there were larger changes in the annual rainfall-runoff relationship. The predictive performance of the GR7J model was more sensitive to objective function used than GR4J. The use of an objective function that combined daily and annual error produced a better goodness of fit when measured against 80, 50 and 20 percent excedance flow quantiles and reduced evaluation bias, especially for the GR7J model.

关键词： drought rainfall– runoff model non-stationarity groundwater– surface water connection objective functions

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Environmental and Economic Optimization and Sizing of a Micro-Grid with Battery Storage for an Industrial Application

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ENERGIES 2021年第18期14卷 5913页

作者： Khlifi, Fethi Cherif, Habib Belhadj, Jamel Univ Tunis Ecole Natl Super Ingenieurs Tunis 1008 Tunisia Univ Tunis El Manar Ecole Natl Ingenieurs Tunis Tunis 1002 Tunisia

This study focuses on the sizing and optimization of a micro-grid with storage, which is destined to supply the load of an economic activity zone (EAZ) in Sidi Bouzid, Tunisia. To solve this problem, a genetic algorithm is established and programmed into MATLAB. The objective functions are considered by providing three minimums, namely Greenhouse Gas emissions (GHG), Life Cycle Cost (LCC) and Embodied Energy (EE), for three values of loss of power supply probability (LPSP) previously fixed. The sizing and optimization results are found and evaluated using a time series exchange of energy during a year to determine the optimal component size of a photovoltaic/wind/battery system (PV/WT/Bat). The simulation results show that the lowest ratio of LPSP values corresponds to the higher GHG, EE, LCC, photovoltaic panels area (A(PV)), battery storage capacity (C-n), wind turbines area (A(WT)) and vice versa. This means that demanding higher energy reliability leads to higher energy cost and pollution. A comparative analysis was made, showing the cons and pros of each LPSP value in order to allow the owner of the plant to choose the most suitable PV/WT/Bat configuration.

关键词： micro-grid renewable energy PV wind source optimization objective functions

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