Laura Palagi

Personal Details

First Name:	Laura
Middle Name:
Last Name:	Palagi
Suffix:
RePEc Short-ID:	ppa772
	[This author has chosen not to make the email address public]
	http://www.dis.uniroma1.it/~palagi

Affiliation

Dipartimento di Ingegneria Informatica, Automatica e Gestionale "Antonio Ruberti"
Facoltà di Ingegneria dell'Informazione Informatica e Statistica
"Sapienza" Università di Roma

Roma, Italy
http://www.dis.uniroma1.it/
RePEc:edi:dirosit (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

1. Marianna De Santis & Giorgio Grani & Laura Palagi, 2019. "Branching with Hyperplanes in the Criterion Space:the Frontier Partitioner Algorithm for Biobjective Integer Programming," DIAG Technical Reports 2019-03, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
   • De Santis, Marianna & Grani, Giorgio & Palagi, Laura, 2020. "Branching with hyperplanes in the criterion space: The frontier partitioner algorithm for biobjective integer programming," European Journal of Operational Research, Elsevier, vol. 283(1), pages 57-69.

   Cited by:

   1. David Bergman & Merve Bodur & Carlos Cardonha & Andre A. Cire, 2022. "Network Models for Multiobjective Discrete Optimization," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 990-1005, March.
   2. Gang Yao & Rui Li & Yang Yang, 2023. "An Improved Multi-Objective Optimization and Decision-Making Method on Construction Sites Layout of Prefabricated Buildings," Sustainability, MDPI, vol. 15(7), pages 1-23, April.

2. Laura Palagi & Ruggiero Seccia, 2019. "Online Block Layer Decomposition schemes for training Deep Neural Networks," DIAG Technical Reports 2019-06, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".

   Cited by:

   1. Ruggiero Seccia & Daniele Gammelli & Fabio Dominici & Silvia Romano & Anna Chiara Landi & Marco Salvetti & Andrea Tacchella & Andrea Zaccaria & Andrea Crisanti & Francesca Grassi & Laura Palagi, 2020. "Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-18, March.

3. Giorgio Grani & Gianmaria Leo & Laura Palagi & Mauro Piacentini & Hunkar Toyoglu, 2019. "The Sales Based Integer Program for Post-Departure Analysis in Airline Revenue Management: model and solution," DIAG Technical Reports 2019-05, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".

   Cited by:

   1. Neha Gupta & J. K. Sharma, 2020. "Fuzzy multi-objective programming problem for revenue management in food industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(5), pages 349-354, October.

4. Christoph Buchheim & Marianna De Santis & Laura Palagi & Mauro Piacentini, 2012. "An Exact Algorithm for Quadratic Integer Minimization using Nonconvex Relaxations," DIS Technical Reports 2012-05, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".

   Cited by:

   1. Christoph Buchheim & Emiliano Traversi, 2018. "Quadratic Combinatorial Optimization Using Separable Underestimators," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 424-437, August.

5. Immanuel M. Bomze & Luigi Grippo & Laura Palagi, 2010. "Unconstrained formulation of standard quadratic optimization problems," DIS Technical Reports 2010-12, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
   • Immanuel Bomze & Luigi Grippo & Laura Palagi, 2012. "Unconstrained formulation of standard quadratic optimization problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 35-51, April.

   Cited by:

   1. Riccardo Bisori & Matteo Lapucci & Marco Sciandrone, 2022. "A study on sequential minimal optimization methods for standard quadratic problems," 4OR, Springer, vol. 20(4), pages 685-712, December.
   2. Immanuel Bomze & Chen Ling & Liqun Qi & Xinzhen Zhang, 2012. "Standard bi-quadratic optimization problems and unconstrained polynomial reformulations," Journal of Global Optimization, Springer, vol. 52(4), pages 663-687, April.
   3. Dellepiane, Umberto & Palagi, Laura, 2015. "Using SVM to combine global heuristics for the Standard Quadratic Problem," European Journal of Operational Research, Elsevier, vol. 241(3), pages 596-605.
   4. Tatyana Gruzdeva, 2013. "On a continuous approach for the maximum weighted clique problem," Journal of Global Optimization, Springer, vol. 56(3), pages 971-981, July.
   5. Wang, Xing & Tao, Chang-qi & Tang, Guo-ji, 2015. "A class of differential quadratic programming problems," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 369-377.

6. Giampaolo Liuzzi & Laura Palagi & Mauro Piacentini, 2010. "On the convergence of a Jacobi-type algorithm for Singly Linearly-Constrained Problems Subject to simple Bounds," DIS Technical Reports 2010-01, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".

   Cited by:

   1. Andrea Manno & Laura Palagi & Simone Sagratella, 2014. "A Class of Convergent Parallel Algorithms for SVMs Training," DIAG Technical Reports 2014-17, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
   2. Andrea Manno & Laura Palagi & Simone Sagratella, 2018. "Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training," Computational Optimization and Applications, Springer, vol. 71(1), pages 115-145, September.
   3. Amir Beck, 2014. "The 2-Coordinate Descent Method for Solving Double-Sided Simplex Constrained Minimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 162(3), pages 892-919, September.
   4. Andrea Cristofari, 2019. "An almost cyclic 2-coordinate descent method for singly linearly constrained problems," Computational Optimization and Applications, Springer, vol. 73(2), pages 411-452, June.

Articles

1. De Santis, Marianna & Grani, Giorgio & Palagi, Laura, 2020. "Branching with hyperplanes in the criterion space: The frontier partitioner algorithm for biobjective integer programming," European Journal of Operational Research, Elsevier, vol. 283(1), pages 57-69.
   • Marianna De Santis & Giorgio Grani & Laura Palagi, 2019. "Branching with Hyperplanes in the Criterion Space:the Frontier Partitioner Algorithm for Biobjective Integer Programming," DIAG Technical Reports 2019-03, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
   See citations under working paper version above.
2. Regina Lamedica & Alessandro Ruvio & Laura Palagi & Nicola Mortelliti, 2020. "Optimal Siting and Sizing of Wayside Energy Storage Systems in a D.C. Railway Line," Energies, MDPI, vol. 13(23), pages 1-22, November.

   Cited by:

   1. Meishner, Fabian & Ünlübayir, Cem & Sauer, Dirk Uwe, 2023. "Model-based investigation of an uncontrolled LTO wayside energy storage system in a 750 V tram grid," Applied Energy, Elsevier, vol. 331(C).
   2. Paweł Ocłoń & Maciej Ławryńczuk & Marek Czamara, 2021. "A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation," Energies, MDPI, vol. 14(16), pages 1-15, August.
   3. Marcin Szott & Marcin Jarnut & Jacek Kaniewski & Łukasz Pilimon & Szymon Wermiński, 2021. "Fault-Tolerant Control in a Peak-Power Reduction System of a Traction Substation with Multi-String Battery Energy Storage System," Energies, MDPI, vol. 14(15), pages 1-23, July.
   4. Regina Lamedica & Marco Maccioni & Alessandro Ruvio & Federico Carere & Nicola Mortelliti & Fabio Massimo Gatta & Alberto Geri, 2022. "Optimization of e-Mobility Service for Disabled People Using a Multistep Integrated Methodology," Energies, MDPI, vol. 15(8), pages 1-22, April.
   5. Szymon Haładyn, 2021. "The Problem of Train Scheduling in the Context of the Load on the Power Supply Infrastructure. A Case Study," Energies, MDPI, vol. 14(16), pages 1-19, August.

3. Laura Palagi, 2019. "Global optimization issues in deep network regression: an overview," Journal of Global Optimization, Springer, vol. 73(2), pages 239-277, February.

   Cited by:

   1. Liu, Longlong & Zhou, Suyu & Jie, Qian & Du, Pei & Xu, Yan & Wang, Jianzhou, 2024. "A robust time-varying weight combined model for crude oil price forecasting," Energy, Elsevier, vol. 299(C).
   2. Tommaso Colombo & Massimiliano Mangone & Andrea Bernetti & Marco Paoloni & Valter Santilli & Laura Palagi, 2019. "Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis," DIAG Technical Reports 2019-08, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
   3. Laura Palagi & Ruggiero Seccia, 2020. "Block layer decomposition schemes for training deep neural networks," Journal of Global Optimization, Springer, vol. 77(1), pages 97-124, May.
   4. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.

4. Andrea Manno & Laura Palagi & Simone Sagratella, 2019. "Case Article—Production and Distribution Optimization of Beach Equipment for the Marinero Company," INFORMS Transactions on Education, INFORMS, vol. 19(3), pages 152-154, May.

   Cited by:

   1. Saurabh Chandra & Amit Kumar Vatsa, 2021. "Case Article—Coastal Shipping for Automobile Distribution," INFORMS Transactions on Education, INFORMS, vol. 22(1), pages 28-34, September.

5. Palagi, Laura & Pesyridis, Apostolos & Sciubba, Enrico & Tocci, Lorenzo, 2019. "Machine Learning for the prediction of the dynamic behavior of a small scale ORC system," Energy, Elsevier, vol. 166(C), pages 72-82.

   Cited by:

   1. Ying Zhang & Li Zhao & Shuai Deng & Ming Li & Yali Liu & Qiongfen Yu & Mengxing Li, 2022. "Novel Off-Design Operation Maps Showing Functionality Limitations of Organic Rankine Cycle Validated by Experiments," Energies, MDPI, vol. 15(21), pages 1-19, November.
   2. He, Jintao & Shi, Lingfeng & Tian, Hua & Wang, Xuan & Sun, Xiaocun & Zhang, Meiyan & Yao, Yu & Shu, Gequn, 2023. "Applying artificial neural network to approximate and predict the transient dynamic behavior of CO2 combined cooling and power cycle," Energy, Elsevier, vol. 285(C).
   3. Michael Chukwuemeka Ekwonu & Mirae Kim & Binqi Chen & Muhammad Tauseef Nasir & Kyung Chun Kim, 2023. "Dynamic Simulation of Partial Load Operation of an Organic Rankine Cycle with Two Parallel Expanders," Energies, MDPI, vol. 16(1), pages 1-18, January.
   4. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Yao, Baofeng & Wang, Yan, 2022. "An outlier removal and feature dimensionality reduction framework with unsupervised learning and information theory intervention for organic Rankine cycle (ORC)," Energy, Elsevier, vol. 254(PB).
   5. Tian, Zhen & Gan, Wanlong & Zou, Xianzhi & Zhang, Yuan & Gao, Wenzhong, 2022. "Performance prediction of a cryogenic organic Rankine cycle based on back propagation neural network optimized by genetic algorithm," Energy, Elsevier, vol. 254(PB).
   6. Wan Rashidi Bin Wan Ramli & Apostolos Pesyridis & Dhrumil Gohil & Fuhaid Alshammari, 2020. "Organic Rankine Cycle Waste Heat Recovery for Passenger Hybrid Electric Vehicles," Energies, MDPI, vol. 13(17), pages 1-27, September.
   7. Lu, Pei & Chen, Kaihuang & Luo, Xianglong & Wu, Wei & Liang, Yingzong & Chen, Jianyong & Chen, Ying, 2024. "Experimental and simulation study on a zeotropic ORC system using R1234ze(E)/R245fa as working fluid," Energy, Elsevier, vol. 292(C).
   8. Imran, Muhammad & Pili, Roberto & Usman, Muhammad & Haglind, Fredrik, 2020. "Dynamic modeling and control strategies of organic Rankine cycle systems: Methods and challenges," Applied Energy, Elsevier, vol. 276(C).
   9. Zhang, Yuan & Wu, Xiaocheng & Tian, Zhen & Gao, Wenzhong & Peng, Hao & Yang, Ke, 2023. "Comparison of random forest, support vector regression, and long short term memory for performance prediction and optimization of a cryogenic organic rankine cycle (ORC)," Energy, Elsevier, vol. 280(C).
   10. Lisheng Pan & Huaixin Wang, 2019. "Experimental Investigation on Performance of an Organic Rankine Cycle System Integrated with a Radial Flow Turbine," Energies, MDPI, vol. 12(4), pages 1-20, February.

6. Palagi, Laura & Sciubba, Enrico & Tocci, Lorenzo, 2019. "A neural network approach to the combined multi-objective optimization of the thermodynamic cycle and the radial inflow turbine for Organic Rankine cycle applications," Applied Energy, Elsevier, vol. 237(C), pages 210-226.

   Cited by:

   1. Nima Javanshir & S. M. Seyed Mahmoudi & Marc A. Rosen, 2019. "Thermodynamic and Exergoeconomic Analyses of a Novel Combined Cycle Comprised of Vapor-Compression Refrigeration and Organic Rankine Cycles," Sustainability, MDPI, vol. 11(12), pages 1-20, June.
   2. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Pan, Yachao & Zhang, Wujie & Wang, Yan, 2023. "Nonlinear modeling and multi-scale influence characteristics analysis of organic Rankine cycle (ORC) system considering variable driving cycles," Energy, Elsevier, vol. 265(C).
   3. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Zhang, Wujie & Wang, Yan, 2022. "Evaluation of hybrid forecasting methods for organic Rankine cycle: Unsupervised learning-based outlier removal and partial mutual information-based feature selection," Applied Energy, Elsevier, vol. 311(C).
   4. Hagen, Brede A.L. & Andresen, Trond & Nekså, Petter, 2022. "Equation-oriented methods for optimizing Rankine cycles using radial inflow turbine," Energy, Elsevier, vol. 252(C).
   5. Shiqi Wang & Zhongyuan Yuan, 2020. "A Hot Water Split-Flow Dual-Pressure Strategy to Improve System Performance for Organic Rankine Cycle," Energies, MDPI, vol. 13(13), pages 1-21, June.
   6. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Zhang, Jian & Xing, Chengda & Yan, Yinlian & Yang, Anren & Wang, Yan, 2023. "Information theory-based dynamic feature capture and global multi-objective optimization approach for organic Rankine cycle (ORC) considering road environment," Applied Energy, Elsevier, vol. 348(C).
   7. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Yao, Baofeng & Wang, Yan, 2022. "An outlier removal and feature dimensionality reduction framework with unsupervised learning and information theory intervention for organic Rankine cycle (ORC)," Energy, Elsevier, vol. 254(PB).
   8. Fuhaid Alshammari & Apostolos Pesyridis & Mohamed Elashmawy, 2020. "Generation of 3D Turbine Blades for Automotive Organic Rankine Cycles: Mathematical and Computational Perspectives," Mathematics, MDPI, vol. 9(1), pages 1-30, December.
   9. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Xing, Chengda & Yang, Anren & Yan, Yinlian & Pan, Yachao & Wang, Yan, 2023. "Ensemble of self-organizing adaptive maps and dynamic multi-objective optimization for organic Rankine cycle (ORC) under transportation and driving environment," Energy, Elsevier, vol. 275(C).
   10. Shuozhuo Hu & Zhen Yang & Jian Li & Yuanyuan Duan, 2021. "A Review of Multi-Objective Optimization in Organic Rankine Cycle (ORC) System Design," Energies, MDPI, vol. 14(20), pages 1-36, October.
   11. Enrico Baldasso & Maria E. Mondejar & Ulrik Larsen & Fredrik Haglind, 2020. "Regression Models for the Evaluation of the Techno-Economic Potential of Organic Rankine Cycle-Based Waste Heat Recovery Systems on Board Ships Using Low Sulfur Fuels," Energies, MDPI, vol. 13(6), pages 1-20, March.
   12. Cheng, Ziyang & Wang, Jiangfeng & Hu, Bin & Chen, Liangqi & Lou, Juwei & Cheng, Shangfang & Wu, Weifeng, 2024. "Improved modelling for ammonia-water power cycle coupled with turbine optimization design: A comparison study," Energy, Elsevier, vol. 292(C).
   13. Han, Yongming & Wu, Hao & Geng, Zhiqiang & Zhu, Qunxiong & Gu, Xiangbai & Yu, Bin, 2020. "Review: Energy efficiency evaluation of complex petrochemical industries," Energy, Elsevier, vol. 203(C).
   14. Yanni Yu & Mingqian Tian & Yanjun Liu & Beichen Lu & Yun Chen, 2024. "Experimental Research and Improved Neural Network Optimization Based on the Ocean Thermal Energy Conversion Experimental Platform," Energies, MDPI, vol. 17(17), pages 1-20, August.
   15. Dokl, Monika & Gomilšek, Rok & Čuček, Lidija & Abikoye, Ben & Kravanja, Zdravko, 2022. "Maximizing the power output and net present value of organic Rankine cycle: Application to aluminium industry," Energy, Elsevier, vol. 239(PE).

7. Andrea Manno & Laura Palagi & Simone Sagratella, 2019. "Case—Production and Distribution Optimization of Beach Equipment for the Marinero Company," INFORMS Transactions on Education, INFORMS, vol. 19(3), pages 155-159, May.

   Cited by:

   1. Saurabh Chandra & Amit Kumar Vatsa, 2021. "Case Article—Coastal Shipping for Automobile Distribution," INFORMS Transactions on Education, INFORMS, vol. 22(1), pages 28-34, September.
   2. Andrea Manno & Laura Palagi & Simone Sagratella, 2019. "Case Article—Production and Distribution Optimization of Beach Equipment for the Marinero Company," INFORMS Transactions on Education, INFORMS, vol. 19(3), pages 152-154, May.

8. Andrea Manno & Laura Palagi & Simone Sagratella, 2018. "Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training," Computational Optimization and Applications, Springer, vol. 71(1), pages 115-145, September.

   Cited by:

   1. Tommaso Colombo & Simone Sagratella, 2020. "Distributed algorithms for convex problems with linear coupling constraints," Journal of Global Optimization, Springer, vol. 77(1), pages 53-73, May.
   2. Andrea Cristofari, 2019. "An almost cyclic 2-coordinate descent method for singly linearly constrained problems," Computational Optimization and Applications, Springer, vol. 73(2), pages 411-452, June.
   3. Valeria Ruggiero & Gerardo Toraldo, 2018. "Introduction to the special issue for SIMAI 2016," Computational Optimization and Applications, Springer, vol. 71(1), pages 1-3, September.

9. Lamedica, Regina & Santini, Ezio & Ruvio, Alessandro & Palagi, Laura & Rossetta, Irene, 2018. "A MILP methodology to optimize sizing of PV - Wind renewable energy systems," Energy, Elsevier, vol. 165(PB), pages 385-398.

   Cited by:

   1. Luo, Xi & Liu, Yanfeng & Feng, Pingan & Gao, Yuan & Guo, Zhenxiang, 2021. "Optimization of a solar-based integrated energy system considering interaction between generation, network, and demand side," Applied Energy, Elsevier, vol. 294(C).
   2. Akhlaque Ahmad Khan & Ahmad Faiz Minai & Rupendra Kumar Pachauri & Hasmat Malik, 2022. "Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review," Energies, MDPI, vol. 15(17), pages 1-29, August.
   3. Park, Musik & Wang, Zhiyuan & Li, Lanyu & Wang, Xiaonan, 2023. "Multi-objective building energy system optimization considering EV infrastructure," Applied Energy, Elsevier, vol. 332(C).
   4. Lee, Jui-Yuan & Aviso, Kathleen B. & Tan, Raymond R., 2019. "Multi-objective optimisation of hybrid power systems under uncertainties," Energy, Elsevier, vol. 175(C), pages 1271-1282.
   5. Abdolahi-Mansoorkhani, Hamed & Seddighi, Sadegh, 2019. "H2S and CO2 capture from gaseous fuels using nanoparticle membrane," Energy, Elsevier, vol. 168(C), pages 847-857.
   6. Ndwali, Kasereka & Njiri, Jackson G. & Wanjiru, Evan M., 2020. "Multi-objective optimal sizing of grid connected photovoltaic batteryless system minimizing the total life cycle cost and the grid energy," Renewable Energy, Elsevier, vol. 148(C), pages 1256-1265.
   7. Li, Rong & Guo, Su & Yang, Yong & Liu, Deyou, 2020. "Optimal sizing of wind/ concentrated solar plant/ electric heater hybrid renewable energy system based on two-stage stochastic programming," Energy, Elsevier, vol. 209(C).
   8. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
   9. Jiang, Yinghua & Kang, Lixia & Liu, Yongzhong, 2020. "Optimal configuration of battery energy storage system with multiple types of batteries based on supply-demand characteristics," Energy, Elsevier, vol. 206(C).
   10. Eva M. Urbano & Victor Martinez-Viol & Konstantinos Kampouropoulos & Luis Romeral, 2021. "Energy-Investment Decision-Making for Industry: Quantitative and Qualitative Risks Integrated Analysis," Sustainability, MDPI, vol. 13(12), pages 1-30, June.
   11. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2022. "Risk assessment of energy investment in the industrial framework – Uncertainty and Sensitivity Analysis for energy design and operation optimisation," Energy, Elsevier, vol. 239(PA).
   12. Sergio Rech & Stefano Casarin & Carlos Santos Silva & Andrea Lazzaretto, 2020. "University Campus and Surrounding Residential Complexes as Energy-Hub: A MILP Optimization Approach for a Smart Exchange of Solar Energy," Energies, MDPI, vol. 13(11), pages 1-22, June.
   13. Wu, Yaqing & Maravelias, Christos T., 2024. "Piecewise linear trees as surrogate models for system design and planning under high-frequency temporal variability," European Journal of Operational Research, Elsevier, vol. 315(2), pages 541-552.
   14. Xiong, Hualin & Xu, Beibei & Kheav, Kimleng & Luo, Xingqi & Zhang, Xingjin & Patelli, Edoardo & Guo, Pengcheng & Chen, Diyi, 2021. "Multiscale power fluctuation evaluation of a hydro-wind-photovoltaic system," Renewable Energy, Elsevier, vol. 175(C), pages 153-166.
   15. Urbano, Eva M. & Martinez-Viol, Victor & Kampouropoulos, Konstantinos & Romeral, Luis, 2021. "Energy equipment sizing and operation optimisation for prosumer industrial SMEs – A lifetime approach," Applied Energy, Elsevier, vol. 299(C).
   16. Ana Rita Silva & Ana Estanqueiro, 2022. "From Wind to Hybrid: A Contribution to the Optimal Design of Utility-Scale Hybrid Power Plants," Energies, MDPI, vol. 15(7), pages 1-19, April.
   17. Jin, Shuwei & Li, Yongping, 2023. "Analyzing the performance of electricity, heating, and cooling supply nexus in a hybrid energy system of airport under uncertainty," Energy, Elsevier, vol. 272(C).

10. Dellepiane, Umberto & Palagi, Laura, 2015. "Using SVM to combine global heuristics for the Standard Quadratic Problem," European Journal of Operational Research, Elsevier, vol. 241(3), pages 596-605.

    Cited by:

    1. Riccardo Bisori & Matteo Lapucci & Marco Sciandrone, 2022. "A study on sequential minimal optimization methods for standard quadratic problems," 4OR, Springer, vol. 20(4), pages 685-712, December.
    2. Pedro Duarte Silva, A., 2017. "Optimization approaches to Supervised Classification," European Journal of Operational Research, Elsevier, vol. 261(2), pages 772-788.

11. Immanuel Bomze & Luigi Grippo & Laura Palagi, 2012. "Unconstrained formulation of standard quadratic optimization problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 35-51, April.
    • Immanuel M. Bomze & Luigi Grippo & Laura Palagi, 2010. "Unconstrained formulation of standard quadratic optimization problems," DIS Technical Reports 2010-12, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    See citations under working paper version above.
12. C. J. Lin & S. Lucidi & L. Palagi & A. Risi & M. Sciandrone, 2009. "Decomposition Algorithm Model for Singly Linearly-Constrained Problems Subject to Lower and Upper Bounds," Journal of Optimization Theory and Applications, Springer, vol. 141(1), pages 107-126, April.

    Cited by:

    1. Giampaolo Liuzzi & Laura Palagi & Mauro Piacentini, 2010. "On the convergence of a Jacobi-type algorithm for Singly Linearly-Constrained Problems Subject to simple Bounds," DIS Technical Reports 2010-01, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    2. I. V. Konnov, 2016. "Selective bi-coordinate variations for resource allocation type problems," Computational Optimization and Applications, Springer, vol. 64(3), pages 821-842, July.
    3. Leonardo Galli & Alessandro Galligari & Marco Sciandrone, 2020. "A unified convergence framework for nonmonotone inexact decomposition methods," Computational Optimization and Applications, Springer, vol. 75(1), pages 113-144, January.
    4. Tommaso Colombo & Simone Sagratella, 2020. "Distributed algorithms for convex problems with linear coupling constraints," Journal of Global Optimization, Springer, vol. 77(1), pages 53-73, May.
    5. Dellepiane, Umberto & Palagi, Laura, 2015. "Using SVM to combine global heuristics for the Standard Quadratic Problem," European Journal of Operational Research, Elsevier, vol. 241(3), pages 596-605.
    6. Paul Tseng & Sangwoon Yun, 2010. "A coordinate gradient descent method for linearly constrained smooth optimization and support vector machines training," Computational Optimization and Applications, Springer, vol. 47(2), pages 179-206, October.
    7. Ion Necoara & Andrei Patrascu, 2014. "A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints," Computational Optimization and Applications, Springer, vol. 57(2), pages 307-337, March.
    8. Andrea Manno & Laura Palagi & Simone Sagratella, 2018. "Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training," Computational Optimization and Applications, Springer, vol. 71(1), pages 115-145, September.
    9. G. Liuzzi & S. Lucidi & F. Rinaldi, 2012. "Derivative-free methods for bound constrained mixed-integer optimization," Computational Optimization and Applications, Springer, vol. 53(2), pages 505-526, October.
    10. David Di Lorenzo & Alessandro Galligari & Marco Sciandrone, 2015. "A convergent and efficient decomposition method for the traffic assignment problem," Computational Optimization and Applications, Springer, vol. 60(1), pages 151-170, January.
    11. Andrei Patrascu & Ion Necoara, 2015. "Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization," Journal of Global Optimization, Springer, vol. 61(1), pages 19-46, January.
    12. Amir Beck, 2014. "The 2-Coordinate Descent Method for Solving Double-Sided Simplex Constrained Minimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 162(3), pages 892-919, September.
    13. Andrea Cristofari, 2019. "An almost cyclic 2-coordinate descent method for singly linearly constrained problems," Computational Optimization and Applications, Springer, vol. 73(2), pages 411-452, June.
    14. Giampaolo Liuzzi & Stefano Lucidi & Francesco Rinaldi, 2015. "Derivative-Free Methods for Mixed-Integer Constrained Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 164(3), pages 933-965, March.
    15. Veronica Piccialli & Marco Sciandrone, 2022. "Nonlinear optimization and support vector machines," Annals of Operations Research, Springer, vol. 314(1), pages 15-47, July.
    16. G. Cocchi & G. Liuzzi & A. Papini & M. Sciandrone, 2018. "An implicit filtering algorithm for derivative-free multiobjective optimization with box constraints," Computational Optimization and Applications, Springer, vol. 69(2), pages 267-296, March.
    17. P. Tseng & S. Yun, 2009. "Block-Coordinate Gradient Descent Method for Linearly Constrained Nonsmooth Separable Optimization," Journal of Optimization Theory and Applications, Springer, vol. 140(3), pages 513-535, March.
    18. Cassioli, A. & Di Lorenzo, D. & Sciandrone, M., 2013. "On the convergence of inexact block coordinate descent methods for constrained optimization," European Journal of Operational Research, Elsevier, vol. 231(2), pages 274-281.
    19. Veronica Piccialli & Marco Sciandrone, 2018. "Nonlinear optimization and support vector machines," 4OR, Springer, vol. 16(2), pages 111-149, June.

13. A. Ciancimino & G. Inzerillo & S. Lucidi & L. Palagi, 1999. "A Mathematical Programming Approach for the Solution of the Railway Yield Management Problem," Transportation Science, INFORMS, vol. 33(2), pages 168-181, May.

    Cited by:

    1. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    2. Haque, Md Tabish & Hamid, Faiz, 2023. "Social distancing and revenue management—A post-pandemic adaptation for railways," Omega, Elsevier, vol. 114(C).
    3. Wang, Xinchang, 2017. "Static and dynamic resource allocation models for single-leg transportation markets with service disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 87-108.
    4. Xu, Guangming & Zhong, Linhuan & Hu, Xinlei & Liu, Wei, 2022. "Optimal pricing and seat allocation schemes in passenger railway systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    5. Harrod, Steven, 2013. "Auction pricing of network access for North American railways," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 49(1), pages 176-189.
    6. Wang, Xinchang, 2016. "Stochastic resource allocation for containerized cargo transportation networks when capacities are uncertain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 334-357.
    7. Yu Wang & Xinghua Shan & Hongye Wang & Junfeng Zhang & Xiaoyan Lv & Jinfei Wu, 2022. "Ticket Allocation Optimization of Fuxing Train Based on Overcrowding Control: An Empirical Study from China," Sustainability, MDPI, vol. 14(12), pages 1-12, June.
    8. D’Alfonso, Tiziana & Jiang, Changmin & Bracaglia, Valentina, 2015. "Would competition between air transport and high-speed rail benefit environment and social welfare?," Transportation Research Part B: Methodological, Elsevier, vol. 74(C), pages 118-137.
    9. Ikeda, Shunnosuke & Nishimura, Naoki & Sukegawa, Noriyoshi & Takano, Yuichi, 2023. "Prescriptive price optimization using optimal regression trees," Operations Research Perspectives, Elsevier, vol. 11(C).
    10. Wang, Tingsong & Meng, Qiang & Tian, Xuecheng, 2024. "Dynamic container slot allocation for a liner shipping service," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    11. William L. Cooper, 2002. "Asymptotic Behavior of an Allocation Policy for Revenue Management," Operations Research, INFORMS, vol. 50(4), pages 720-727, August.
    12. H Jiang, 2008. "A Lagrangian relaxation approach for network inventory control of stochastic revenue management with perishable commodities," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(3), pages 372-380, March.
    13. Haque, Md Tabish & Hamid, Faiz, 2022. "An optimization model to assign seats in long distance trains to minimize SARS-CoV-2 diffusion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 104-120.
    14. Xueyi Guan & Jin Qin & Chenghui Mao & Wenliang Zhou, 2023. "A Literature Review of Railway Pricing Based on Revenue Management," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
    15. Wang, Xinchang & Wang, Hua & Zhang, Xiaoning, 2016. "Stochastic seat allocation models for passenger rail transportation under customer choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 95-112.
    16. Wang, Hua & Wang, Xinchang & Zhang, Xiaoning, 2017. "Dynamic resource allocation for intermodal freight transportation with network effects: Approximations and algorithms," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 83-112.
    17. Barry C. Smith & Dirk P. Günther & B. Venkateshwara Rao & Richard M. Ratlife, 2001. "E-Commerce and Operations Research in Airline Planning, Marketing, and Distribution," Interfaces, INFORMS, vol. 31(2), pages 37-55, April.
    18. Aparna Jayaram & R. K. Amit & Amit Agarwal & Xiaodong Luo, 2024. "Elasticity-integrated pricing and allocation heuristic for airline revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(4), pages 305-317, August.
    19. Wang, Xinchang, 2016. "Optimal allocation of limited and random network resources to discrete stochastic demands for standardized cargo transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 310-331.
    20. Pak, K. & Dekker, R. & Kindervater, G.A.P., 2003. "Airline Revenue Management with Shifting Capacity," ERIM Report Series Research in Management ERS-2003-091-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
        • Pak, K. & Dekker, R. & Kindervater, G.A.P., 2003. "Airline Revenue Management with Shifting Capacity," Econometric Institute Research Papers ERS-2003-091-LIS, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    21. Wuyang Yuan & Lei Nie & Xin Wu & Huiling Fu, 2018. "A dynamic bid price approach for the seat inventory control problem in railway networks with consideration of passenger transfer," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-23, August.
    22. Alexander Armstrong & Joern Meissner, 2010. "Railway Revenue Management: Overview and Models (Operations Research)," Working Papers MRG/0019, Department of Management Science, Lancaster University, revised Jul 2010.
    23. Xu, Guangming & Zhong, Linhuan & Liu, Wei & Guo, Jing, 2024. "A flexible train composition strategy with extra-long trains for high-speed railway corridors with time-varying demand," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    24. Andreea Popescu & Earl Barnes & Ellis Johnson & Pinar Keskinocak, 2013. "Bid Prices When Demand Is a Mix of Individual and Batch Bookings," Transportation Science, INFORMS, vol. 47(2), pages 198-213, May.
    25. Xu, Guangming & Liu, Yihan & Gao, Yihan & Liu, Wei, 2023. "Integrated optimization of train stopping plan and seat allocation scheme for railway systems under equilibrium travel choice and elastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    26. Li, Dongjun & Islam, Dewan Md Zahurul & Robinson, Mark & Song, Dong-Ping & Dong, Jing-Xin & Reimann, Marc, 2024. "Network revenue management game in the railway industry: Stackelberg equilibrium, global optimality, and mechanism design," European Journal of Operational Research, Elsevier, vol. 312(1), pages 240-254.
    27. Wang, Xiubin & Wang, Fenghuan, 2007. "Dynamic network yield management," Transportation Research Part B: Methodological, Elsevier, vol. 41(4), pages 410-425, May.
    28. Xiang Zhao & Xinghua Shan & Jinfei Wu, 2023. "The Impact of Seat Resource Fragmentation on Railway Network Revenue Management," Networks and Spatial Economics, Springer, vol. 23(1), pages 135-177, March.
    29. Raja Gopalakrishnan & Narayan Rangaraj, 2010. "Capacity Management on Long-Distance Passenger Trains of Indian Railways," Interfaces, INFORMS, vol. 40(4), pages 291-302, August.
    30. de Boer, Sanne V. & Freling, Richard & Piersma, Nanda, 2002. "Mathematical programming for network revenue management revisited," European Journal of Operational Research, Elsevier, vol. 137(1), pages 72-92, February.
    31. Wuyang Yuan & Lei Nie, 2020. "Optimization of seat allocation with fixed prices: An application of railway revenue management in China," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-25, April.

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 7 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.

1. NEP-CMP: Computational Economics (6) 2012-07-14 2014-11-22 2017-11-12 2019-06-24 2019-06-24 2019-06-24. Author is listed
2. NEP-BIG: Big Data (2) 2017-11-12 2019-06-24
3. NEP-TRE: Transport Economics (2) 2019-06-24 2019-06-24
4. NEP-ECM: Econometrics (1) 2019-06-24
5. NEP-ORE: Operations Research (1) 2019-06-24

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