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Kostas Florios

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. Florios, Kostas & Mavrotas, George, 2014. "Generation of the exact Pareto set in multi-objective traveling salesman and set covering problems," MPRA Paper 105074, University Library of Munich, Germany.

    Cited by:

    1. Ömer Faruk Yılmaz & Büşra Yazıcı, 2022. "Tactical level strategies for multi-objective disassembly line balancing problem with multi-manned stations: an optimization model and solution approaches," Annals of Operations Research, Springer, vol. 319(2), pages 1793-1843, December.
    2. Justus Bonz, 2021. "Application of a multi-objective multi traveling salesperson problem with time windows," Public Transport, Springer, vol. 13(1), pages 35-57, March.
    3. Mohammed Mahrach & Gara Miranda & Coromoto León & Eduardo Segredo, 2020. "Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman Problem," Mathematics, MDPI, vol. 8(11), pages 1-23, November.
    4. Lakmali Weerasena & Aniekan Ebiefung & Anthony Skjellum, 2022. "Design of a heuristic algorithm for the generalized multi-objective set covering problem," Computational Optimization and Applications, Springer, vol. 82(3), pages 717-751, July.
    5. 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.
    6. Lakmali Weerasena & Margaret M. Wiecek & Banu Soylu, 2017. "An algorithm for approximating the Pareto set of the multiobjective set covering problem," Annals of Operations Research, Springer, vol. 248(1), pages 493-514, January.
    7. Alexandros Nikas & Angelos Fountoulakis & Aikaterini Forouli & Haris Doukas, 2022. "A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems," Operational Research, Springer, vol. 22(2), pages 1291-1332, April.
    8. Gais Alhadi & Imed Kacem & Pierre Laroche & Izzeldin M. Osman, 2020. "Approximation algorithms for minimizing the maximum lateness and makespan on parallel machines," Annals of Operations Research, Springer, vol. 285(1), pages 369-395, February.
    9. Dinçer Konur & Hadi Farhangi & Cihan H. Dagli, 2016. "A multi-objective military system of systems architecting problem with inflexible and flexible systems: formulation and solution methods," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(4), pages 967-1006, October.
    10. Oracio I. Barbosa-Ayala & Jhon A. Montañez-Barrera & Cesar E. Damian-Ascencio & Adriana Saldaña-Robles & J. Arturo Alfaro-Ayala & Jose Alfredo Padilla-Medina & Sergio Cano-Andrade, 2020. "Solution to the Economic Emission Dispatch Problem Using Numerical Polynomial Homotopy Continuation," Energies, MDPI, vol. 13(17), pages 1-15, August.

  2. Mavrotas, George & Florios, Kostas, 2013. "An improved version of the augmented epsilon-constraint method (AUGMECON2) for finding the exact Pareto set in Multi-Objective Integer Programming problems," MPRA Paper 105034, University Library of Munich, Germany.

    Cited by:

    1. Bababeik, Mostafa & Khademi, Navid & Chen, Anthony, 2018. "Increasing the resilience level of a vulnerable rail network: The strategy of location and allocation of emergency relief trains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 119(C), pages 110-128.
    2. Amin Eshkiti & Fatemeh Sabouhi & Ali Bozorgi-Amiri, 2023. "A data-driven optimization model to response to COVID-19 pandemic: a case study," Annals of Operations Research, Springer, vol. 328(1), pages 337-386, September.
    3. Zhang, Weihua & Reimann, Marc, 2014. "A simple augmented ∊-constraint method for multi-objective mathematical integer programming problems," European Journal of Operational Research, Elsevier, vol. 234(1), pages 15-24.
    4. Panos Xidonas & Haris Doukas & George Mavrotas & Olena Pechak, 2016. "Environmental corporate responsibility for investments evaluation: an alternative multi-objective programming model," Annals of Operations Research, Springer, vol. 247(2), pages 395-413, December.
    5. Pejman Peykani & Jafar Gheidar-Kheljani & Sheida Shahabadi & Seyyed Hassan Ghodsypour & Mojtaba Nouri, 2023. "A two-phase resource-constrained project scheduling approach for design and development of complex product systems," Operational Research, Springer, vol. 23(1), pages 1-25, March.
    6. Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2017. "Train timetabling at rapid rail transit lines: a robust multi-objective stochastic programming approach," Operational Research, Springer, vol. 17(2), pages 435-477, July.
    7. de Oliveira, Glauber Cardoso & Bertone, Edoardo & Stewart, Rodney A., 2022. "Optimisation modelling tools and solving techniques for integrated precinct-scale energy–water system planning," Applied Energy, Elsevier, vol. 318(C).
    8. Mesquita-Cunha, Mariana & Figueira, José Rui & Barbosa-Póvoa, Ana Paula, 2023. "New ϵ−constraint methods for multi-objective integer linear programming: A Pareto front representation approach," European Journal of Operational Research, Elsevier, vol. 306(1), pages 286-307.
    9. Ali Gharaei & Fariborz Jolai, 2021. "A Pareto approach for the multi-factory supply chain scheduling and distribution problem," Operational Research, Springer, vol. 21(4), pages 2333-2364, December.
    10. Yan, Ran & Liu, Yan & Wang, Shuaian, 2024. "A data-driven optimization approach to improving maritime transport efficiency," Transportation Research Part B: Methodological, Elsevier, vol. 180(C).
    11. Majid Askarifard & Hamidreza Abbasianjahromi & Mehran Sepehri & Ehsanollah Zeighami, 2021. "A robust multi-objective optimization model for project scheduling considering risk and sustainable development criteria," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 11494-11524, August.
    12. Mavrotas, George & Gakis, Nikos & Skoulaxinou, Sotiria & Katsouros, Vassilis & Georgopoulou, Elena, 2015. "Municipal solid waste management and energy production: Consideration of external cost through multi-objective optimization and its effect on waste-to-energy solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1205-1222.
    13. Nathan Adelgren & Akshay Gupte, 2022. "Branch-and-Bound for Biobjective Mixed-Integer Linear Programming," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 909-933, March.
    14. Jing, Rui & Wang, Meng & Liang, Hao & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2018. "Multi-objective optimization of a neighborhood-level urban energy network: Considering Game-theory inspired multi-benefit allocation constraints," Applied Energy, Elsevier, vol. 231(C), pages 534-548.
    15. Mavrotas, George & Pechak, Olena & Siskos, Eleftherios & Doukas, Haris & Psarras, John, 2015. "Robustness analysis in Multi-Objective Mathematical Programming using Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 240(1), pages 193-201.
    16. Rong, Aiying & Figueira, José Rui, 2014. "Dynamic programming algorithms for the bi-objective integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 236(1), pages 85-99.
    17. Zhong, Jia & Yu, T. Edward & Larson, James A. & English, Burton C. & Fu, Joshua S. & Calcagno, James, 2016. "Analysis of environmental and economic tradeoffs in switchgrass supply chains for biofuel production," Energy, Elsevier, vol. 107(C), pages 791-803.
    18. Alcaraz, Javier & Landete, Mercedes & Monge, Juan F. & Sainz-Pardo, José L., 2020. "Multi-objective evolutionary algorithms for a reliability location problem," European Journal of Operational Research, Elsevier, vol. 283(1), pages 83-93.
    19. Wendy Wijaya & Kuntjoro Adji Sidarto, 2023. "Large-Scale Portfolio Optimization Using Biogeography-Based Optimization," IJFS, MDPI, vol. 11(4), pages 1-16, October.
    20. Nadide Caglayan & Sule Itir Satoglu, 2021. "Multi-Objective Two-Stage Stochastic Programming Model for a Proposed Casualty Transportation System in Large-Scale Disasters: A Case Study," Mathematics, MDPI, vol. 9(4), pages 1-22, February.
    21. Mohebalizadehgashti, Fatemeh & Zolfagharinia, Hossein & Amin, Saman Hassanzadeh, 2020. "Designing a green meat supply chain network: A multi-objective approach," International Journal of Production Economics, Elsevier, vol. 219(C), pages 312-327.
    22. Pereira, Daniel Filipe & Oliveira, José Fernando & Carravilla, Maria Antónia, 2022. "Merging make-to-stock/make-to-order decisions into sales and operations planning: A multi-objective approach," Omega, Elsevier, vol. 107(C).
    23. Petrelli, Marina & Fioriti, Davide & Berizzi, Alberto & Bovo, Cristian & Poli, Davide, 2021. "A novel multi-objective method with online Pareto pruning for multi-year optimization of rural microgrids," Applied Energy, Elsevier, vol. 299(C).
    24. Audrey Cerqueus & Xavier Gandibleux & Anthony Przybylski & Frédéric Saubion, 2017. "On branching heuristics for the bi-objective 0/1 unidimensional knapsack problem," Journal of Heuristics, Springer, vol. 23(5), pages 285-319, October.
    25. Bashir Bashir & Özlem Karsu, 2022. "Solution approaches for equitable multiobjective integer programming problems," Annals of Operations Research, Springer, vol. 311(2), pages 967-995, April.
    26. Ghaithan, Ahmed M. & Mohammed, Awsan & Al-Hanbali, Ahmad & Attia, Ahmed M. & Saleh, Haitham, 2022. "Multi-objective optimization of a photovoltaic-wind- grid connected system to power reverse osmosis desalination plant," Energy, Elsevier, vol. 251(C).
    27. Sahebjamnia, Navid & Torabi, S. Ali & Mansouri, S. Afshin, 2018. "Building organizational resilience in the face of multiple disruptions," International Journal of Production Economics, Elsevier, vol. 197(C), pages 63-83.
    28. Pudasaini, Pramesh, 2021. "Integrated planning of downstream petroleum supply chain: a multi-objective stochastic approach," Operations Research Perspectives, Elsevier, vol. 8(C).
    29. Satya Tamby & Daniel Vanderpooten, 2021. "Enumeration of the Nondominated Set of Multiobjective Discrete Optimization Problems," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 72-85, January.
    30. N. A. Arellano-Arriaga & J. Molina & S. E. Schaeffer & A. M. Álvarez-Socarrás & I. A. Martínez-Salazar, 2019. "A bi-objective study of the minimum latency problem," Journal of Heuristics, Springer, vol. 25(3), pages 431-454, June.
    31. Oylum S¸eker & Mucahit Cevik & Merve Bodur & Young Lee & Mark Ruschin, 2023. "A Multiobjective Approach for Sector Duration Optimization in Stereotactic Radiosurgery Treatment Planning," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 248-264, January.
    32. Mavrotas, George & Figueira, José Rui & Siskos, Eleftherios, 2015. "Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection," Omega, Elsevier, vol. 52(C), pages 142-155.
    33. Jabbarzadeh, Armin & Azad, Nader & Verma, Manish, 2020. "An optimization approach to planning rail hazmat shipments in the presence of random disruptions," Omega, Elsevier, vol. 96(C).
    34. Finke, Jonas & Bertsch, Valentin, 2022. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," MPRA Paper 115504, University Library of Munich, Germany.
    35. Jun Zhao & Lixiang Huang, 2019. "Multi-Period Network Design Problem in Regional Hazardous Waste Management Systems," IJERPH, MDPI, vol. 16(11), pages 1-27, June.
    36. Zahra Ghasemzadeh & Ahmad Sadeghieh & Davood Shishebori, 2021. "A stochastic multi-objective closed-loop global supply chain concerning waste management: a case study of the tire industry," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5794-5821, April.
    37. de Oliveira, Glauber Cardoso & Bertone, Edoardo & Stewart, Rodney A., 2022. "Challenges, opportunities, and strategies for undertaking integrated precinct-scale energy–water system planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    38. Saedinia, R. & Vahdani, Behnam & Etebari, F. & Afshar Nadjafi, B., 2019. "Robust gasoline closed loop supply chain design with redistricting, service sharing and intra-district service transfer," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 121-141.
    39. Ömer Faruk Yılmaz & Büşra Yazıcı, 2022. "Tactical level strategies for multi-objective disassembly line balancing problem with multi-manned stations: an optimization model and solution approaches," Annals of Operations Research, Springer, vol. 319(2), pages 1793-1843, December.
    40. I. Kaliszewski & J. Miroforidis, 2022. "Probing the Pareto front of a large-scale multiobjective problem with a MIP solver," Operational Research, Springer, vol. 22(5), pages 5617-5673, November.
    41. Jabbarzadeh, Armin & Haughton, Michael & Pourmehdi, Fahime, 2019. "A robust optimization model for efficient and green supply chain planning with postponement strategy," International Journal of Production Economics, Elsevier, vol. 214(C), pages 266-283.
    42. Almeida, João & Santos, Daniel & Figueira, José Rui & Francisco, Alexandre P., 2024. "A multi-objective mixed integer linear programming model for thesis defence scheduling," European Journal of Operational Research, Elsevier, vol. 312(1), pages 92-116.
    43. H. Khorshidian & M. Akbarpour Shirazi & S. M. T. Fatemi Ghomi, 2019. "An intelligent truck scheduling and transportation planning optimization model for product portfolio in a cross-dock," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 163-184, January.
    44. Gülesin Sena Daş & Fatma Gzara, 2024. "Column generation based solution for bi-objective gate assignment problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 100(1), pages 123-151, August.
    45. Maliheh Ganji & Rahmat Rabet & Seyed Mojtaba Sajadi, 2022. "A new coordinating model for green supply chain and batch delivery scheduling with satisfaction customers," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(4), pages 4566-4601, April.
    46. Wu, Zhiyong & Lu, Zhibin & Zhang, Bingjian & He, Chang & Chen, Qinglin & Yu, Haoshui & Ren, Jingzheng, 2022. "Stochastic bi-objective optimization for closed wet cooling tower systems based on a simplified analytical model," Energy, Elsevier, vol. 250(C).
    47. Justus Bonz, 2021. "Application of a multi-objective multi traveling salesperson problem with time windows," Public Transport, Springer, vol. 13(1), pages 35-57, March.
    48. Seyyed Amir Babak Rasmi & Ali Fattahi & Metin Türkay, 2021. "SASS: slicing with adaptive steps search method for finding the non-dominated points of tri-objective mixed-integer linear programming problems," Annals of Operations Research, Springer, vol. 296(1), pages 841-876, January.
    49. Forouli, Aikaterini & Gkonis, Nikolaos & Nikas, Alexandros & Siskos, Eleftherios & Doukas, Haris & Tourkolias, Christos, 2019. "Energy efficiency promotion in Greece in light of risk: Evaluating policies as portfolio assets," Energy, Elsevier, vol. 170(C), pages 818-831.
    50. Raja Awais Liaqait & Salman Sagheer Warsi & Taiba Zahid & Usman Ghafoor & Muhammad Shakeel Ahmad & Jeyraj Selvaraj, 2021. "A Decision Framework for Solar PV Panels Supply Chain in Context of Sustainable Supplier Selection and Order Allocation," Sustainability, MDPI, vol. 13(23), pages 1-25, November.
    51. Jens Rocholl & Lars Mönch & John Fowler, 2020. "Bi-criteria parallel batch machine scheduling to minimize total weighted tardiness and electricity cost," Journal of Business Economics, Springer, vol. 90(9), pages 1345-1381, November.
    52. Mohammad S. Roni & Sandra D. Eksioglu & Kara G. Cafferty & Jacob J. Jacobson, 2017. "A multi-objective, hub-and-spoke model to design and manage biofuel supply chains," Annals of Operations Research, Springer, vol. 249(1), pages 351-380, February.
    53. Saeedeh Anvari & Metin Turkay, 2017. "The facility location problem from the perspective of triple bottom line accounting of sustainability," International Journal of Production Research, Taylor & Francis Journals, vol. 55(21), pages 6266-6287, November.
    54. Jing, Rui & Kuriyan, Kamal & Kong, Qingyuan & Zhang, Zhihui & Shah, Nilay & Li, Ning & Zhao, Yingru, 2019. "Exploring the impact space of different technologies using a portfolio constraint based approach for multi-objective optimization of integrated urban energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    55. Lakmali Weerasena & Aniekan Ebiefung & Anthony Skjellum, 2022. "Design of a heuristic algorithm for the generalized multi-objective set covering problem," Computational Optimization and Applications, Springer, vol. 82(3), pages 717-751, July.
    56. Di Martinelly, Christine & Meskens, Nadine, 2017. "A bi-objective integrated approach to building surgical teams and nurse schedule rosters to maximise surgical team affinities and minimise nurses' idle time," International Journal of Production Economics, Elsevier, vol. 191(C), pages 323-334.
    57. Mousazadeh, M. & Torabi, S. Ali & Pishvaee, M.S. & Abolhassani, F., 2018. "Accessible, stable, and equitable health service network redesign: A robust mixed possibilistic-flexible approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 113-129.
    58. Wenchao Yi & Yinzhi Zhou & Liang Gao & Xinyu Li & Chunjiang Zhang, 2018. "Engineering design optimization using an improved local search based epsilon differential evolution algorithm," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1559-1580, October.
    59. Mavrotas, George & Florios, Kostas & Figueira, José Rui, 2015. "An improved version of a core based algorithm for the multi-objective multi-dimensional knapsack problem: A computational study and comparison with meta-heuristics," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 25-43.
    60. Pablo A. Miranda-Gonzalez & Javier Maturana-Ross & Carola A. Blazquez & Guillermo Cabrera-Guerrero, 2021. "Exact Formulation and Analysis for the Bi-Objective Insular Traveling Salesman Problem," Mathematics, MDPI, vol. 9(21), pages 1-33, October.
    61. Tautenhain, Camila P.S. & Barbosa-Povoa, Ana Paula & Mota, Bruna & Nascimento, Mariá C.V., 2021. "An efficient Lagrangian-based heuristic to solve a multi-objective sustainable supply chain problem," European Journal of Operational Research, Elsevier, vol. 294(1), pages 70-90.
    62. Zahiri, Behzad & Zhuang, Jun & Mohammadi, Mehrdad, 2017. "Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 109-142.
    63. Hombach, Laura Elisabeth & Walther, Grit, 2015. "Pareto-efficient legal regulation of the (bio)fuel market using a bi-objective optimization model," European Journal of Operational Research, Elsevier, vol. 245(1), pages 286-295.
    64. Florios, Kostas & Mavrotas, George, 2014. "Generation of the exact Pareto set in multi-objective traveling salesman and set covering problems," MPRA Paper 105074, University Library of Munich, Germany.
    65. Salman, F. Sibel & Yücel, Eda & Kayı, İlker & Turper-Alışık, Sedef & Coşkun, Abdullah, 2021. "Modeling mobile health service delivery to Syrian migrant farm workers using call record data," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    66. Sahar Moazzeni & Sobhan Mostafayi Darmian & Lars Magnus Hvattum, 2023. "Multiple criteria decision making and robust optimization to design a development plan for small and medium-sized enterprises in the east of Iran," Operational Research, Springer, vol. 23(1), pages 1-32, March.
    67. Forouli, Aikaterini & Doukas, Haris & Nikas, Alexandros & Sampedro, Jon & Van de Ven, Dirk-Jan, 2019. "Identifying optimal technological portfolios for European power generation towards climate change mitigation: A robust portfolio analysis approach," Utilities Policy, Elsevier, vol. 57(C), pages 33-42.
    68. Zhang, Chuntian & Gao, Yuan & Yang, Lixing & Gao, Ziyou & Qi, Jianguo, 2020. "Joint optimization of train scheduling and maintenance planning in a railway network: A heuristic algorithm using Lagrangian relaxation," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 64-92.
    69. Finke, Jonas & Bertsch, Valentin, 2023. "Implementing a highly adaptable method for the multi-objective optimisation of energy systems," Applied Energy, Elsevier, vol. 332(C).
    70. Barbati, Maria & Greco, Salvatore & Kadziński, Miłosz & Słowiński, Roman, 2018. "Optimization of multiple satisfaction levels in portfolio decision analysis," Omega, Elsevier, vol. 78(C), pages 192-204.
    71. Alexandros Nikas & Angelos Fountoulakis & Aikaterini Forouli & Haris Doukas, 2022. "A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems," Operational Research, Springer, vol. 22(2), pages 1291-1332, April.
    72. Danwen Bao & Shijia Tian & Rui Li & Tianxuan Zhang & Ting Zhu, 2022. "Multi-Objective Decision Method for Airport Landside Rapid Transit Network Design," Networks and Spatial Economics, Springer, vol. 22(4), pages 767-801, December.
    73. Özarık, Sami Serkan & Lokman, Banu & Köksalan, Murat, 2020. "Distribution based representative sets for multi-objective integer programs," European Journal of Operational Research, Elsevier, vol. 284(2), pages 632-643.
    74. Kamyabniya, Afshin & Noormohammadzadeh, Zohre & Sauré, Antoine & Patrick, Jonathan, 2021. "A robust integrated logistics model for age-based multi-group platelets in disaster relief operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    75. Yan, Fei & Bešinović, Nikola & Goverde, Rob M.P., 2019. "Multi-objective periodic railway timetabling on dense heterogeneous railway corridors," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 52-75.
    76. Ataman Nikian & Hassan Khademi Zare & Mohammad Mehdi Lotfi & Mohammad Saber Fallah Nezhad, 2023. "Redesign of a sustainable and resilient closed-loop supply chain network under uncertainty and disruption caused by sanctions and COVID-19," Operations Management Research, Springer, vol. 16(2), pages 1019-1042, June.
    77. Jing, Rui & Lin, Yufeng & Khanna, Nina & Chen, Xiang & Wang, Meng & Liu, Jiahui & Lin, Jianyi, 2021. "Balancing the Energy Trilemma in energy system planning of coastal cities," Applied Energy, Elsevier, vol. 283(C).
    78. Gkonis, Nikolaos & Arsenopoulos, Apostolos & Stamatiou, Athina & Doukas, Haris, 2020. "Multi-perspective design of energy efficiency policies under the framework of national energy and climate action plans," Energy Policy, Elsevier, vol. 140(C).
    79. Farnaz Torabi Yeganeh & Seyed Hessameddin Zegordi, 2020. "A multi-objective optimization approach to project scheduling with resiliency criteria under uncertain activity duration," Annals of Operations Research, Springer, vol. 285(1), pages 161-196, February.
    80. Wang, Ziyao & Zhong, Lipeng & Pan, Zhenning & Yu, Tao & Qiu, Xingyu, 2022. "Optimal double Q AC-DC hybrid distribution system planning with explicit topology-variable-based reliability assessment," Applied Energy, Elsevier, vol. 322(C).
    81. S. Razavyan, 2016. "A Method for Generating a Well-Distributed Pareto Set in Multiple Objective Mixed Integer Linear Programs Based on the Decision Maker’s Initial Aspiration Level," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(04), pages 1-23, August.
    82. Peyman Kiani Nahand & Mahdi Hamid & Mahdi Bastan & Ali Mollajan, 2019. "Human resource management: new approach to nurse scheduling by considering human error," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(6), pages 1429-1443, December.
    83. Jian Wong, Khai & Keat Ooi, Jun & Sin Woon, Kok & Ren Mong, Guo & Shadman, Saleh & Lam Ng, Wai, 2022. "A country-level Pareto-optimal palm waste utilisation network for economic and environmental sustainability," Energy, Elsevier, vol. 260(C).
    84. Dinçer Konur & Hadi Farhangi & Cihan H. Dagli, 2016. "A multi-objective military system of systems architecting problem with inflexible and flexible systems: formulation and solution methods," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(4), pages 967-1006, October.
    85. Mohammad Mahdi Nasiri & Amir Khaleghi & Kannan Govindan & Ali Bozorgi-Amiri, 2023. "Sustainable hierarchical multi-modal hub network design problem: bi-objective formulations and solution algorithms," Operational Research, Springer, vol. 23(2), pages 1-62, June.
    86. Mohammadi, Mehrdad & Jula, Payman & Tavakkoli-Moghaddam, Reza, 2019. "Reliable single-allocation hub location problem with disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 90-120.
    87. Barbati, Maria & Corrente, Salvatore & Greco, Salvatore, 2020. "A general space-time model for combinatorial optimization problems (and not only)," Omega, Elsevier, vol. 96(C).
    88. Theofanis Petropoulos & Paris Patsis & Konstantinos Liapis & Evangelos Chytis, 2024. "A Double Optimum New Solution Method Based on EVA and Knapsack," JRFM, MDPI, vol. 17(11), pages 1-22, November.
    89. Baharmand, Hossein & Comes, Tina & Lauras, Matthieu, 2019. "Bi-objective multi-layer location–allocation model for the immediate aftermath of sudden-onset disasters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 86-110.
    90. Zhao, Jun & Huang, Lixia & Lee, Der-Horng & Peng, Qiyuan, 2016. "Improved approaches to the network design problem in regional hazardous waste management systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 52-75.
    91. José A. Caballero & Juan A. Labarta & Natalia Quirante & Alba Carrero-Parreño & Ignacio E. Grossmann, 2020. "Environmental and Economic Water Management in Shale Gas Extraction," Sustainability, MDPI, vol. 12(4), pages 1-20, February.
    92. Soltanisehat, Leili & González, Andrés D. & Barker, Kash, 2023. "Modeling social, economic, and health perspectives for optimal pandemic policy decision-making," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    93. Zhong, Jia & Yu, T. Edward & Clark, Christopher D. & English, Burton C. & Larson, James A. & Cheng, Chu-Lin, 2018. "Effect of land use change for bioenergy production on feedstock cost and water quality," Applied Energy, Elsevier, vol. 210(C), pages 580-590.
    94. Schmidt, Adam & Albert, Laura A. & Zheng, Kaiyue, 2021. "Risk management for cyber-infrastructure protection: A bi-objective integer programming approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    95. Xuan Luo & Wenzhu Liao, 2022. "Collaborative Reverse Logistics Network for Infectious Medical Waste Management during the COVID-19 Outbreak," IJERPH, MDPI, vol. 19(15), pages 1-28, August.
    96. Attia, Ahmed M. & Al Hanbali, Ahmad & Saleh, Haitham H. & Alsawafy, Omar G. & Ghaithan, Ahmed M. & Mohammed, Awsan, 2021. "A multi-objective optimization model for sizing decisions of a grid-connected photovoltaic system," Energy, Elsevier, vol. 229(C).
    97. Farajiamiri, Mina & Meyer, Jörn-Christian & Walther, Grit, 2023. "Multi-objective optimization of renewable fuel supply chains regarding cost, land use, and water use," Applied Energy, Elsevier, vol. 349(C).
    98. Prina, Matteo Giacomo & Casalicchio, Valeria & Kaldemeyer, Cord & Manzolini, Giampaolo & Moser, David & Wanitschke, Alexander & Sparber, Wolfram, 2020. "Multi-objective investment optimization for energy system models in high temporal and spatial resolution," Applied Energy, Elsevier, vol. 264(C).
    99. Holzmann, Tim & Smith, J.C., 2018. "Solving discrete multi-objective optimization problems using modified augmented weighted Tchebychev scalarizations," European Journal of Operational Research, Elsevier, vol. 271(2), pages 436-449.

  3. Mavrotas, George & Florios, Kostas & Vlachou, Dimitra, 2010. "Energy planning of a hospital using Mathematical Programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters," MPRA Paper 105754, University Library of Munich, Germany.

    Cited by:

    1. Morgan Bazilian & Debabrata Chattopadhyay, 2015. "Considering Power System Planning in Fragile and Conflict States," Cambridge Working Papers in Economics 1530, Faculty of Economics, University of Cambridge.
    2. Akbari, Kaveh & Jolai, Fariborz & Ghaderi, Seyed Farid, 2016. "Optimal design of distributed energy system in a neighborhood under uncertainty," Energy, Elsevier, vol. 116(P1), pages 567-582.
    3. Mavrotas, George & Figueira, José Rui & Siskos, Eleftherios, 2015. "Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection," Omega, Elsevier, vol. 52(C), pages 142-155.
    4. Dominik Kryzia & Marta Kuta & Dominika Matuszewska & Piotr Olczak, 2020. "Analysis of the Potential for Gas Micro-Cogeneration Development in Poland Using the Monte Carlo Method," Energies, MDPI, vol. 13(12), pages 1-24, June.
    5. Caralis, George & Diakoulaki, Danae & Yang, Peijin & Gao, Zhiqiu & Zervos, Arthouros & Rados, Kostas, 2014. "Profitability of wind energy investments in China using a Monte Carlo approach for the treatment of uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 224-236.
    6. Kang, Jing & Wang, Shengwei, 2018. "Robust optimal design of distributed energy systems based on life-cycle performance analysis using a probabilistic approach considering uncertainties of design inputs and equipment degradations," Applied Energy, Elsevier, vol. 231(C), pages 615-627.
    7. Pablo Benalcazar & Przemysław Kaszyński & Jacek Kamiński, 2021. "Assessing the Effects of Uncertain Energy and Carbon Prices on the Operational Patterns and Economic Results of CHP Systems," Energies, MDPI, vol. 14(24), pages 1-19, December.
    8. Capozzoli, Alfonso & Piscitelli, Marco Savino & Neri, Francesco & Grassi, Daniele & Serale, Gianluca, 2016. "A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres," Applied Energy, Elsevier, vol. 171(C), pages 592-607.
    9. Pizzuti, Andrea & Jin, Lingkang & Rossi, Mosè & Marinelli, Fabrizio & Comodi, Gabriele, 2024. "A novel approach for multi-stage investment decisions and dynamic variations in medium-term energy planning for multi-energy carriers community," Applied Energy, Elsevier, vol. 353(PB).
    10. Zhigang Duan & Yamin Yan & Xiaohan Yan & Qi Liao & Wan Zhang & Yongtu Liang & Tianqi Xia, 2017. "An MILP Method for Design of Distributed Energy Resource System Considering Stochastic Energy Supply and Demand," Energies, MDPI, vol. 11(1), pages 1-23, December.
    11. Vaziri, Shabnam Mahmoudzadeh & Rezaee, Babak & Monirian, Masoud Amel, 2020. "Utilizing renewable energy sources efficiently in hospitals using demand dispatch," Renewable Energy, Elsevier, vol. 151(C), pages 551-562.
    12. Mohseni, Soheil & Brent, Alan C. & Burmester, Daniel, 2020. "A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid," Applied Energy, Elsevier, vol. 259(C).
    13. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 214(C), pages 219-238.
    14. Karmellos, M. & Georgiou, P.N. & Mavrotas, G., 2019. "A comparison of methods for the optimal design of Distributed Energy Systems under uncertainty," Energy, Elsevier, vol. 178(C), pages 318-333.
    15. Song, Tangnyu & Huang, Guohe & Zhou, Xiong & Wang, Xiuquan, 2018. "An inexact two-stage fractional energy systems planning model," Energy, Elsevier, vol. 160(C), pages 275-289.
    16. Dong, Cong & Huang, Guohe & Cai, Yanpeng & Cheng, Guanhui & Tan, Qian, 2016. "Bayesian interval robust optimization for sustainable energy system planning in Qiqihar City, China," Energy Economics, Elsevier, vol. 60(C), pages 357-376.
    17. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    18. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    19. Zheng, Donglin & Yu, Lijun & Wang, Lizhen, 2019. "A techno-economic-risk decision-making methodology for large-scale building energy efficiency retrofit using Monte Carlo simulation," Energy, Elsevier, vol. 189(C).
    20. Tao Zhang & Minli Wang & Peihong Wang & Junyu Liang, 2020. "Optimal Design of a Combined Cooling, Heating, and Power System and Its Ability to Adapt to Uncertainty," Energies, MDPI, vol. 13(14), pages 1-17, July.
    21. Wei, F. & Wu, Q.H. & Jing, Z.X. & Chen, J.J. & Zhou, X.X., 2016. "Optimal unit sizing for small-scale integrated energy systems using multi-objective interval optimization and evidential reasoning approach," Energy, Elsevier, vol. 111(C), pages 933-946.
    22. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    23. Pablo Benalcazar & Jacek Kamiński & Karol Stós, 2022. "An Integrated Approach to Long-Term Fuel Supply Planning in Combined Heat and Power Systems," Energies, MDPI, vol. 15(22), pages 1-22, November.
    24. Zhu, H. & Huang, W.W. & Huang, G.H., 2014. "Planning of regional energy systems: An inexact mixed-integer fractional programming model," Applied Energy, Elsevier, vol. 113(C), pages 500-514.
    25. Afzali, Sayyed Faridoddin & Cotton, James S. & Mahalec, Vladimir, 2020. "Urban community energy systems design under uncertainty for specified levels of carbon dioxide emissions," Applied Energy, Elsevier, vol. 259(C).
    26. Petkov, Ivalin & Gabrielli, Paolo, 2020. "Power-to-hydrogen as seasonal energy storage: an uncertainty analysis for optimal design of low-carbon multi-energy systems," Applied Energy, Elsevier, vol. 274(C).

  4. Mavrotas, George & Figueira, José Rui & Florios, Kostas, 2009. "Solving the bi-objective multidimensional knapsack problem exploiting the concept of core," MPRA Paper 105087, University Library of Munich, Germany.

    Cited by:

    1. Rong, Aiying & Figueira, José Rui, 2013. "A reduction dynamic programming algorithm for the bi-objective integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 231(2), pages 299-313.
    2. Rong, Aiying & Figueira, José Rui, 2014. "Dynamic programming algorithms for the bi-objective integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 236(1), pages 85-99.
    3. Mavrotas, George & Florios, Kostas & Figueira, José Rui, 2015. "An improved version of a core based algorithm for the multi-objective multi-dimensional knapsack problem: A computational study and comparison with meta-heuristics," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 25-43.
    4. Wishon, Christopher & Villalobos, J. Rene, 2016. "Robust efficiency measures for linear knapsack problem variants," European Journal of Operational Research, Elsevier, vol. 254(2), pages 398-409.
    5. Bas, Esra, 2011. "An investment plan for preventing child injuries using risk priority number of failure mode and effects analysis methodology and a multi-objective, multi-dimensional mixed 0-1 knapsack model," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 748-756.

Articles

  1. Bilias, Yannis & Florios, Kostas & Skouras, Spyros, 2019. "Exact computation of Censored Least Absolute Deviations estimator," Journal of Econometrics, Elsevier, vol. 212(2), pages 584-606.

    Cited by:

    1. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Exact computation of GMM estimators for instrumental variable quantile regression models," CeMMAP working papers CWP52/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

  2. K. Florios & I. Moustaki & D. Rizopoulos & V. Vasdekis, 2015. "A modified weighted pairwise likelihood estimator for a class of random effects models," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 217-228, August.

    Cited by:

    1. Marco Alfó & Francesco Bartolucci, 2015. "Latent variable models for the analysis of socio-economic data," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 151-154, August.

  3. Mavrotas, George & Florios, Kostas & Figueira, José Rui, 2015. "An improved version of a core based algorithm for the multi-objective multi-dimensional knapsack problem: A computational study and comparison with meta-heuristics," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 25-43.

    Cited by:

    1. Hadi Jahangir & Mohammad Mohammadi & Seyed Hamid Reza Pasandideh & Neda Zendehdel Nobari, 2019. "Comparing performance of genetic and discrete invasive weed optimization algorithms for solving the inventory routing problem with an incremental delivery," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2327-2353, August.
    2. I. Kaliszewski & J. Miroforidis, 2022. "Probing the Pareto front of a large-scale multiobjective problem with a MIP solver," Operational Research, Springer, vol. 22(5), pages 5617-5673, November.
    3. Hedieh Sajedi & Seyedeh Fatemeh Razavi, 2017. "DGSA: discrete gravitational search algorithm for solving knapsack problem," Operational Research, Springer, vol. 17(2), pages 563-591, July.
    4. I. Kaliszewski & J. Miroforidis, 2021. "Cooperative multiobjective optimization with bounds on objective functions," Journal of Global Optimization, Springer, vol. 79(2), pages 369-385, February.

  4. Florios, Kostas & Mavrotas, George & Diakoulaki, Danae, 2010. "Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 203(1), pages 14-21, May.

    Cited by:

    1. Brester Christina & Ryzhikov Ivan & Semenkin Eugene, 2017. "Multi-objective Optimization Algorithms with the Island Metaheuristic for Effective Project Management Problem Solving," Organizacija, Sciendo, vol. 50(4), pages 364-373, December.
    2. Rong, Aiying & Figueira, José Rui, 2013. "A reduction dynamic programming algorithm for the bi-objective integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 231(2), pages 299-313.
    3. Tsionas, Mike G., 2019. "Multi-objective optimization using statistical models," European Journal of Operational Research, Elsevier, vol. 276(1), pages 364-378.
    4. Rong, Aiying & Figueira, José Rui, 2014. "Dynamic programming algorithms for the bi-objective integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 236(1), pages 85-99.
    5. Audrey Cerqueus & Xavier Gandibleux & Anthony Przybylski & Frédéric Saubion, 2017. "On branching heuristics for the bi-objective 0/1 unidimensional knapsack problem," Journal of Heuristics, Springer, vol. 23(5), pages 285-319, October.
    6. Przybylski, Anthony & Gandibleux, Xavier, 2017. "Multi-objective branch and bound," European Journal of Operational Research, Elsevier, vol. 260(3), pages 856-872.
    7. Mavrotas, George & Florios, Kostas, 2013. "An improved version of the augmented epsilon-constraint method (AUGMECON2) for finding the exact Pareto set in Multi-Objective Integer Programming problems," MPRA Paper 105034, University Library of Munich, Germany.
    8. Hedieh Sajedi & Seyedeh Fatemeh Razavi, 2017. "DGSA: discrete gravitational search algorithm for solving knapsack problem," Operational Research, Springer, vol. 17(2), pages 563-591, July.
    9. Harris, Irina & Mumford, Christine L. & Naim, Mohamed M., 2014. "A hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modeling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 66(C), pages 1-22.
    10. Forget, Nicolas & Gadegaard, Sune Lauth & Nielsen, Lars Relund, 2022. "Warm-starting lower bound set computations for branch-and-bound algorithms for multi objective integer linear programs," European Journal of Operational Research, Elsevier, vol. 302(3), pages 909-924.
    11. Sune Lauth Gadegaard & Lars Relund Nielsen & Matthias Ehrgott, 2019. "Bi-objective Branch-and-Cut Algorithms Based on LP Relaxation and Bound Sets," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 790-804, October.
    12. Mavrotas, George & Florios, Kostas & Figueira, José Rui, 2015. "An improved version of a core based algorithm for the multi-objective multi-dimensional knapsack problem: A computational study and comparison with meta-heuristics," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 25-43.
    13. Madjid Tavana & Kaveh Khalili-Damghani & Amir-Reza Abtahi, 2013. "A fuzzy multidimensional multiple-choice knapsack model for project portfolio selection using an evolutionary algorithm," Annals of Operations Research, Springer, vol. 206(1), pages 449-483, July.
    14. García-Martínez, C. & Rodriguez, F.J. & Lozano, M., 2014. "Tabu-enhanced iterated greedy algorithm: A case study in the quadratic multiple knapsack problem," European Journal of Operational Research, Elsevier, vol. 232(3), pages 454-463.
    15. Cerqueus, Audrey & Przybylski, Anthony & Gandibleux, Xavier, 2015. "Surrogate upper bound sets for bi-objective bi-dimensional binary knapsack problems," European Journal of Operational Research, Elsevier, vol. 244(2), pages 417-433.
    16. Bas, Esra, 2011. "An investment plan for preventing child injuries using risk priority number of failure mode and effects analysis methodology and a multi-objective, multi-dimensional mixed 0-1 knapsack model," Reliability Engineering and System Safety, Elsevier, vol. 96(7), pages 748-756.
    17. Mauricio Diéguez & Jaime Bustos & Carlos Cares, 0. "Mapping the variations for implementing information security controls to their operational research solutions," Information Systems and e-Business Management, Springer, vol. 0, pages 1-30.
    18. Mauricio Diéguez & Jaime Bustos & Carlos Cares, 2020. "Mapping the variations for implementing information security controls to their operational research solutions," Information Systems and e-Business Management, Springer, vol. 18(2), pages 157-186, June.
    19. Cacchiani, Valentina & D’Ambrosio, Claudia, 2017. "A branch-and-bound based heuristic algorithm for convex multi-objective MINLPs," European Journal of Operational Research, Elsevier, vol. 260(3), pages 920-933.

  5. Mavrotas, George & Diakoulaki, Danae & Florios, Kostas & Georgiou, Paraskevas, 2008. "A mathematical programming framework for energy planning in services' sector buildings under uncertainty in load demand: The case of a hospital in Athens," Energy Policy, Elsevier, vol. 36(7), pages 2415-2429, July.

    Cited by:

    1. Paul de Guibert & Behrang Shirizadeh & Philippe Quirion, 2020. "Variable time-step: A method for improving computational tractability for energy system models with long-term storage," Post-Print hal-03100309, HAL.
    2. Wang, Jiangjiang & Zhai, Zhiqiang (John) & Jing, Youyin & Zhang, Xutao & Zhang, Chunfa, 2011. "Sensitivity analysis of optimal model on building cooling heating and power system," Applied Energy, Elsevier, vol. 88(12), pages 5143-5152.
    3. Schütz, Thomas & Schraven, Markus Hans & Fuchs, Marcus & Remmen, Peter & Müller, Dirk, 2018. "Comparison of clustering algorithms for the selection of typical demand days for energy system synthesis," Renewable Energy, Elsevier, vol. 129(PA), pages 570-582.
    4. Akbari, Kaveh & Jolai, Fariborz & Ghaderi, Seyed Farid, 2016. "Optimal design of distributed energy system in a neighborhood under uncertainty," Energy, Elsevier, vol. 116(P1), pages 567-582.
    5. Zhou, Xiong & Huang, Guohe & Zhu, Hua & Chen, Jiapei & Xu, Jinliang, 2015. "Chance-constrained two-stage fractional optimization for planning regional energy systems in British Columbia, Canada," Applied Energy, Elsevier, vol. 154(C), pages 663-677.
    6. Stojiljković, Mirko M., 2017. "Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics," Energy, Elsevier, vol. 137(C), pages 1231-1251.
    7. Hu, Mengqi, 2015. "A data-driven feed-forward decision framework for building clusters operation under uncertainty," Applied Energy, Elsevier, vol. 141(C), pages 229-237.
    8. Hoffmann, Maximilian & Kotzur, Leander & Stolten, Detlef, 2022. "The Pareto-optimal temporal aggregation of energy system models," Applied Energy, Elsevier, vol. 315(C).
    9. Bahl, Björn & Kümpel, Alexander & Seele, Hagen & Lampe, Matthias & Bardow, André, 2017. "Time-series aggregation for synthesis problems by bounding error in the objective function," Energy, Elsevier, vol. 135(C), pages 900-912.
    10. Pérez-Iribarren, E. & González-Pino, I. & Azkorra-Larrinaga, Z. & Gómez-Arriarán, I., 2020. "Optimal design and operation of thermal energy storage systems in micro-cogeneration plants," Applied Energy, Elsevier, vol. 265(C).
    11. Hassan, Muhammed A. & Khalil, Adel & Abubakr, Mohamed, 2021. "Selection methodology of representative meteorological days for assessment of renewable energy systems," Renewable Energy, Elsevier, vol. 177(C), pages 34-51.
    12. Urban, Kristof L. & Scheller, Fabian & Bruckner, Thomas, 2021. "Suitability assessment of models in the industrial energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    13. Kotzur, Leander & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "Time series aggregation for energy system design: Modeling seasonal storage," Applied Energy, Elsevier, vol. 213(C), pages 123-135.
    14. Ashouri, Araz & Fux, Samuel S. & Benz, Michael J. & Guzzella, Lino, 2013. "Optimal design and operation of building services using mixed-integer linear programming techniques," Energy, Elsevier, vol. 59(C), pages 365-376.
    15. Hoffmann, Maximilian & Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron & Kotzur, Leander & Stolten, Detlef, 2021. "Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models," Applied Energy, Elsevier, vol. 304(C).
    16. Fazlollahi, Samira & Mandel, Pierre & Becker, Gwenaelle & Maréchal, Francois, 2012. "Methods for multi-objective investment and operating optimization of complex energy systems," Energy, Elsevier, vol. 45(1), pages 12-22.
    17. Lythcke-Jørgensen, Christoffer Ernst & Münster, Marie & Ensinas, Adriano Viana & Haglind, Fredrik, 2016. "A method for aggregating external operating conditions in multi-generation system optimization models," Applied Energy, Elsevier, vol. 166(C), pages 59-75.
    18. Wang, S. & Xie, Y.L. & Huang, G.H. & Yao, Y. & Wang, S.Y. & Li, Y.F., 2021. "A Structural Adjustment optimization model for electric-power system management under multiple Uncertainties—A case study of Urumqi city, China," Energy Policy, Elsevier, vol. 149(C).
    19. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    20. Pöstges, Arne & Weber, Christoph, 2019. "Time series aggregation – A new methodological approach using the “peak-load-pricing” model," Utilities Policy, Elsevier, vol. 59(C), pages 1-1.
    21. Teichgraeber, Holger & Lindenmeyer, Constantin P. & Baumgärtner, Nils & Kotzur, Leander & Stolten, Detlef & Robinius, Martin & Bardow, André & Brandt, Adam R., 2020. "Extreme events in time series aggregation: A case study for optimal residential energy supply systems," Applied Energy, Elsevier, vol. 275(C).
    22. Naraharisetti, Pavan Kumar & Karimi, I.A. & Anand, Abhay & Lee, Dong-Yup, 2011. "A linear diversity constraint – Application to scheduling in microgrids," Energy, Elsevier, vol. 36(7), pages 4235-4243.
    23. Mallikarjun, Sreekanth & Lewis, Herbert F., 2014. "Energy technology allocation for distributed energy resources: A strategic technology-policy framework," Energy, Elsevier, vol. 72(C), pages 783-799.
    24. Ren, Hongbo & Zhou, Weisheng & Nakagami, Ken'ichi & Gao, Weijun & Wu, Qiong, 2010. "Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects," Applied Energy, Elsevier, vol. 87(12), pages 3642-3651, December.
    25. Régis Delubac & Mohammad Sadr & Sabine Sochard & Sylvain Serra & Jean-Michel Reneaume, 2023. "Optimized Operation and Sizing of Solar District Heating Networks with Small Daily Storage," Energies, MDPI, vol. 16(3), pages 1-20, January.
    26. Yu, Nan & Kang, Jin-Su & Chang, Chung-Chuan & Lee, Tai-Yong & Lee, Dong-Yup, 2016. "Robust economic optimization and environmental policy analysis for microgrid planning: An application to Taichung Industrial Park, Taiwan," Energy, Elsevier, vol. 113(C), pages 671-682.
    27. Karmellos, M. & Georgiou, P.N. & Mavrotas, G., 2019. "A comparison of methods for the optimal design of Distributed Energy Systems under uncertainty," Energy, Elsevier, vol. 178(C), pages 318-333.
    28. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    29. Zhu, Y. & Huang, G.H. & Li, Y.P. & He, L. & Zhang, X.X., 2011. "An interval full-infinite mixed-integer programming method for planning municipal energy systems - A case study of Beijing," Applied Energy, Elsevier, vol. 88(8), pages 2846-2862, August.
    30. Ellen De Schepper & Steven Van Passel & Sebastien Lizin & Thomas Vincent & Benjamin Martin & Xavier Gandibleux, 2016. "Economic and environmental multi-objective optimisation to evaluate the impact of Belgian policy on solar power and electric vehicles," Journal of Environmental Economics and Policy, Taylor & Francis Journals, vol. 5(1), pages 1-27, March.
    31. Alfonso Marino & Paolo Pariso & Michele Picariello, 2023. "Energy use and End-use Technologies: Organizational and Energy Analysis in Italian Hospitals," International Journal of Energy Economics and Policy, Econjournals, vol. 13(3), pages 36-45, May.
    32. Moradi, Mohammad H. & Hajinazari, Mehdi & Jamasb, Shahriar & Paripour, Mahmoud, 2013. "An energy management system (EMS) strategy for combined heat and power (CHP) systems based on a hybrid optimization method employing fuzzy programming," Energy, Elsevier, vol. 49(C), pages 86-101.
    33. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    34. Tao Zhang & Minli Wang & Peihong Wang & Junyu Liang, 2020. "Optimal Design of a Combined Cooling, Heating, and Power System and Its Ability to Adapt to Uncertainty," Energies, MDPI, vol. 13(14), pages 1-17, July.
    35. Wen-Hsien Tsai & Chih-Hao Yang & Cheng-Tsu Huang & Yen-Ying Wu, 2017. "The impact of the carbon tax policy on green building strategy," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 60(8), pages 1412-1438, August.
    36. Kavvadias, K.C. & Maroulis, Z.B., 2010. "Multi-objective optimization of a trigeneration plant," Energy Policy, Elsevier, vol. 38(2), pages 945-954, February.
    37. Hailun Xie & Lars Johanning, 2023. "A Hierarchical Met-Ocean Data Selection Model for Fast O&M Simulation in Offshore Renewable Energy Systems," Energies, MDPI, vol. 16(3), pages 1-20, February.
    38. Safaei, Amir & Freire, Fausto & Antunes, Carlos Henggeler, 2013. "A model for optimal energy planning of a commercial building integrating solar and cogeneration systems," Energy, Elsevier, vol. 61(C), pages 211-223.
    39. Georgios P. Trachanas & Aikaterini Forouli & Nikolaos Gkonis & Haris Doukas, 2020. "Hedging uncertainty in energy efficiency strategies: a minimax regret analysis," Operational Research, Springer, vol. 20(4), pages 2229-2244, December.
    40. Rezvan, A. Taghipour & Gharneh, N. Shams & Gharehpetian, G.B., 2012. "Robust optimization of distributed generation investment in buildings," Energy, Elsevier, vol. 48(1), pages 455-463.
    41. Afzali, Sayyed Faridoddin & Cotton, James S. & Mahalec, Vladimir, 2020. "Urban community energy systems design under uncertainty for specified levels of carbon dioxide emissions," Applied Energy, Elsevier, vol. 259(C).
    42. Carvalho, Monica & Lozano, Miguel A. & Serra, Luis M., 2012. "Multicriteria synthesis of trigeneration systems considering economic and environmental aspects," Applied Energy, Elsevier, vol. 91(1), pages 245-254.
    43. Cai, Y.P. & Huang, G.H. & Yang, Z.F. & Tan, Q., 2009. "Identification of optimal strategies for energy management systems planning under multiple uncertainties," Applied Energy, Elsevier, vol. 86(4), pages 480-495, April.
    44. Mahdi Karami Darabi & Hamed Ganjeh Ganjehlou & Amirreza Jafari & Morteza Nazari-Heris & Gevork B. Gharehpetian & Mehrdad Abedi, 2021. "Evaluating the Effect of Demand Response Programs (DRPs) on Robust Optimal Sizing of Islanded Microgrids," Energies, MDPI, vol. 14(18), pages 1-20, September.
    45. Mirko M. Stojiljković & Mladen M. Stojiljković & Bratislav D. Blagojević, 2014. "Multi-Objective Combinatorial Optimization of Trigeneration Plants Based on Metaheuristics," Energies, MDPI, vol. 7(12), pages 1-28, December.

  6. Florios, Kostas & Skouras, Spyros, 2008. "Exact computation of max weighted score estimators," Journal of Econometrics, Elsevier, vol. 146(1), pages 86-91, September.

    Cited by:

    1. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    2. Le-Yu Chen & Sokbae (Simon) Lee, 2017. "Exact computation of GMM estimators for instrumental variable quantile regression models," CeMMAP working papers CWP52/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Le-Yu Chen & Sokbae (Simon) Lee & Myung Jae Sung, 2014. "Maximum score estimation with nonparametrically generated regressors," CeMMAP working papers CWP27/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Le-Yu Chen & Sokbae Lee, 2016. "Best Subset Binary Prediction," Papers 1610.02738, arXiv.org, revised May 2018.
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    6. Max Tabord-Meehan, 2023. "Stratification Trees for Adaptive Randomisation in Randomised Controlled Trials," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2646-2673.
    7. Chen, Le-Yu & Oparina, Ekaterina & Powdthavee, Nattavudh & Srisuma, Sorawoot, 2019. "Have Econometric Analyses of Happiness Data Been Futile? A Simple Truth about Happiness Scales," IZA Discussion Papers 12152, Institute of Labor Economics (IZA).
    8. Toru Kitagawa & Aleksey Tetenov, 2015. "Who should be treated? Empirical welfare maximization methods for treatment choice," CeMMAP working papers 10/15, Institute for Fiscal Studies.
    9. Youngki Shin & Zvezdomir Todorov, 2021. "Exact Computation of Maximum Rank Correlation Estimator," Department of Economics Working Papers 2021-03, McMaster University.
    10. Stéphane Bonhomme & Martin Weidner, 2019. "Posterior average effects," CeMMAP working papers CWP43/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Dries Benoit & Rahim Alhamzawi & Keming Yu, 2013. "Bayesian lasso binary quantile regression," Computational Statistics, Springer, vol. 28(6), pages 2861-2873, December.
    12. Chen, Le-Yu & Oparina, Ekaterina & Powdthavee, Nattavudh & Srisuma, Sorawoot, 2022. "Robust Ranking of Happiness Outcomes: A Median Regression Perspective," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 672-686.
    13. Davide Viviano & Jelena Bradic, 2020. "Fair Policy Targeting," Papers 2005.12395, arXiv.org, revised Jun 2022.
    14. Christopher D. Walker, 2024. "A Bayesian Perspective on the Maximum Score Problem," Papers 2410.17153, arXiv.org.
    15. Chen, Songnian & Zhang, Hanghui, 2015. "Binary quantile regression with local polynomial smoothing," Journal of Econometrics, Elsevier, vol. 189(1), pages 24-40.
    16. Le-Yu Chen & Sokbae (Simon) Lee & Myung Jae Sung, 2013. "Maximum score estimation of preference parameters for a binary choice model under uncertainty," CeMMAP working papers CWP14/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    17. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2023. "Econometrics of Machine Learning Methods in Economic Forecasting," Papers 2308.10993, arXiv.org.
    18. Bilias, Yannis & Florios, Kostas & Skouras, Spyros, 2019. "Exact computation of Censored Least Absolute Deviations estimator," Journal of Econometrics, Elsevier, vol. 212(2), pages 584-606.
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    20. Stéphane Bonhomme & Martin Weidner, 2020. "Posterior average effects," CeMMAP working papers CWP49/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    21. Le-Yu Chen & Sokbae Lee, 2018. "High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization," Papers 1811.09540, arXiv.org.
    22. Toru Kitagawa & Aleksey Tetenov, 2017. "Who should be treated? Empirical welfare maximization methods for treatment choice," CeMMAP working papers 24/17, Institute for Fiscal Studies.

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