Publications

Papers in international journals

  • Lima, R. M., Conejo, A. J., Giraldi, L., Le Maitre, O., Hoteit, I., & Knio, O. M.. (2022). Risk-averse stochastic programming vs. adaptive robust optimization: a virtual power plant application. INFORMS Journal on Computing, 34(3), 1795-1818.
    [BibTeX] [Abstract] [Download PDF]

    This paper compares risk-averse optimization methods to address the self-scheduling and market involvement of a virtual power plant (VPP). The decision-making problem of the VPP involves uncertainty in the wind speed and electricity price forecast We focus on two methods: risk-averse two-stage stochastic programming (SP) and two-stage adaptive robust optimization (ARO). We investigate both methods concerning formulations, uncertainty and risk, decomposition algorithms, and their computational performance. To quantify the risk in SP, we use the conditional value at risk (CVaR) because it can resemble a worst race measure, which naturally links to ARO. We use two efficient implementations of the decomposition algorithms for SP and ARO; we assess (1) the operational results regarding first-stage decision variables, estimate of expected profit, and estimate of the CVaR of the profit and (2) their performance taking into consideration different sample sizes and risk management parameters. The results show that similar first-stage solutions am obtained depending on the risk parameterizations used in each formulation. Computationally, we identified three cases: (1) SP with a sample of 500 elements is competitive with ARO; (2) SP performance degrades comparing to the first rase and ARO fails to converge in four out of five risk parameters; (3) SP fails to converge, whereas ARO converges in three out of five risk parameters. Overall, these performance cases depend on the combined effect of deterministic and uncertain data and risk parameters. Summary of Contribution: The work presented in this manuscript is at the intersection of operations research and computer science, which are intrinsically related with the scope and mission of IJOC. From the operations research perspective, two methodologies for optimization under uncertainty are studied: risk-averse stochastic programming and adaptive robust optimization. These methodologies are illustrated using an energy scheduling problem. The study includes a comparison from the point of view of uncertainty modeling, formulations, decomposition methods, and analysis of solutions. From the computer science perspective, a careful implementation of decomposition methods using parallelization techniques and a sample average approximation methodology was done . A detailed comparison of the computational performance of both methods is performed. Finally, the conclusions allow establishing links between two alternative methodologies in operations research: stochastic programming and robust optimization.

    @article{lima2022,
    Abstract = {This paper compares risk-averse optimization methods to address the
    self-scheduling and market involvement of a virtual power plant (VPP).
    The decision-making problem of the VPP involves uncertainty in the wind
    speed and electricity price forecast We focus on two methods:
    risk-averse two-stage stochastic programming (SP) and two-stage adaptive
    robust optimization (ARO). We investigate both methods concerning
    formulations, uncertainty and risk, decomposition algorithms, and their
    computational performance. To quantify the risk in SP, we use the
    conditional value at risk (CVaR) because it can resemble a worst race
    measure, which naturally links to ARO. We use two efficient
    implementations of the decomposition algorithms for SP and ARO; we
    assess (1) the operational results regarding first-stage decision
    variables, estimate of expected profit, and estimate of the CVaR of the
    profit and (2) their performance taking into consideration different
    sample sizes and risk management parameters. The results show that
    similar first-stage solutions am obtained depending on the risk
    parameterizations used in each formulation. Computationally, we
    identified three cases: (1) SP with a sample of 500 elements is
    competitive with ARO; (2) SP performance degrades comparing to the first
    rase and ARO fails to converge in four out of five risk parameters; (3)
    SP fails to converge, whereas ARO converges in three out of five risk
    parameters. Overall, these performance cases depend on the combined
    effect of deterministic and uncertain data and risk parameters.
    Summary of Contribution: The work presented in this manuscript is at the
    intersection of operations research and computer science, which are
    intrinsically related with the scope and mission of IJOC. From the
    operations research perspective, two methodologies for optimization
    under uncertainty are studied: risk-averse stochastic programming and
    adaptive robust optimization. These methodologies are illustrated using
    an energy scheduling problem. The study includes a comparison from the
    point of view of uncertainty modeling, formulations, decomposition
    methods, and analysis of solutions. From the computer science
    perspective, a careful implementation of decomposition methods using
    parallelization techniques and a sample average approximation
    methodology was done . A detailed comparison of the computational
    performance of both methods is performed. Finally, the conclusions allow
    establishing links between two alternative methodologies in operations
    research: stochastic programming and robust optimization.},
    Address = {5521 RESEARCH PARK DR, SUITE 200, CATONSVILLE, MD 21228 USA},
    Affiliation = {Lima, RM (Corresponding Author), King Abdullah Univ Sci \& Technol, Comp Elect \& Math Sci \& Engn Div, Thuwal 239556900, Saudi Arabia. Lima, Ricardo M.; Knio, Omar M., King Abdullah Univ Sci \& Technol, Comp Elect \& Math Sci \& Engn Div, Thuwal 239556900, Saudi Arabia. Ohio State Univ, Dept Integrated Syst Engn, Columbus, OH 43210 USA. Ohio State Univ, Dept Elect \& Comp Engn, Columbus, OH 43210 USA. Res Inst Nucl Syst Low Carbon Energy Prod, Dept Etud Combustibles, Fuel Dept,Direct Energies,Enery Div,Inst Rech Sys, Commissariat Energie Atom \& Aux Energies Alterna, F-13108 St Paul Les Durance, France. Inria, Ecole Polytech, Ctr Natl Rech Sci, Ctr Math Appl, F-91128 Palaiseau, France. Hoteit, Ibrahim, King Abdullah Univ Sci \& Technol, Phys Sci \& Engn Div, Thuwal 239556900, Saudi Arabia.},
    Author = {Lima, Ricardo M. and Conejo, Antonio J. and Giraldi, Loic and Le Maitre, Olivier and Hoteit, Ibrahim and Knio, Omar M.},
    Author-Email = {ricardo.lima@kaust.edu.sa conejo.1@osu.edu loic.giraldi@cea.fr olivier.le-maitre@polytechnique.edu ibrahim.hoteit@kaust.edu.sa omar.knio@kaust.edu.sa},
    Da = {2022-06-16},
    Date-Added = {2022-06-16 17:15:15 +0300},
    Date-Modified = {2022-06-16 17:16:58 +0300},
    Doc-Delivery-Number = {1R9JX},
    %  Doi = {10.1287/ijoc.2022.1157},
    Earlyaccessdate = {FEB 2022},
    Eissn = {1526-5528},
    Funding-Acknowledgement = {King Abdullah University of Science and Technology (KAUST)},
    Funding-Text = {Research reported in this publication was supported by research funding from King Abdullah University of Science and Technology (KAUST) and computational resources from the Supercomputing Laboratory fromKAUST Core Labs.},
    Issn = {1091-9856},
    Journal = {{INFORMS Journal on Computing}},
    Journal-Iso = {INFORMS J. Comput.},
    Keywords = {stochastic programming; robust optimization; risk management; virtual power plant},
    Keywords-Plus = {UNIT COMMITMENT; FUTURES PRICES; ENERGY; CONTRACTS; DISPATCH; MARKETS; SPOT},
    Language = {English},
    Number = {3},
    Number-Of-Cited-References = {61},
    Oa = {Green Submitted},
    Orcid-Numbers = {Le Maitre, Olivier/0000-0002-3811-7787},
    Pages = {1795-1818},
    Publisher = {INFORMS},
    Research-Areas = {Computer Science; Operations Research \& Management Science},
    Researcherid-Numbers = {Le Maitre, Olivier/D-8570-2011},
    Times-Cited = {0},
    Title = {Risk-Averse Stochastic Programming vs. Adaptive Robust Optimization: A Virtual Power Plant Application},
    Type = {Article; Early Access},
    Unique-Id = {WOS:000803679200001},
    Usage-Count-Last-180-Days = {0},
    Usage-Count-Since-2013 = {0},
    Volume = {34},
    Web-Of-Science-Categories = {Computer Science, Interdisciplinary Applications; Operations Research \& Management Science},
    Web-Of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
    Year = {2022},
    Bdsk-Url-1 = {https://doi.org/10.1287/ijoc.2022.1157},
    Url = {https://pubsonline.informs.org/doi/abs/10.1287/ijoc.2022.1157}}

  • Souayfane, F., Lima, R. M., Dahrouj, H., & Knio, O.. (2022). A weather-clustering and energy-thermal comfort optimization methodology for indoor cooling in subtropical desert climates. Journal of building engineering, 51, 104327.
    [BibTeX] [Abstract] [Download PDF]

    This paper proposes a novel methodology to assess the yearly Heating Ventilation Air Conditioning (HVAC) energy costs and indoor comfort levels for indoor spaces. The methodology involves a weather clustering technique coupled with a simulation-based multi-objective optimization for HVAC systems operation control. The clustering technique is utilized to determine representative days that capture the yearly variability of outdoor air temperature, total solar radiation on horizontal surface, wind speed, and outdoor relative humidity from historical time series. The optimization framework then determines the optimal cooling operation strategies that simultaneously minimize energy consumption cost and thermal discomfort for each representative day. Such clustering-based approach, particularly, enables the assessment of the annual operation of the HVAC using representative daily weather conditions while avoiding the high computational costs of a day-by-day optimization. The numerical prospects of the proposed framework are illustrated using an office building located in Saudi Arabia, i.e., under subtropical desert conditions. The results show that the proposed methodology can achieve reductions of up to 17.6% and 19.4% in annual cooling consumption cost and thermal discomfort, respectively, compared to standard baseline policies.

    @article{souyfane2022,
    title = {A weather-clustering and energy-thermal comfort optimization methodology for indoor cooling in subtropical desert climates},
    journal = {Journal of Building Engineering},
    volume = {51},
    pages = {104327},
    year = {2022},
    issn = {2352-7102},
    %doi = {https://doi.org/10.1016/j.jobe.2022.104327},
    url = {https://www.sciencedirect.com/science/article/pii/S2352710222003400},
    author = {Farah Souayfane and Ricardo M. Lima and Hayssam Dahrouj and Omar Knio},
    keywords = {Simulation-based optimization, HVAC optimal control, Weather-clustering technique, Subtropical desert climate},
    abstract = {This paper proposes a novel methodology to assess the yearly Heating Ventilation Air Conditioning (HVAC) energy costs and indoor comfort levels for indoor spaces. The methodology involves a weather clustering technique coupled with a simulation-based multi-objective optimization for HVAC systems operation control. The clustering technique is utilized to determine representative days that capture the yearly variability of outdoor air temperature, total solar radiation on horizontal surface, wind speed, and outdoor relative humidity from historical time series. The optimization framework then determines the optimal cooling operation strategies that simultaneously minimize energy consumption cost and thermal discomfort for each representative day. Such clustering-based approach, particularly, enables the assessment of the annual operation of the HVAC using representative daily weather conditions while avoiding the high computational costs of a day-by-day optimization. The numerical prospects of the proposed framework are illustrated using an office building located in Saudi Arabia, i.e., under subtropical desert conditions. The results show that the proposed methodology can achieve reductions of up to 17.6% and 19.4% in annual cooling consumption cost and thermal discomfort, respectively, compared to standard baseline policies.}
    }

  • Riera, J. A., Lima, R. M., Hoteit, I., & Knio, O.. (2022). Simulated co-optimization of renewable energy and desalination systems in Neom, Saudi Arabia. Nature communications, 13(1), 3514.
    [BibTeX] [Abstract] [Download PDF]

    {The interdependence between the water and power sectors is a growing concern as the need for desalination increases globally. Therefore, co-optimizing interdependent systems is necessary to understand the impact of one sector on another. We propose a framework to identify the optimal investment mix for a co-optimized water-power system and apply it to Neom, Saudi Arabia. Our results show that investment strategies that consider the co-optimization of both systems result in total cost savings for the power sector compared to independent approaches. Analysis results suggest that systems with higher shares of non-dispatchable renewables experience the most significant cost reductions. Co-optimization of renewable power and water desalination systems for Neom, a futuristic seaside city in an arid region, results in more pronounced cost investment savings for a high share of renewable sources.}

    @article{riera2022,
    year = {2022},
    title = {{Simulated co-optimization of renewable energy and desalination systems in Neom, Saudi Arabia}},
    author = {Riera, Jefferson A and Lima, Ricardo M and Hoteit, Ibrahim and Knio, Omar},
    journal = {Nature Communications},
    url = {http://dx.doi.org/10.1038/s41467-022-31233-3},
    abstract = {{The interdependence between the water and power sectors is a growing concern as the need for desalination increases globally. Therefore, co-optimizing interdependent systems is necessary to understand the impact of one sector on another. We propose a framework to identify the optimal investment mix for a co-optimized water-power system and apply it to Neom, Saudi Arabia. Our results show that investment strategies that consider the co-optimization of both systems result in total cost savings for the power sector compared to independent approaches. Analysis results suggest that systems with higher shares of non-dispatchable renewables experience the most significant cost reductions. Co-optimization of renewable power and water desalination systems for Neom, a futuristic seaside city in an arid region, results in more pronounced cost investment savings for a high share of renewable sources.}},
    pages = {3514},
    number = {1},
    volume = {13}
    }

  • Albarakati, S., Lima, R. M., Theußl, T., Hoteit, I., & Knio, O.. (2021). Multi-objective risk-aware path planning in uncertain transient currents: an ensemble-based stochastic optimization approach. IEEE Journal of Oceanic Engineering.
    [BibTeX] [Abstract] [Download PDF]

    {In this paper, we consider the autonomous underwater vehicle (AUV) trajectory planning problem under the influence of a realistic 3D current as simulated by an ocean general circulation model (OGCM). Attention is focused on the case of a deterministic steady OGCM field, which is used to specify data for both the ocean current and for ocean bathymetry. A general framework for optimal trajectory planning is developed for this setting, accounting for the 3D ocean current and for static obstacle avoidance constraints. A nonlinear programming approach is used for this purpose, which leads to a low complexity discrete-time model that can be efficiently solved. To demonstrate the efficiency of the model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and Gulf of Aden, with velocity, and bathymetric data provided by an eddy-resolving MITgcm. Different optimal-time trajectory planning scenarios are implemented to demonstrate the capabilities of the model to identify trajectories that adapt to favorable and adverse currents and to avoid obstacles corresponding to a complex bathymetry environment. The simulations are also used to evaluate the performance of the proposed approach, and to illustrate the application of advanced visualization tools to interpret the model predictions.}

    @article{temp_risk_albarakati2020,
    Abstract = {{In this paper, we consider the autonomous underwater vehicle (AUV)
    trajectory planning problem under the influence of a realistic 3D
    current as simulated by an ocean general circulation model (OGCM).
    Attention is focused on the case of a deterministic steady OGCM field,
    which is used to specify data for both the ocean current and for ocean
    bathymetry. A general framework for optimal trajectory planning is
    developed for this setting, accounting for the 3D ocean current and for
    static obstacle avoidance constraints. A nonlinear programming approach
    is used for this purpose, which leads to a low complexity discrete-time
    model that can be efficiently solved. To demonstrate the efficiency of
    the model, we consider the optimal time trajectory planning of an AUV
    operating in the Red Sea and Gulf of Aden, with velocity, and
    bathymetric data provided by an eddy-resolving MITgcm. Different
    optimal-time trajectory planning scenarios are implemented to
    demonstrate the capabilities of the model to identify trajectories that
    adapt to favorable and adverse currents and to avoid obstacles
    corresponding to a complex bathymetry environment. The simulations are
    also used to evaluate the performance of the proposed approach, and to
    illustrate the application of advanced visualization tools to interpret
    the model predictions.}},
    Author = {Albarakati, Sultan and Lima, Ricardo M. and Theu{\ss}l, Thomas and Hoteit, Ibrahim and Knio, Omar},
    Date-Added = {2021-01-18 14:26:04 +0300},
    Date-Modified = {2021-01-18 14:29:09 +0300},
    Note = {{(Accepted).}},
    Title = {Multi-Objective Risk-aware Path Planning in Uncertain Transient Currents: an Ensemble-Based Stochastic Optimization Approach},
    Journal = {{IEEE Journal of Oceanic Engineering}},
    Url = "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9419859",
    Year = 2021}

  • Lima, R. M., Conejo, A. J., Giraldi, L., Le Maître, O., Hoteit, I., & Knio, O. M.. (2021). Sample average approximation for risk-averse problems: a virtual power plant scheduling application. EURO Journal on Computational Optimization, 9, 100005.
    [BibTeX] [Abstract] [Download PDF]

    {In this paper, we address the decision-making problem of a virtual power plant (VPP) involving a self-scheduling and market involvement problem under uncertainty in the wind speed and electricity prices. The problem is modeled using a risk-neutral and two risk-averse two-stage stochastic programming formulations, where the conditional value at risk is used to represent risk. A sample average approximation methodology is integrated with an adapted L-Shaped solution method, which can solve risk-neutral and specific risk-averse problems. This methodology provides a framework to understand and quantify the impact of the sample size on the variability of the results. The numerical results include an analysis of the computational performance of the methodology for two case studies, estimators for the bounds of the true optimal solutions of the problems, and an assessment of the quality of the solutions obtained. In particular, numerical experiences indicate that when an adequate sample size is used, the solution obtained is close to the optimal one.}

    @article{lima2021,
    Abstract = {{In this paper, we address the decision-making problem of a virtual power plant (VPP) involving a self-scheduling and market involvement problem under uncertainty in the wind speed and electricity prices. The problem is modeled using a risk-neutral and two risk-averse two-stage stochastic programming formulations, where the conditional value at risk is used to represent risk. A sample average approximation methodology is integrated with an adapted L-Shaped solution method, which can solve risk-neutral and specific risk-averse problems. This methodology provides a framework to understand and quantify the impact of the sample size on the variability of the results. The numerical results include an analysis of the computational performance of the methodology for two case studies, estimators for the bounds of the true optimal solutions of the problems, and an assessment of the quality of the solutions obtained. In particular, numerical experiences indicate that when an adequate sample size is used, the solution obtained is close to the optimal one.}},
    Author = {Lima, R. M. and Conejo, A. J. and Giraldi, L. and Le Maître, O. and Hoteit, I. and Knio, O. M.},
    Issn = {2192-4406},
    Journal = {{EURO Journal on Computational Optimization}},
    Keywords = {Sample average approximation, Risk-averse stochastic programming, Virtual power plant},
    Pages = {100005},
    Title = {Sample average approximation for risk-averse problems: A virtual power plant scheduling application},
    Url = {https://www.sciencedirect.com/science/article/pii/S2192440621000022},
    Volume = {9},
    Year = {2021}
    }

  • Alraddadi, M., Conejo, A. J., & Lima, R. M.. (2020). Expansion planning for renewable integration in power system of regions with very high solar irradiation. Journal of Modern Power Systems and Clean Energy.
    [BibTeX] [Abstract] [Download PDF]

    {In this paper, we address the long-term generation and transmission expansion planning problem of power systems in regions with very high solar irradiation. We target systems that currently rely mainly on thermal generators and that aim to adopt high shares of renewable sources. We propose a stochastic optimization model with expansion alternatives including transmission lines, solar power plants (photovoltaic and concentrated solar), wind farms, energy storage, and flexible combined cycle gas turbines. The model represents long-term uncertainty to characterize the demand growth, and short-term uncertainty to characterize daily solar, wind, and demand patterns. We use the Saudi Arabia power system to illustrate the functioning of the proposed model for several cases with different renewable integration targets. The results show that a strong dependence on solar power for high shares of renewables requires high generation capacity and storage to meet the night demand.}

    @article{tempmusfer2019,
    Author = {Musfer Alraddadi and Antonio J. Conejo and Ricardo M. Lima},
    Date-Added = {2020-04-08 17:06:07 +0300},
    Date-Modified = {2020-05-11 11:15:07 +0300},
    Journal = {{Journal of Modern Power Systems and Clean Energy}},
    Note = {{(Accepted).}},
    Title = {Expansion Planning for Renewable Integration in Power System of Regions with Very High Solar Irradiation},
    url ={https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9198218},
    abstract = {{In this paper, we address the long-term generation and transmission expansion planning problem of power systems in regions with very high solar irradiation. We target systems that currently rely mainly on thermal generators and that aim to adopt high shares of renewable sources. We propose a stochastic optimization model with expansion alternatives including transmission lines, solar power plants (photovoltaic and concentrated solar), wind farms, energy storage, and flexible combined cycle gas turbines. The model represents long-term uncertainty to characterize the demand growth, and short-term uncertainty to characterize daily solar, wind, and demand patterns. We use the Saudi Arabia power system to illustrate the functioning of the proposed model for several cases with different renewable integration targets. The results show that a strong dependence on solar power for high shares of renewables requires high generation capacity and storage to meet the night demand.}},
    Year = {2020}}

  • Albarakati, S., Lima, R. M., Theußl, T., Hoteit, I., & Knio, O.. (2020). Optimal 3D time-energy trajectory planning for AUVs using ocean general circulation models. Ocean Engineering, 218.
    [BibTeX] [Abstract] [Download PDF]

    {This paper develops a new approach for solving optimal time and energy trajectory planning problems for Autonomous Underwater Vehicles (AUVs) in transient, 3D, ocean currents. Realistic forecasts using an Ocean General Circulation Model (OGCM) are used for this purpose. The approach is based on decomposing the problem into a minimal time problem, followed by minimal energy subproblems. In both cases, a non-linear programming (NLP) formulation is adopted. The scheme is demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In particular, the numerical experiments illustrate the capability of generating Pareto optimal solutions in a broad range of mission durations. In addition, the analysis also highlights how the methodology effectively exploits both the vertical structure of the current field, as well as its unsteadiness, namely to minimize travel time and energy consumption.}

    @article{albarakati2020,
    Abstract = {{This paper develops a new approach for solving optimal time and energy trajectory planning problems for Autonomous Underwater Vehicles (AUVs) in transient, 3D, ocean currents. Realistic forecasts using an Ocean General Circulation Model (OGCM) are used for this purpose. The approach is based on decomposing the problem into a minimal time problem, followed by minimal energy subproblems. In both cases, a non-linear programming (NLP) formulation is adopted. The scheme is demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In particular, the numerical experiments illustrate the capability of generating Pareto optimal solutions in a broad range of mission durations. In addition, the analysis also highlights how the methodology effectively exploits both the vertical structure of the current field, as well as its unsteadiness, namely to minimize travel time and energy consumption.}},
    Article-Number = {108057},
    Author = {Albarakati, Sultan and Lima, Ricardo M. and Theu{\ss}l, Thomas and Hoteit, Ibrahim and Knio, Omar},
    Journal = {{Ocean Engineering}},
    Keywords = {Time-energy trajectory planningOcean general circulation modelGulf of adenPareto optimal solutionsUnsteady ocean current},
    Title = {Optimal 3{D} Time-Energy Trajectory Planning for {AUV}s using Ocean General Circulation Models},
    Url = "https://doi.org/10.1016/j.oceaneng.2020.108057",
    Volume = {218},
    Year = 2020}

  • Wang, T., Lima, R. M., Giraldi, L., & Knio, O. M.. (2019). Trajectory planning for autonomous underwater vehicles in the presence of obstacles and a nonlinear flow field using mixed integer nonlinear programming. Computers & Operations Research, 101, 55-75.
    [BibTeX] [Abstract] [Download PDF]

    This paper addresses the time-optimal trajectory planning for autonomous underwater vehicles. A detailed mixed integer nonlinear programming (MINLP) model is presented, explicitly taking into account vehicle kinematic constraints, obstacle avoidance, and a nonlinear flow field to represent the ocean current. MINLP problems pose great challenges because of the combinatorial complexity and nonconvexities introduced by the nature of the flow field. A novel solution approach in an optimization framework is developed to address associated difficulties. The main benefit of the proposed methodology is the ability to find multiple local minima. The contribution of the paper is fourfold: (1) a novel approach to integrate the flow field into the MINLP model; (2) a diversified initialization strategy using multiple waypoints, different solvers and approximated models, namely, a mixed integer linear programming model and the MINLP model with and without the flow field; (3) an algorithm that forces the solver to seek improved solutions; and (4) a parallel computing approach capitalizing on diversified initialization. The performance of the resulting methodology is illustrated on idealized case studies, and the results are used to gain insight into trajectory planning in the presence of flow fields.

    @article{wang2019,
    Author = {Tong Wang and Ricardo M. Lima and Giraldi, L. and Omar M. Knio},
    Date-Added = {2018-09-18 06:27:35 +0000},
    Date-Modified = {2018-09-18 06:27:58 +0000},
    OPTDoi = {https://doi.org/10.1016/j.cor.2018.08.008},
    Issn = {0305-0548},
    Journal = {{Computers & Operations Research}},
    Keywords = {Optimal trajectory planning, MINLP, MILP},
    Pages = {55 - 75},
    Title = {Trajectory planning for autonomous underwater vehicles in the presence of obstacles and a nonlinear flow field using mixed integer nonlinear programming},
    Url = {http://www.sciencedirect.com/science/article/pii/S0305054818302272},
    Volume = {101},
    Year = {2019},
    abstract = "This paper addresses the time-optimal trajectory planning for autonomous underwater vehicles. A detailed mixed integer nonlinear programming (MINLP) model is presented, explicitly taking into account vehicle kinematic constraints, obstacle avoidance, and a nonlinear flow field to represent the ocean current. MINLP problems pose great challenges because of the combinatorial complexity and nonconvexities introduced by the nature of the flow field. A novel solution approach in an optimization framework is developed to address associated difficulties. The main benefit of the proposed methodology is the ability to find multiple local minima. The contribution of the paper is fourfold: (1) a novel approach to integrate the flow field into the MINLP model; (2) a diversified initialization strategy using multiple waypoints, different solvers and approximated models, namely, a mixed integer linear programming model and the MINLP model with and without the flow field; (3) an algorithm that forces the solver to seek improved solutions; and (4) a parallel computing approach capitalizing on diversified initialization. The performance of the resulting methodology is illustrated on idealized case studies, and the results are used to gain insight into trajectory planning in the presence of flow fields."
    }

  • Albarakati, S., Lima, R. M., Giraldi, L., Hoteit, I., & Knio, O.. (2019). Optimal 3D Trajectory Planning for AUVs using Ocean General Circulation Models. Ocean Engineering, 188.
    [BibTeX] [Abstract] [Download PDF]

    In this paper, we consider the autonomous underwater vehicle (AUV) trajectory planning problem under the influence of a realistic 3D current as simulated by an ocean general circulation model (OGCM). Attention is focused on the case of a deterministic steady OGCM field, which is used to specify data for both the ocean current and for ocean bathymetry. A general framework for optimal trajectory planning is developed for this setting, accounting for the 3D ocean current and for static obstacle avoidance constraints. A nonlinear programming approach is used for this purpose, which leads to a low complexity discrete-time model that can be efficiently solved. To demonstrate the efficiency of the model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and Gulf of Aden, with velocity, and bathymetric data provided by an eddy-resolving MITgcm. Different optimal-time trajectory planning scenarios are implemented to demonstrate the capabilities of the model to identify trajectories that adapt to favorable and adverse currents and to avoid obstacles corresponding to a complex bathymetry environment. The simulations are also used to evaluate the performance of the proposed approach, and to illustrate the application of advanced visualization tools to interpret the model predictions.

    @article{albarakati2019,
    author = {Sultan Albarakati and Ricardo M. Lima and Lo{\"i}c Giraldi and Ibrahim Hoteit and Omar Knio },
    title = {{Optimal 3D Trajectory Planning for AUVs using Ocean General Circulation Models}},
    journal = {{Ocean Engineering}},
    year = {2019},
    OPTkey = {},
    volume = {188},
    OPTnumber = {},
    OPTpages = {},
    OPTmonth = {},
    url = "http://www.sciencedirect.com/science/article/pii/S0029801819304421",
    abstract = {In this paper, we consider the autonomous underwater vehicle (AUV) trajectory planning problem under the influence of a realistic 3D current as simulated by an ocean general circulation model (OGCM). Attention is focused on the case of a deterministic steady OGCM field, which is used to specify data for both the ocean current and for ocean bathymetry. A general framework for optimal trajectory planning is developed for this setting, accounting for the 3D ocean current and for static obstacle avoidance constraints. A nonlinear programming approach is used for this purpose, which leads to a low complexity discrete-time model that can be efficiently solved. To demonstrate the efficiency of the model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and Gulf of Aden, with velocity, and bathymetric data provided by an eddy-resolving MITgcm. Different optimal-time trajectory planning scenarios are implemented to demonstrate the capabilities of the model to identify trajectories that adapt to favorable and adverse currents and to avoid obstacles corresponding to a complex bathymetry environment. The simulations are also used to evaluate the performance of the proposed approach, and to illustrate the application of advanced visualization tools to interpret the model predictions.}
    }

  • Lima, R. M., Conejo, A. J., Langodan, S., Hoteit, I., & Knio, O. M.. (2018). Risk-averse formulations and methods for a virtual power plant. Computers & Operations Research, 96, 350-373.
    [BibTeX] [Download PDF]
    @article{Lima2018,
    title = "Risk-averse formulations and methods for a virtual power plant",
    journal = {{Computers & Operations Research}},
    volume = "96",
    pages = "350 - 373",
    year = "2018",
    issn = "0305-0548",
    url = "http://www.sciencedirect.com/science/article/pii/S0305054817303076",
    author = "Ricardo M. Lima and Antonio J. Conejo and Sabique Langodan and Ibrahim Hoteit and Omar M. Knio",
    keywords = "Optimization under uncertainty, Stochastic programming, Conditional value at risk, Energy, Virtual power plant"
    }

  • Lima, R. M., & Grossmann, I. E.. (2017). On the solution of nonconvex cardinality boolean quadratic programming problems: a computational study. Computational Optimization and Applications, 66(1), 1–37.
    [BibTeX] [Download PDF]
    @article{lima2017a,
    Author = {Lima, Ricardo M. and Grossmann, Ignacio E.},
    OPTDOI = {10.1007/s10589-016-9856-7},
    Issn = {1573-2894},
    Journal = {{Computational Optimization and Applications}},
    Pages = {1--37},
    Title = {On the solution of nonconvex cardinality Boolean
    quadratic programming problems: a computational
    study},
    OPTDOI = {http://dx.doi.org/10.1007/s10589-016-9856-7},
    Year = 2017,
    Number = 1,
    Volume = 66,
    Url = {http://dx.doi.org/10.1007/s10589-016-9856-7},
    }

  • Lima, R. M., & Novais, A. Q.. (2016). Symmetry breaking in MILP formulations for Unit Commitment problems. Computers & Chemical Engineering, 85, 162-176.
    [BibTeX] [Download PDF]
    @article{lima2016a,
    Author = {Lima, Ricardo M. and Novais, Augusto Q.},
    OPTDOI = {10.1016/j.compchemeng.2015.11.004},
    Eissn = {1873-4375},
    Issn = {0098-1354},
    Journal = {{Computers \& Chemical Engineering}},
    Month = {FEB 2},
    Orcid-Numbers ={Lima, Ricardo/0000-0002-5735-6089},
    Pages = {162-176},
    Researcherid-Numbers ={Lima, Ricardo/M-5164-2013},
    Times-Cited = 0,
    Title = {{Symmetry breaking in MILP formulations for Unit
    Commitment problems}},
    Unique-Id = {ISI:000368540100016},
    Volume = 85,
    Year = 2016,
    Url = {http://dx.doi.org/10.1016/j.compchemeng.2015.11.004},
    }

  • Zhang, Q., Grossmann, I. E., & Lima, R. M.. (2016). On the relation between flexibility analysis and robust optimization for linear systems. AIChE Journal, 62(9), 3109–3123.
    [BibTeX] [Download PDF]
    @article {zhang2016,
    Author = {Zhang, Qi and Grossmann, Ignacio E. and Lima,
    Ricardo M.},
    Title = {On the relation between flexibility analysis and
    robust optimization for linear systems},
    Journal = {{AIChE Journal}},
    Volume = 62,
    Number = 9,
    ISSN = {1547-5905},
    Url = {http://dx.doi.org/10.1002/aic.15221},
    OPTDOI = {10.1002/aic.15221},
    Pages = {3109--3123},
    Keywords = {flexibility analysis, robust optimization,
    optimization under uncertainty},
    Year = 2016,
    }

  • Malheiro, A., Castro, P. M., Lima, R. M., & Estanqueiro, A.. (2015). Integrated sizing and scheduling of wind/PV/diesel/battery isolated systems. Renewable Energy, 83, 646-657.
    [BibTeX] [Download PDF]
    @article{malheiro2015,
    Author = {Malheiro, Andre and Castro, Pedro M. and Lima,
    Ricardo M. and Estanqueiro, Ana},
    Title = {{Integrated sizing and scheduling of
    wind/PV/diesel/battery isolated systems}},
    Journal = {{Renewable Energy}},
    Year = 2015,
    Volume = 83,
    Pages = {646-657},
    Month = {NOV},
    OPTDOI = {10.1016/j.renene.2015.04.066},
    Url = {http://dx.doi.org/10.1016/j.renene.2015.04.066},
    ISSN = {0960-1481},
    ResearcherID-Numbers ={Castro, Pedro/C-3642-2008 Estanqueiro,
    Ana/J-9752-2012 LNEG, Producao
    Cientifica/D-2212-2012 Lima, Ricardo/M-5164-2013},
    ORCID-Numbers ={Castro, Pedro/0000-0002-4898-8922 Estanqueiro,
    Ana/0000-0002-0476-2526 Lima,
    Ricardo/0000-0002-5735-6089},
    Times-Cited = 3,
    Unique-ID = {ISI:000358455100061}
    }

  • Lima, R. M., Novais, A. Q., & Conejo, A. J.. (2015). Weekly self-scheduling, forward contracting, and pool involvement for an electricity producer. An adaptive robust optimization approach. European Journal of Operational Research, 240(2), 457-475.
    [BibTeX] [Download PDF]
    @article{lima2015,
    Author = {Lima, Ricardo M. and Novais, Augusto Q. and Conejo,
    Antonio J.},
    OPTDOI = {10.1016/j.ejor.2014.07.013},
    Eissn = {1872-6860},
    Issn = {0377-2217},
    Journal = {{European Journal of Operational Research}},
    Month = {JAN 16},
    Number = 2,
    Orcid-Numbers ={Conejo, Antonio/0000-0002-2324-605X Lima,
    Ricardo/0000-0002-5735-6089},
    Pages = {457-475},
    Researcherid-Numbers ={Conejo, Antonio/I-2757-2012 LNEG, Producao
    Cientifica/D-2212-2012 Lima, Ricardo/M-5164-2013},
    Times-Cited = 6,
    Title = {{Weekly self-scheduling, forward contracting, and
    pool involvement for an electricity producer. An
    adaptive robust optimization approach}},
    Unique-Id = {ISI:000343347300017},
    Volume = 240,
    Year = 2015,
    Url = {http://dx.doi.org/10.1016/j.ejor.2014.07.013},
    }

  • Lima, R. M., Marcovecchio, M. G., Novais, A. Q., & Grossmann, I. E.. (2013). On the Computational Studies of Deterministic Global Optimization of Head Dependent Short-Term Hydro Scheduling. IEEE Transactions on Power Systems, 28(4), 4336-4347.
    [BibTeX] [Download PDF]
    @article{lima2013,
    Author = {Lima, Ricardo M. and Marcovecchio, Marian G. and
    Novais, Augusto Queiroz and Grossmann, Ignacio E.},
    OPTDOI = {10.1109/TPWRS.2013.2274559},
    Eissn = {1558-0679},
    Issn = {0885-8950},
    Journal = {{IEEE Transactions on Power Systems}},
    Month = {NOV},
    Number = 4,
    Orcid-Numbers ={Novais, Augusto/0000-0002-8440-0946 Lima,
    Ricardo/0000-0002-5735-6089},
    Pages = {4336-4347},
    Researcherid-Numbers ={LNEG, UMOSE/C-1701-2010 LNEG, Producao
    Cientifica/D-2212-2012 Lima, Ricardo/M-5164-2013},
    Times-Cited = 7,
    Title = {{On the Computational Studies of Deterministic
    Global Optimization of Head Dependent Short-Term
    Hydro Scheduling}},
    Unique-Id = {ISI:000326184100085},
    Volume = 28,
    Year = 2013,
    Url = {http://dx.doi.org/10.1109/TPWRS.2013.2274559},
    }

  • Lima, R. M., Grossmann, I. E., & Jiao, Y.. (2011). Long-term scheduling of a single-unit multi-product continuous process to manufacture high performance glass. Computers & Chemical Engineering, 35(3), 554-574.
    [BibTeX] [Download PDF]
    @article{lima2011a,
    Author = {Lima, Ricardo M. and Grossmann, Ignacio E. and Jiao,
    Yu},
    Date-Modified ={2016-07-27 10:59:32 +0000},
    OPTDOI = {10.1016/j.compchemeng.2010.06.011},
    Issn = {0098-1354},
    Journal = {{Computers \& Chemical Engineering}},
    Month = {MAR 8},
    Number = 3,
    Orcid-Numbers ={Lima, Ricardo/0000-0002-5735-6089},
    Pages = {554-574},
    Researcherid-Numbers ={Lima, Ricardo/M-5164-2013},
    Times-Cited = 22,
    Title = {{Long-term scheduling of a single-unit multi-product
    continuous process to manufacture high performance
    glass}},
    Unique-Id = {ISI:000288411900017},
    Volume = 35,
    Year = 2011,
    Url = {http://dx.doi.org/10.1016/j.compchemeng.2010.06.011},
    }

  • Lima, R. M., & Grossmann, I. E.. (2009). Optimal Synthesis of p-Xylene Separation Processes Based on Crystallization Technology. AIChE Journal, 55(2), 354-373.
    [BibTeX] [Download PDF]
    @article{lima2009,
    Author = {Lima, Ricardo M. and Grossmann, Ignacio E.},
    OPTDOI = {10.1002/aic.11666},
    Issn = {0001-1541},
    Journal = {{AIChE Journal}},
    Month = {FEB},
    Number = 2,
    Orcid-Numbers ={Lima, Ricardo/0000-0002-5735-6089},
    Pages = {354-373},
    Researcherid-Numbers ={Lima, Ricardo/M-5164-2013},
    Times-Cited = 13,
    Title = {{Optimal Synthesis of p-Xylene Separation Processes
    Based on Crystallization Technology}},
    Unique-Id = {ISI:000262793600007},
    Volume = 55,
    Year = 2009,
    Url = {http://dx.doi.org/10.1002/aic.11666},
    }

  • Lima, R. M., Salcedo, R. L., & Barbosa, D.. (2006). SIMOP efficient reactive distillation optimization using stochastic optimizers. Chemical Engineering Science, 61(5), 1718-1739.
    [BibTeX] [Download PDF]
    @article{lima2006,
    Author = {Lima, R. M. and Salcedo, R. L. and Barbosa, D.},
    OPTDOI = {10.1016/j.ces.2005.10.009},
    Issn = {0009-2509},
    Journal = {{Chemical Engineering Science}},
    Month = {MAR},
    Number = 5,
    Orcid-Numbers ={Barbosa, Domingos/0000-0002-6707-9775 Salcedo,
    Romualdo/0000-0003-3546-7404 Lima,
    Ricardo/0000-0002-5735-6089},
    Pages = {1718-1739},
    Researcherid-Numbers ={Lima, Ricardo/M-5164-2013},
    Times-Cited = 17,
    Title = {{SIMOP efficient reactive distillation optimization
    using stochastic optimizers}},
    Unique-Id = {ISI:000235324600034},
    Volume = 61,
    Year = 2006,
    Url = {http://dx.doi.org/10.1016/j.ces.2005.10.009}
    }

  • Lima, R. M., Francois, G., Srinivasan, B., & Salcedo, R. L.. (2004). Dynamic optimization of batch emulsion polymerization using MSIMPSA, a simulated-annealing-based algorithm. Industrial & Engineering Chemistry Research, 43(24), 7796-7806.
    [BibTeX] [Download PDF]
    @article{lima2004,
    Author = {Lima, R. M. and Francois, G. and Srinivasan, B. and
    Salcedo, R. L.},
    OPTDOI = {10.1021/ie0496784},
    Issn = {0888-5885},
    Journal = {{Industrial \& Engineering Chemistry Research}},
    Month = {NOV 24},
    Number = 24,
    Orcid-Numbers ={Salcedo, Romualdo/0000-0003-3546-7404 Lima,
    Ricardo/0000-0002-5735-6089 Francois,
    Gregory/0000-0002-0315-8722},
    Pages = {7796-7806},
    Researcherid-Numbers ={Lima, Ricardo/M-5164-2013 },
    Times-Cited = 10,
    Title = {{Dynamic optimization of batch emulsion
    polymerization using MSIMPSA, a
    simulated-annealing-based algorithm}},
    Unique-Id = {ISI:000225323800016},
    Volume = 43,
    Year = 2004,
    Url = {http://dx.doi.org/10.1021/ie0496784%7D},
    }

  • Ferreira, E. C., Lima, R., & Salcedo, R.. (2004). Spreadsheets in chemical engineering education – A tool in process design and process integration. International Journal of Engineering Education, 20(6), 928-938.
    [BibTeX] [Download PDF]
    @article{ferreira2004,
    Author = {Ferreira, E. C. and Lima, R. and Salcedo, R.},
    Issn = {0949-149X},
    Journal = {{International Journal of Engineering Education}},
    Number = 6,
    Orcid-Numbers ={Ferreira, Eugenio/0000-0002-5400-3333 Salcedo,
    Romualdo/0000-0003-3546-7404 Lima,
    Ricardo/0000-0002-5735-6089},
    Pages = {928-938},
    Researcherid-Numbers ={Ferreira, Eugenio/B-5417-2009 Lima,
    Ricardo/M-5164-2013},
    Times-Cited = 7,
    Title = {{Spreadsheets in chemical engineering education - A
    tool in process design and process integration}},
    Unique-Id = {ISI:000225562800007},
    Volume = 20,
    Year = 2004,
    Url = {http://www.ijee.ie/articles/Vol20-6/IJEE1562.pdf},
    }

  • Salcedo, R. L., Lima, R. P., & Cardoso, M. F.. (2003). Simulated annealing for the global optimization of chemical processes. PINSA-A, Part A, 69, 359-401.
    [BibTeX] [Download PDF]
    @article{salcedo2003,
    Author = {Salcedo, R.L. and Lima, R.P. and Cardoso, M.F.},
    Identifying-Codes ={[0370-0046(200305/06)69:3/4L.359:SAGO;1-9]},
    Issn = {0370-0046},
    Issue = {3-4},
    Journal = {{PINSA-A, Part A}},
    Month = {May-June},
    Pages = {359-401},
    Publication-Type ={J},
    Title = {{Simulated annealing for the global optimization of
    chemical processes}},
    Unique-Id = {INSPEC:8524669},
    Volume = 69,
    Year = 2003,
    Url = {http://www.insa.nic.in/writereaddata/UpLoadedFiles/PINSA/Vol69A_2003_3And4_Art06.pdf},
    }

  • Salcedo, R., & Lima, R.. (1999). On the optimum choice of decision variables for equation-oriented global optimization. Industrial & Engineering Chemistry Research, 38(12), 4742-4758.
    [BibTeX] [Download PDF]
    @article{salcedo1999,
    Author = {Salcedo, RL and Lima, RM},
    OPTDOI = {10.1021/ie9901980},
    Issn = {0888-5885},
    Journal = {{Industrial \& Engineering Chemistry Research}},
    Month = {DEC},
    Number = 12,
    Orcid-Numbers ={Salcedo, Romualdo/0000-0003-3546-7404 Lima,
    Ricardo/0000-0002-5735-6089},
    Pages = {4742-4758},
    Researcherid-Numbers ={Lima, Ricardo/M-5164-2013},
    Times-Cited = 10,
    Title = {{On the optimum choice of decision variables for
    equation-oriented global optimization}},
    Unique-Id = {ISI:000084126900028},
    Volume = 38,
    Year = 1999,
    Url = {http://dx.doi.org/10.1021/ie9901980},
    }

Papers in conference proceedings
  • Marcovecchio, M., Lima, R., Novais, A., & Grossmann, I.. (2012). Deterministic optimization of short-term scheduling for hydroelectric power generation. Paper presented at the European Symposium on Computer Aided Process Engineering – 22.
    [BibTeX] [Download PDF]
    @inproceedings{marcovecchio2012,
    Author = {Marcovecchio, MG and Lima, RM and Novais, AQ and
    Grossmann, IE},
    Booktitle = {{European Symposium on Computer Aided Process
    Engineering - 22}},
    Editor = {Bogle, IDL and Fairweather, M},
    Isbn = {978-0-444-59519-5},
    Note = {{22nd European Symposium on Computer Aided Process
    Engineering (ESCAPE-22), London, UK, June 17-20,
    2012}},
    Pages = {913-918},
    Series = {{Computer-Aided Chemical Engineering}},
    Title = {{Deterministic optimization of short-term scheduling
    for hydroelectric power generation}},
    Volume = 10,
    Year = 2012,
    Url =
    {http://booksite.elsevier.com/9780444594310/downloads/ESC.414%20-%20Deterministic%20optimisation%20of%20short%20term%20scheduling%20for%20hydroelectric%20power%20generation.pdf}
    }

  • Lima, R. M., & Salcedo, R. L.. (2002). An optimized strategy for equation-oriented global optimization. Paper presented at the European Symposium on Computer Aided Process Engineering – 12.
    [BibTeX] [Download PDF]
    @inproceedings{lima2002,
    Author = {Lima, R. M. and Salcedo, R. L.},
    Booktitle = {{European Symposium on Computer Aided Process
    Engineering - 12}},
    Editor = {{Grievink, J and VanSchijndel, J}},
    Isbn = {0-444-51109-1},
    Issn = {1570-7946},
    Note = {{12th European Symposium on Computer Aided Process
    Engineering (ESCAPE-12), The Hague, Netherlands, May
    20-29, 2002}},
    Orcid-Numbers ={Salcedo, Romualdo/0000-0003-3546-7404 Lima,
    Ricardo/0000-0002-5735-6089},
    Pages = {913-918},
    Researcherid-Numbers ={Lima, Ricardo/M-5164-2013},
    Series = {{Computer-Aided Chemical Engineering}},
    Times-Cited = 3,
    Title = {An optimized strategy for equation-oriented global
    optimization},
    Unique-Id = {ISI:000180725000147},
    Volume = 10,
    Year = 2002,
    Url = {http://dx.doi.org/10.1016/S1570-7946(02)80180-8}
    }

Chapters in books
  • Perez-Uresti, S. I., Lima, R. M., & Jimenez-Gutierrez, A.. (2021). Sustainable Design for Renewable Processes: Principles and Case Studies. In Martin, M. (Ed.), (pp. 553-571). Elsevier Science.
    [BibTeX]
    @inbook{salvadorbook2021,
    Author = {Perez-Uresti, Salvador I. and Lima, Ricardo M. and Jimenez-Gutierrez, Arturo},
    Chapter = {{Renewable-based process integration}},
    Date-Added = {2022-01-13 15:56:02 +0300},
    Date-Modified = {2022-01-13 16:01:53 +0300},
    Editor = {Martin, M.},
    Pages = {553-571},
    Publisher = {{Elsevier Science}},
    Title = {{Sustainable Design for Renewable Processes: Principles and Case Studies}},
    Year = {2021}}

  • Lima, R., & Grossmann, I.. (2014). Introduction to software for Chemical Engineers. In Martin, M. (Ed.), (pp. 455-479). Crc press.
    [BibTeX] [Download PDF]
    @inbook{lima2014,
    Author = {Lima, RMP and Grossmann, IE},
    Chapter = {Algebraic Modeling and Optimization},
    Editor = {Martin, MM},
    Isbn = 9781466599369,
    Pages = {455-479},
    Publisher = {CRC Press},
    Title = {{Introduction to software for Chemical Engineers}},
    Year = 2014,
    Url =
    {https://www.crcpress.com/Introduction-to-Software-for-Chemical-Engineers/Martin/p/book/9781466599369}
    }

  • Lima, R. M., & Grossmann, I. E.. (2011). Chemical Engineering Greetings to prof. Sauro Pierucci. (pp. 151-160). Aidic.
    [BibTeX] [Download PDF]
    @inbook{lima2011b,
    Author = {Lima, Ricardo M and Grossmann, Ignacio E},
    Chapter = {Computational advances in solving mixed integer
    linear programming problems},
    Date-Modified ={2016-07-27 10:59:39 +0000},
    OPTDOI = {10.3303/MSC1101018},
    Isbn = {978-88-95608-10-5},
    Issn = {2036-5969},
    Note = {{http://www.aidic.it/msc/65year/000.html}},
    Pages = {151-160},
    Publisher = {AIDIC},
    Title = {{Chemical Engineering Greetings to prof. Sauro
    Pierucci}},
    Year = 2011,
    Url = {http://dx.doi.org/10.3303/MSC1101018}
    }