A Nested Design and Analysis of Computer Experiments Approach to Enable Design for Controllability of a System of Electric Vehicle Charging Stations

Published Online:https://doi.org/10.1287/ijds.2024.0053

References

  • Anjos MF, Brotcorne L, Guillot G (2025) Optimal electric vehicle charging with dynamic pricing, customer preferences and power peak reduction. Inform. Systems Oper. Res. 63(3):1–20.Google Scholar
  • Ariyajunya B, Chen Y, Chen VCP, Kim SB (2017) Data mining for state space orthogonalization in adaptive dynamic programming. Expert Systems Appl. 76:49–58.Google Scholar
  • Ariyajunya B, Chen Y, Chen VCP, Kim SB, Rosenberger J (2021) Addressing state space multicollinearity in solving an ozone pollution dynamic control problem. Eur. J. Oper. Res. 289(2):683–695.Google Scholar
  • Asadi A, Nurre Pinkley S, Mes M (2022) A Markov decision process approach for managing medical drone deliveries. Expert Systems Appl. 204:117490.Google Scholar
  • Aute V, Saleh K, Abdelaziz O, Azarm S, Radermacher R (2008) Cross-validation based single response adaptive design of experiments for deterministic computer simulations. 12th AIAA/ISSMO Multidisciplinary Analysis Analysis Optim. Conf. (ACM, New York), 6067.Google Scholar
  • Bahrami M, Martin J-P, Maranzana G, Pierfederici S, Weber M, Meibody-Tabar F, Zandi M (2019) Design and modeling of an equalizer for fuel cell energy management systems. IEEE Trans. Power Electronics 34(11):10925–10935.Google Scholar
  • Bai J, Wei W, Guo Z, Chen L, Mei S (2023) Two-timescale coordinated operation of wind-advanced adiabatic compressed air energy storage system: A bilevel stochastic dynamic programming method. J. Energy Storage 67:107502.Google Scholar
  • Basciftci B, Ahmed S, Gebraeel N (2024) Adaptive two-stage stochastic programming with an analysis on capacity expansion planning problem. Manufacturing Service Oper. Management 26(6):2121–2141.LinkGoogle Scholar
  • Bellman RE (1957) Dynamic Programming (Princeton University Press, Princeton, NJ).Google Scholar
  • Bennouna A, Pachamanova D, Perakis G, Skali Lami O (2025) Learning the minimal representation of a continuous state-space Markov decision process from transition data. Management Sci. 71(6):5162–5184.LinkGoogle Scholar
  • Bertsekas DP (2025) Neuro-dynamic programming. Encyclopedia of Optimization (Springer, Cham), 1–6.Google Scholar
  • Birhanie HM, Messous MA, Senouci SM, Aglzim EH, Ahmed AM (2018) MDP-based resource allocation scheme towards a vehicular fog computing with energy constraints. 2018 IEEE Global Comm. Conf. (GLOBECOM) (IEEE, Piscataway, NJ), 1–6.Google Scholar
  • Birge J, Louveaux F (2011) Introduction to Stochastic Programming, 2nd ed. (Springer, Berlin).Google Scholar
  • Boaro M, Fuselli D, De Angelis F, Liu D, Wei Q, Piazza F (2013) Adaptive dynamic programming algorithm for renewable energy scheduling and battery management. Cognitive Comput. 5(2):264–277.Google Scholar
  • Box GEP, Hunter WG, Hunter JS (1978) Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building (Wiley, New York).Google Scholar
  • Bralla JG (1996) Design for Excellence (McGraw-Hill, New York).Google Scholar
  • Cen H, Xu Y, Sun K, Tian H, Chen K, Lin L (2024) Markov decision process framework of optimal energy dispatch in a smart data center with uninterruptible power supplies. J. Comput. Methods Sci. Engrg. 24(3):1317–1329.Google Scholar
  • Cervellera C, Macciò D (2017) A novel approach for sampling in approximate dynamic programming based on $F $-Discrepancy. IEEE Trans. Cybernetics 47(10):3355–3366.Google Scholar
  • Cervellera C, Muselli M (2007) Efficient sampling in approximate dynamic programming algorithms. Comput. Optim. Appl. 38(3):417–443.Google Scholar
  • Cervellera C, Wen A, Chen VC (2007) Neural network and regression spline value function approximations for stochastic dynamic programming. Comput. Oper. Res. 34(1):70–90.Google Scholar
  • Chawal U, Rosenberger J, Chen VCP, Lee W-J, Wijemanne M, Punugu RK, Kulvanitchaiyanunt A (2024) A design and analysis of computer experiments based mixed integer linear programming approach for optimizing a system of electric vehicle charging stations. Expert Systems Appl. 245:123064.Google Scholar
  • Chen VCP (2001) Measuring the goodness of orthogonal array discretizations for stochastic programming and stochastic dynamic programming. SIAM J. Optim. 12(2):322–344.Google Scholar
  • Chen Y, Castillo K, Dong B (2021) Stochastic control of a micro-grid using battery energy storage in solar-powered buildings. Ann. Oper. Res. 303:197–216.Google Scholar
  • Chen VCP, Ruppert D, Shoemaker CA (1999) Applying experimental design and regression splines to high-dimensional continuous-state stochastic dynamic programming. Oper. Res. 47(1):38–53.LinkGoogle Scholar
  • Chen H-J, Wang DWP, Chen S-L (2005) Optimization of an ice-storage air conditioning system using dynamic programming method. Appl. Thermogenic Engrg. 25:461–472.Google Scholar
  • Chen VC, Tsui KL, Barton RR, Meckesheimer M (2006) A review on design, modeling and applications of computer experiments. IIE Trans. 38(4):273–291.Google Scholar
  • Chen Y, Liu F, Rosenberger JM, Chen VCP, Kulvanitchaiyanunt A, Zhou Y (2020) Efficient approximate dynamic programming based on design and analysis of computer experiments for infinite-horizon optimization. Comput. Oper. Res. 124:105032.Google Scholar
  • Coffman M, Bernstein P, Wee S (2017) Electric vehicles revisited: A review of factors that affect adoption. Transportation Rev. 37(1):79–93.Google Scholar
  • CPLEX, IBM ILOG (2015) International Business Machines Corporation v12.6.3 (Version 12.6.3). Accessed May 12, 2018, http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/.Google Scholar
  • Electric Reliability Council of Texas (2002) ERCOT history. Accessed August 2, 2016, http://www.ercot.com/about/profile/history/.Google Scholar
  • Fan H-Y, Tarun PK, Chen VCP (2013) Adaptive value function approximation for continuous-state stochastic dynamic programming. Comput. Oper. Res. 40:1076–1084.Google Scholar
  • Fan H, Tarun PK, Viswanatha A, Chen VC (2025) A fully adaptive framework for continuous-state stochastic dynamic programming. Comput. Oper. Res. 183:107160.Google Scholar
  • Fang KT, Li R, Sudjianto A (2005) Design and Modeling for Computer Experiments (Chapman and Hall/CRC, Boca Raton, FL).Google Scholar
  • Franke T, Krems JF (2013) What drives range preferences in electric vehicle users? Transportation Policy (Oxford) 30:56–62.Google Scholar
  • Frazier PI (2018) A tutorial on Bayesian optimization. Preprint, submitted July 8, https://arxiv.org/abs/1807.02811.Google Scholar
  • Friedman JH (1991) Multivariate adaptive regression splines. Ann. Statist. 19(1):1–67.Google Scholar
  • Gerbaud V, Rodriguez-Donis I, Hegely L, Lang P, Denes F, You X (2019) Review of extractive distillation process design, operation, optimization and control. Chemical Engrg. Res. Design 141:229–271.Google Scholar
  • GUROBI Optimization, LLC (2022) GUROBI Optimization Reference Manual v9.5 (Version 9.5). Accessed January 16, 2022, https://www.gurobi.com.Google Scholar
  • Halton JH (1960) On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numerical Math. (Heidelberg) 2:84–90.Google Scholar
  • Hastie T, Friedman JH, Tibshirani R (2001) Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer-Verlag, New York). Google Scholar
  • Heath DC, Jackson PL (1994) Modeling the evolution of demand forecasts ITH application to safety stock analysis in production/distribution systems. IIE Trans. 26(3):17–30.Google Scholar
  • Hedayat AS, Sloane NJA, Stufken J (1999) Orthogonal Arrays (Springer-Verlag, New York).Google Scholar
  • Huangfu Y, Tian C, Zhuo S, Xu L, Li P, Quan S, Ma R (2023) An optimal energy management strategy with subsection bi-objective optimization dynamic programming for photovoltaic/battery/hydrogen hybrid energy system. Internat. J. Hydrogen Energy 48(8):3154–3170.Google Scholar
  • Jiang DR, Powell WB (2015) Optimal hour-ahead bidding in the real-time electricity market with battery storage using approximate dynamic programming. INFORMS J. Comput. 27(3):525–543.LinkGoogle Scholar
  • Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4):455–492.Google Scholar
  • Khosrojerdi A, Allen J, Lee WJ, Mistree F (2012) Designing an electric charging station for plug-in-hybrid-electric-vehicles in the face of uncertain demand. 12th AIAA Aviation Tech. Integration Oper. Conf. 14th AIAA/ISSMO Multidisciplinary Analysis and Optim. Conf. (AIAA, Reston, VA), 5432.Google Scholar
  • Khosrojerdi A, Xiao M, Sariprueck P, Allen J, Mistree F (2013) Designing a system of plug-in hybrid electric vehicle charging stations. Internat. Design Engrg. Technical Conf. Comput. Inform. Engrg. Conf., vol. 55881 (ASME, New York), 9.Google Scholar
  • Kleijnen JP (2005) An overview of the design and analysis of simulation experiments for sensitivity analysis. Eur. J. Oper. Res. 164(2):287–300.Google Scholar
  • Kong Y, Xu N, Liu Q, Sui Y, Yue F (2023) A data-driven energy management method for parallel PHEVs based on action dependent heuristic dynamic programming (ADHDP) model. Energy 265:126306.Google Scholar
  • Kulvanitchaiyanunt A, Chen VC, Rosenberger J, Sarikprueck P, Lee WJ (2016) A linear program for system-level control of regional PHEV charging stations. IEEE Trans. Industrial Appl. 52(3):2046–2052.Google Scholar
  • Li W, Zou Y, Yang H, Fu X, Xiang S, Li Z (2025) Two-stage stochastic energy scheduling for multi-energy rural microgrids with irrigation systems and biomass fermentation. IEEE Trans. Smart Grid 16(2):1075–1087.Google Scholar
  • Lin CF, LeBoulluec AK, Zeng L, Chen VCP, Gatchel RJ (2014) A decision-making framework for adaptive pain management. Health Care Management Sci. 17(3):270–283.Google Scholar
  • MATLAB (2016) The Math Works, Inc. 2016 (Version 2016a). Accessed October 30, 2017, https://www.mathworks.com/.Google Scholar
  • Mhaisalkar VA, Bassin JK, Parmasivam R, Khanna P (1993) Dynamic programming optimization of water-treatment-plant design. J. Environment Engrg. 119(6):1158–1175.Google Scholar
  • Miller A, Lumby B (2012) Utility scale solar power plants: A guide for developers and investors. Guidelines book written for IFC. World Bank Group, New Delhi, India.Google Scholar
  • Minitab, LLC (2016) Salford Predictive Modeler v8.0 (Version 8.0). Accessed May 25, 2018, https://www.salford-systems.com/products/.Google Scholar
  • Munos R, Szepesvári C (2008) Finite-time bounds for fitted value iteration. J. Machine Learn. Res. 9(27):815 − 857.Google Scholar
  • National Renewable Energy Laboratory (2012) National Solar Radiation Data Base. Accessed July 26, 2016, http://rredc.nrel.gov/solar/old_data/nsrdb/.Google Scholar
  • Pan J-S, Song P-C, Chu S-C, Snášel V, Watada J (2025) Machine learning-enabled evolutionary two-stage stochastic programming. IEEE Trans. Emerging Top Comput. Intelligence. (IEEE, Piscataway, NJ), 1–12.Google Scholar
  • Pilla VL, Rosenberger JM, Chen VCP, Smith BC (2008) A statistical computer experiments approach to airline fleet assignment. IIE Trans. 40(5):524–537.Google Scholar
  • Pilla VL, Rosenberger JM, Chen VCP, Engsuwan N, Siddappa S (2012) A multivariate adaptive regression splines cutting plane approach for solving a two-stage stochastic programming fleet assignment model. Eur. J. Oper. Res. 216:162–171.Google Scholar
  • Posit Team (2025) RStudio: Integrated development environment for R (Version 2025.09.2 Build 418). Accessed February 3, 2026, https://posit.co/.Google Scholar
  • Powell WB (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality, vol. 703 (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Purnami P, Nugroho WS, Anggayasti WL, Sofi’i YK, Wardana ING (2025) Markov decision process for current density optimization to improve hydrogen production by water electrolysis. Electrochemical Comm. 177:107987.Google Scholar
  • R Core Team (2024) R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria).Google Scholar
  • Ren Y, Tang J, Yu Y, Li X (2024) A two-stage stochastic programming model and parallel Master–Slave adaptive GA for flexible Seru system formation. Internat. J. Production Res. 62(4):1144–1161.Google Scholar
  • Ryu JS, Kim MS, Cha KJ, Lee TH, Choi DH (2002) Kriging interpolation methods in geostatistics and DACE model. KSME Internat. J. 16:619–632.Google Scholar
  • Sacks J, Welch WJ, Mitchell TJ, Wynn HP (1989) Design and analysis of computer experiments. Statist. Sci. 4(4):409–423.Google Scholar
  • Sakhavand N, Rosenberger JM, Chen VCP, Gangammanavar H (2024) Design of experiments for the stochastic unit commitment with economic dispatch models. EURO J. Comput. Optim. 12:100089.Google Scholar
  • Salas DF, Powell WB (2018) Benchmarking a scalable approximate dynamic programming algorithm for stochastic control of grid-level energy storage. INFORMS J. Comput. 30(1):106–123.LinkGoogle Scholar
  • Santner TJ, Williams BJ, Notz WI, Williams BJ (2003) The Design and Analysis of Computer Experiments, vol. 1 (Springer, New York).Google Scholar
  • Sarikprueck P, Lee W-J, Kulvanitchaiyanunt A, Chen V, Rosenberger J (2018) Bounds for optimal control of a reginal plug-in electric vehicle charging station system. IEEE Trans. Industry Appl. 54(2):977–986.Google Scholar
  • Sharifzadeh M (2013) Integration of process design and control: A review. Chemical Engrg. Res. Design 91(12):2515–2549.Google Scholar
  • Shi G, Wei Q, Liu D (2016) An adaptive dynamic programming-based method for optimization of electricity consumption in office buildings. 2016 Internat. Joint Conf. Neural Networks (IJCNN) (IEEE, Piscataway, NJ), 4551–4556.Google Scholar
  • Shih DT, Kim SB, Chen VCP, Rosenberger JM, Pilla VL (2014) Efficient computer experiment-based optimization through variable selection. Ann. Oper. Res. 216:287–305.Google Scholar
  • Si J, Barto AG, Powell WB, Wunsch D, eds. (2004) Handbook of Learning and Approximate Dynamic Programming, vol. 2 (John Wiley & Sons. Hoboken, NJ).Google Scholar
  • Sobol IM (1967) The distribution of points in a cube and the approximate evaluation of integrals. USSR Comput. Math. Math. Phys. 7:784–802.Google Scholar
  • Sorrentino M, Cirillo V, Nappi L (2019) Development of flexible procedures for co-optimizing design and control of fuel cell hybrid vehicles. Energy Conversion Management 185:537–551.Google Scholar
  • Swartz CL, Kawajiri Y (2019) Design for dynamic operation-A review and new perspectives for an increasingly dynamic plant operating environment. Comput. Chemical Engrg. 128:329–339.Google Scholar
  • Tang W, Wang Y, Jiao X, Ren L (2023) Hierarchical energy management strategy based on adaptive dynamic programming for hybrid electric vehicles in car-following scenarios. Energy 265:126264.Google Scholar
  • Thorhauge M, Rich J, Mabit SE (2024) Charging behaviour and range anxiety in long-distance EV travel: An adaptive choice design study. Transportation (Amsterdam) 1–23.Google Scholar
  • Tiwari A, Liang Y, Bengio Y (2025) Geometry of neural reinforcement learning in continuous state and action spaces. Preprint, submitted July 28, https://doi.org/10.48550/arXiv.2507.20853.Google Scholar
  • Tsai JC, Chen VCP, Beck MB, Chen J (2004) Stochastic dynamic programming formulation for a wastewater treatment decision-making framework. Ann. Oper. Res. 132(1):207–221.Google Scholar
  • U.S. Department of Energy (2012) Electric power monthly March 2012 (with data for January 2012). Accessed August 12, 2016, http://www.eia.gov/cneaf/electricity/epm/epm_sum.html.Google Scholar
  • Wang C, Lei S, Ju P, Chen C, Peng C, Hou Y (2020) MDP-based distribution network reconfiguration with renewable distributed generation: Approximate dynamic programming approach. IEEE Trans. Smart Grid 11(4):3620–3631.Google Scholar
  • Wei Q, Liu D, Lewis FL, Liu Y, Zhang J (2017) Mixed iterative adaptive dynamic programming for optimal battery energy control in smart residential microgrids. IEEE Trans. Industrial Electronics 64(5):4110–4120.Google Scholar
  • Werbos P (1974) Beyond regression: New tools for prediction and analysis in the behavioral sciences. PhD thesis, Harvard University, Cambridge, MA.Google Scholar
  • Wetz D (2010) Energy storage needs and options. Technical report, The University of Texas at Arlington, Arlington, TX.Google Scholar
  • Xia Y, Yin M, Li R, Liu D, Zou Y (2019) Integrated structure and maximum power point tracking control design for wind turbines based on degree of controllability. J. Vibration Control 25(2):397–407.Google Scholar
  • Xie S, Zhong W, Xie K, Yu R, Zhang Y (2016) Fair energy scheduling for vehicle-to-grid networks using adaptive dynamic programming. IEEE Trans. Neural Networks Learn. Systems 27(8):1697–1707.Google Scholar
  • Xu Y, Wu Q (2024) Markov decision process modeling in pharmacoeconomics with application perspectives. Appl. Math. Nonlinear Sci. 9(1):88–92.Google Scholar
  • Yang Z, Chen VCP, Chang ME, Murphy TE, Tsai JC (2007) Mining and modeling for a metropolitan Atlanta ozone pollution decision-making framework. IIE Trans. 39(6):607–615.Google Scholar
  • Yang Z, Chen VCP, Chang ME, Sattler ML, Wen A (2009) A decision-making framework for ozone pollution control. Oper. Res. 57(2):484–498.LinkGoogle Scholar
  • Yang A, Shen W, Wei S, Dong L, Li J, Gerbaud V (2019) Design and control of pressure-swing distillation for separating ternary systems with three binary minimum azeotropes. AIChe J. 65(4):1281–1293.Google Scholar
  • Yu R, Zhong W, Xie S, Zhang Y, Zhang Y (2016) QoS differential scheduling in cognitive-radio-based smart grid networks: An adaptive dynamic programming approach. IEEE Trans. Neural Networks Learn. Systems 27(2):435–443.Google Scholar
  • Zhang H, Wu Q, Chen J, Lu L, Zhang J, Zhang S (2023) Multiple stage stochastic planning of integrated electricity and gas system based on distributed approximate dynamic programming. Energy 270:126892.Google Scholar
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