Metamodel-based dynamic algorithm configuration using artificial neural networks

References

• Alsrehin, Nawaf O., Ahmad F. Klaib, and Aws Magableh. 2019. “Intelligent Transportation and Control Systems Using Data Mining and Machine Learning Techniques: A Comprehensive Study.” IEEE Access 7: 49830–49857. https://doi.org/10.1109/Access.6287639.
   Web of Science ®Google Scholar
• Amaran, Satyajith, Nikolaos V. Sahinidis, Bikram Sharda, and Scott J. Bury. 2016. “Simulation Optimization: A Review of Algorithms and Applications.” Annals of Operations Research 240 (1): 351–380. https://doi.org/10.1007/s10479-015-2019-x.
   Web of Science ®Google Scholar
• Andrade, Manuel A., Christopher Y. Choi, Kevin Lansey, and Donghwi Jung. 2016. “Enhanced Artificial Neural Networks Estimating Water Quality Constraints for the Optimal Water Distribution Systems Design.” Journal of Water Resources Planning and Management 142 (9): Article 04016024. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000663.
   Web of Science ®Google Scholar
• Bailey, William A., and Thomas D. Clark. 1987. “A Simulation Analysis of Demand and Fleet Size Effects on Taxicab Service Rates.” In Proceedings of the 19th Winter Simulation Conference, edited by A. Thesen, H. Grant, and W. D. Kelton, 838–844. New York, NY: Association for Computing Machinery.
   Google Scholar
• Baker, Kenneth R. 1977. “An Experimental Study of the Effectiveness of Rolling Schedules in Production Planning.” Decision Sciences 8 (1): 19–27. https://doi.org/10.1111/deci.1977.8.issue-1.
   Google Scholar
• Barton, Russell R., and Martin Meckesheimer. 2006a. “Metamodel-Based Simulation Optimization.” In Simulation, edited by Shane G. Henderson and Barry L. Nelson, Vol. 13 of Handbooks in Operations Research and Management Science, 535–574. London, UK: Elsevier.
   Google Scholar
• Barton, Russell R., and Martin Meckesheimer. 2006b. “Metamodel-Based Simulation Optimization.” Handbooks in Operations Research and Management Science 13 (C): 535–574. https://doi.org/10.1016/S0927-0507(06)13018-2.
   Google Scholar
• Battiti, Roberto, Mauro Brunato, and Andrea Mariello. 2019. “Reactive Search Optimization: Learning While Optimizing.” In Handbook of Metaheuristics, 479–511. Boston, MA: Springer.
   Google Scholar
• Battiti, Roberto, Mauro Brunato, and Franco Mascia. 2008. Reactive Search and Intelligent Optimization. Boston, MA: Springer.
   Google Scholar
• Bes, Christian, and Suresh Sethi. 1988. “Concepts of Forecast and Decision Horizons: Applications to Dynamic Stochastic Optimization Problems.” Mathematics of Operations Research 13 (2): 295–310. https://doi.org/10.1287/moor.13.2.295.
   Web of Science ®Google Scholar
• Biedenkapp, André, H. Furkan Bozkurt, Theresa Eimer, Frank Hutter, and Marius Lindauer. 2020. “Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework.” In Proceedings of the 24th European Conference on Artificial Intelligence, 1–8. Amsterdam, Netherlands: IOS Press.
   Google Scholar
• Bookbinder, James H., and Jin-Yan Tan. 1988. “Strategies for the Probabilistic Lot-Sizing Problem with Service-Level Constraints.” Management Science 34 (9): 1096–1108. https://doi.org/10.1287/mnsc.34.9.1096.
   Web of Science ®Google Scholar
• Borodin, Alan, and Ran El-Yaniv. 2005. Online Computation and Competitive Analysis. 2nd ed., Cambridge, UK: Cambridge University Press.
   Google Scholar
• Brunton, Steven L., and J. Nathan Kutz. 2022. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge, UK: Cambridge University Press.
   Google Scholar
• Chen, Huey-Kuo, Che-Fu Hsueh, and Mei-Shiang Chang. 2006. “The Real-Time Time-Dependent Vehicle Routing Problem.” Transportation Research Part E: Logistics and Transportation 42 (5): 383–408. https://doi.org/10.1016/j.tre.2005.01.003.
   Web of Science ®Google Scholar
• Chen, Yiyang, and Yingwei Zhou. 2020. “Machine Learning Based Decision Making for Time Varying Systems: Parameter Estimation and Performance Optimization.” Knowledge-Based Systems 190:Article 105479. https://doi.org/10.1016/j.knosys.2020.105479.
   Web of Science ®Google Scholar
• Chung, Chen-Hua, and Lee J. Krajewski. 1987. “Interfacing Aggregate Plans and Master Production Schedules Via a Rolling Horizon Feedback Procedure.” Omega 15 (5): 401–409. https://doi.org/10.1016/0305-0483(87)90041-7.
   Web of Science ®Google Scholar
• Ciocan, Dragos Florin, and Vivek Farias. 2012. “Model Predictive Control for Dynamic Resource Allocation.” Mathematics of Operations Research 37 (3): 501–525. https://doi.org/10.1287/moor.1120.0548.
   Web of Science ®Google Scholar
• Domański, Paweł D. 2020. “Performance Assessment of Predictive Control: A Survey.” Algorithms 13 (4): 97. https://doi.org/10.3390/a13040097.
   Web of Science ®Google Scholar
• Dunke, Fabian, and Stefan Nickel. 2020. “Neural Networks for the Metamodeling of Simulation Models with Online Decision Making.” Simulation Modelling Practice and Theory 99:Article 102016. https://doi.org/10.1016/j.simpat.2019.102016.
   Web of Science ®Google Scholar
• Eggensperger, Katharina, Marius Lindauer, and Frank Hutter. 2019. “Pitfalls and Best Practices in Algorithm Configuration.” Journal of Artificial Intelligence Research 64:861–893. https://doi.org/10.1613/jair.1.11420.
   Web of Science ®Google Scholar
• Ehmke, Jan Fabian, and Ann Melissa Campbell. 2014. “Customer Acceptance Mechanisms for Home Deliveries in Metropolitan Areas.” European Journal of Operational Research 233 (1): 193–207. https://doi.org/10.1016/j.ejor.2013.08.028.
   Web of Science ®Google Scholar
• Erichsen, Gerrit, Tobias Zimmermann, and Alfons Kather. 2019. “Effect of Different Interval Lengths in a Rolling Horizon MILP Unit Commitment with Non-linear Control Model for a Small Energy System.” Energies 12 (6): 1003. https://doi.org/10.3390/en12061003.
   Web of Science ®Google Scholar
• Fiat, Amos, and Gerhard Woeginger (Eds.). 1998. Online Algorithms: The State of the Art. Lecture Notes in Computer Science 1442. Springer.
   Google Scholar
• Fienen, Michael N., Bernard T. Nolan, and Daniel T. Feinstein. 2016. “Evaluating the Sources of Water to Wells: Three Techniques for Metamodeling of a Groundwater Flow Model.” Environmental Modelling and Software 77:95–107. https://doi.org/10.1016/j.envsoft.2015.11.023.
   Web of Science ®Google Scholar
• Fleischmann, Bernhard, Herbert Meyr, and Michael Wagner. 2015. “Advanced Planning.” In Supply Chain Management and Advanced Planning, edited by Hartmut Stadtler, Christoph Kilger, and Herbert Meyr, 5th ed., 71–95. Berlin, Germany: Springer.
   Google Scholar
• Fonseca, Daniel J., and Daniel Navaresse. 2002. “Artificial Neural Networks for Job Shop Simulation.” Advanced Engineering Informatics 16 (4): 241–246. https://doi.org/10.1016/S1474-0346(03)00005-3.
   Web of Science ®Google Scholar
• Fonseca, Daniel J., Daniel O. Navaresse, and Gary P. Moynihan. 2003. “Simulation Metamodeling Through Artificial Neural Networks.” Engineering Applications of Artificial Intelligence 16 (3): 177–183. https://doi.org/10.1016/S0952-1976(03)00043-5.
   Web of Science ®Google Scholar
• Fonseca, Daniel J., Daniel O. Navaresse, and Gary P. Moynihan. 2003. “Simulation Metamodeling Through Artificial Neural Networks.” Engineering Applications of Artificial Intelligence 16 (3): 177–183. https://doi.org/10.1016/S0952-1976(03)00043-5.
   Web of Science ®Google Scholar
• Garcia, Carlos E., David M. Prett, and Manfred Morari. 1989. “Model Predictive Control: Theory and Practice – a Survey.” Automatica 25 (3): 335–348. https://doi.org/10.1016/0005-1098(89)90002-2.
   Web of Science ®Google Scholar
• Gaudio, Joseph E., Travis E. Gibson, Anuradha M. Annaswamy, Michael A. Bolender, and Eugene Lavretsky. 2019. “Connections Between Adaptive Control and Optimization in Machine Learning.” In 2019 IEEE Conference on Decision and Control, 4563–4568. IEEE.
   Google Scholar
• Ghiasi, Ramin, Mohammad Reza Ghasemi, and Mohammad Noori. 2018. “Comparative Studies of Metamodeling and AI-Based Techniques in Damage Detection of Structures.” Advances in Engineering Software 125:101–112. https://doi.org/10.1016/j.advengsoft.2018.02.006.
   Web of Science ®Google Scholar
• Hamadi, Youssef, Eric Monfroy, and Frédéric Saubion. 2012. “An Introduction to Autonomous Search.” In Autonomous Search, edited by Youssef Hamadi, Eric Monfroy, and Frédéric Saubion, 1–11. Berlin, Germany: Springer.
   Google Scholar
• Hornik, Kurt. 1991. “Approximation Capabilities of Multilayer Feedforward Networks.” Neural Networks 4 (2): 251–257. https://doi.org/10.1016/0893-6080(91)90009-T.
   Web of Science ®Google Scholar
• Hrovat, Davor, Stefano Di Cairano, H. Eric Tseng, and Ilya V. Kolmanovsky. 2012. “The Development of Model Predictive Control in Automotive Industry: A Survey.” In 2012 IEEE International Conference on Control Applications, 295–302. IEEE.
   Google Scholar
• Jarosz, Piotr, Jan Kusiak, Stanisław Małecki, Paweł Morkisz, Piotr Oprocha, Wojciech Pietrucha, and Łukasz Sztangret. 2016. “Metamodeling and Optimization of a Blister Copper Two-Stage Production Process.” JOM 68 (6): 1535–1540. https://doi.org/10.1007/s11837-016-1916-z.
   Web of Science ®Google Scholar
• Kerschke, Pascal, Holger Hoos, Frank Neumann, and Heike Trautmann. 2019. “Automated Algorithm Selection: Survey and Perspectives.” Evolutionary Computation 27 (1): 3–45. https://doi.org/10.1162/evco_a_00242.
   PubMed Web of Science ®Google Scholar
• Khalid, Muhammad S., and Andrey V. Savkin. 2010. “A Model Predictive Control Approach to the Problem of Wind Power Smoothing with Controlled Battery Storage.” Renewable Energy 35 (7): 1520–1526. https://doi.org/10.1016/j.renene.2009.11.030.
   Web of Science ®Google Scholar
• Köhler, Charlotte, Jan Fabian Ehmke, and Ann Melissa Campbell. 2020. “Flexible Time Window Management for Attended Home Deliveries.” Omega 91:Article 102023. https://doi.org/10.1016/j.omega.2019.01.001.
   Web of Science ®Google Scholar
• Kotthoff, Lars. 2016. “Algorithm Selection for Combinatorial Search Problems: A Survey.” In Data Mining and Constraint Programming, edited by C. Bessiere, L. De Raedt, L. Kotthoff, S. Nijssen, B. O'Sullivan, and D. Pedreschi, 149–190. Cham, Switzerland: Springer.
   Google Scholar
• Kroetz, Henrique M., Rodolfo K. Tessari, and André T. Beck. 2017. “Performance of Global Metamodeling Techniques in Solution of Structural Reliability Problems.” Advances in Engineering Software 114:394–404. https://doi.org/10.1016/j.advengsoft.2017.08.001.
   Web of Science ®Google Scholar
• Laguna, Manuel, and Rafael Marti. 2002. “Neural Network Prediction in a System for Optimizing Simulations.” IIE Transactions 34 (3): 273–282.
   Web of Science ®Google Scholar
• Lin, Po-Chen, and Reha Uzsoy. 2016. “Estimating the Costs of Planned Changes Implied by Freezing Production Plans.” In Heuristics, Metaheuristics and Approximate Methods in Planning and Scheduling, edited by G. Rabadi, 17–44. Cham, Switzerland: Springer.
   Google Scholar
• Lu, Hao. 2021. “Machine Learning for Decision Making: Applications to Off-Policy Learning and Combinatorial Optimization.” PhD diss., Princeton University.
   Google Scholar
• Mitrovic-Minic, Snezana, Ramesh Krishnamurti, and Gilbert Laporte. 2004. “Double-Horizon Based Heuristics for the Dynamic Pickup and Delivery Problem with Time Windows.” Transportation Research Part B: Methodological 38 (8): 669–685. https://doi.org/10.1016/j.trb.2003.09.001.
   Web of Science ®Google Scholar
• Mitrović-Minić, Snežana, and Gilbert Laporte. 2004. “Waiting Strategies for the Dynamic Pickup and Delivery Problem with Time Windows.” Transportation Research Part B: Methodological 38 (7): 635–655. https://doi.org/10.1016/j.trb.2003.09.002.
   Web of Science ®Google Scholar
• Mouelhi-Chibani, Wiem, and Henri Pierreval. 2010. “Training a Neural Network to Select Dispatching Rules in Real Time.” Computers and Industrial Engineering 58 (2): 249–256. https://doi.org/10.1016/j.cie.2009.03.008.
   Web of Science ®Google Scholar
• Muñoz, Mario A., Yuan Sun, Michael Kirley, and Saman K. Halgamuge. 2015. “Algorithm Selection for Black-Box Continuous Optimization Problems: A Survey on Methods and Challenges.” Information Sciences 317:224–245. https://doi.org/10.1016/j.ins.2015.05.010.
   Web of Science ®Google Scholar
• Negahban, Ashkan, and Jeffrey S. Smith. 2014. “Simulation for Manufacturing System Design and Operation: Literature Review and Analysis.” Journal of Manufacturing Systems 33 (2): 241–261. https://doi.org/10.1016/j.jmsy.2013.12.007.
   Web of Science ®Google Scholar
• Ninikas, George, and Ioannis Minis. 2014. “Reoptimization Strategies for a Dynamic Vehicle Routing Problem with Mixed Backhauls.” Networks 64 (3): 214–231. https://doi.org/10.1002/net.v64.3.
   Web of Science ®Google Scholar
• Peres, Ricardo Silva, Jose Barata, Paulo Leitao, and Gisela Garcia. 2019. “Multistage Quality Control Using Machine Learning in the Automotive Industry.” IEEE Access 7:79908–79916. https://doi.org/10.1109/Access.6287639.
   Web of Science ®Google Scholar
• Pierreval, Henri. 1992. “Training a Neural Network by Simulation for Dispatching Problems.” In The Third International Conference on Computer Integrated Manufacturing, 332–333. IEEE Computer Society.
   Google Scholar
• Powell, Warren B., Patrick Jaillet, and Amedeo Odoni. 1995. “Stochastic and Dynamic Networks and Routing.” In Network Routing, edited by M. O. Ball, T. L. Magnanti, C. L. Monma, and G. L. Nemhauser, Vol. 8 of Handbooks in Operations Research and Management Science, 141–295. Amsterdam, Netherlands: Elsevier.
   Google Scholar
• Pushak, Yasha, and Holger Hoos. 2018. “Algorithm Configuration Landscapes.” In International Conference on Parallel Problem Solving from Nature, edited by A. Auger, C. M. Fonseca, N. Lourenco, P. Machado, L. Paquete, and D. Whitley, 271–283. Cham, Switzerland: Springer.
   Google Scholar
• Qin, S. Joe, and Thomas A. Badgwell. 2003. “A Survey of Industrial Model Predictive Control Technology.” Control Engineering Practice 11 (7): 733–764. https://doi.org/10.1016/S0967-0661(02)00186-7.
   Web of Science ®Google Scholar
• Rice, John R. 1976. “The Algorithm Selection Problem.” In Advances in Computers, edited by M. Rubinoff and M. C. Yovits, Vol. 15, 65–118. West Lafayette, IN: Elsevier.
   Google Scholar
• Ritzinger, Ulrike, Jakob Puchinger, and Richard F. Hartl. 2016. “A Survey on Dynamic and Stochastic Vehicle Routing Problems.” International Journal of Production Research 54 (1): 215–231. https://doi.org/10.1080/00207543.2015.1043403.
   Web of Science ®Google Scholar
• Raúl, Rojas. 2013. Neural Networks: A Systematic Introduction. Berlin: Springer.
   Google Scholar
• Sabuncuoglu, Ihsan, and Souheyl Touhami. 2002. “Simulation Metamodelling with Neural Networks: An Experimental Investigation.” International Journal of Production Research 40 (11): 2483–2505. https://doi.org/10.1080/00207540210135596.
   Web of Science ®Google Scholar
• Sahin, Funda, Arunachalam Narayanan, and E. Powell Robinson. 2013. “Rolling Horizon Planning in Supply Chains: Review, Implications and Directions for Future Research.” International Journal of Production Research 51 (18): 5413–5436. https://doi.org/10.1080/00207543.2013.775523.
   Web of Science ®Google Scholar
• Schwarz, Hannes, Lars Kotthoff, Holger Hoos, Wolf Fichtner, and Valentin Bertsch. 2019. “Improving the Computational Efficiency of Stochastic Programs Using Automated Algorithm Configuration: An Application to Decentralized Energy Systems.” Annals of Operations Research1–22.
   Google Scholar
• Sethi, Suresh, and Gerhard Sorger. 1991. “A Theory of Rolling Horizon Decision Making.” Annals of Operations Research 29 (1): 387–415. https://doi.org/10.1007/BF02283607.
   Google Scholar
• Silvente, Javier, Georgios M. Kopanos, Efstratios N. Pistikopoulos, and Antonio Espuña. 2015. “A Rolling Horizon Optimization Framework for the Simultaneous Energy Supply and Demand Planning in Microgrids.” Applied Energy 155:485–501. https://doi.org/10.1016/j.apenergy.2015.05.090.
   Web of Science ®Google Scholar
• Stadtler, Hartmut, and Malte Meistering. 2019. “Model Formulations for the Capacitated Lot-Sizing Problem with Service-Level Constraints.” OR Spectrum 41 (4): 1025–1056. https://doi.org/10.1007/s00291-019-00552-1.
   Web of Science ®Google Scholar
• Thomas, Barrett W. 2007. “Waiting Strategies for Anticipating Service Requests from Known Customer Locations.” Transportation Science 41 (3): 319–331. https://doi.org/10.1287/trsc.1060.0183.
   Web of Science ®Google Scholar
• Ulmer, Marlin W. 2019. “Anticipation Versus Reactive Reoptimization for Dynamic Vehicle Routing with Stochastic Requests.” Networks 73 (3): 277–291. https://doi.org/10.1002/net.v73.3.
   Web of Science ®Google Scholar
• Ulmer, Marlin W., Leonard Heilig, and Stefan Voß. 2017. “On the Value and Challenge of Real-Time Information in Dynamic Dispatching of Service Vehicles.” Business and Information Systems Engineering 59 (3): 161–171. https://doi.org/10.1007/s12599-017-0468-2.
   Web of Science ®Google Scholar
• Van Gelder, Liesje, Payel Das, Hans Janssen, and Staf Roels. 2014. “Comparative Study of Metamodelling Techniques in Building Energy Simulation: Guidelines for Practitioners.” Simulation Modelling Practice and Theory 49:245–257. https://doi.org/10.1016/j.simpat.2014.10.004.
   Web of Science ®Google Scholar
• Wang, Ling. 2005. “A Hybrid Genetic Algorithm–Neural Network Strategy for Simulation Optimization.” Applied Mathematics and Computation 170 (2): 1329–1343. https://doi.org/10.1016/j.amc.2005.01.024.
   Web of Science ®Google Scholar
• Xu, Jie, Edward Huang, Chun-Hung Chen, and Loo Hay Lee. 2015. “Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data.” Asia-Pacific Journal of Operational Research 32 (3): Article 1550019. https://doi.org/10.1142/S0217595915500190.
   Web of Science ®Google Scholar