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Prof. dr. ir. D. (Dick) den Hertog

Faculty of Economics and Business
Section Operations Management

Visiting address
  • Plantage Muidergracht 12
Postal address
  • Postbus 15953
    1001 NL Amsterdam
Contact details
Social media
  • Dick den Hertog

    Dick den Hertog is professor of Operations Research at University of Amsterdam. His research interests cover various fields in prescriptive analytics, in particular linear and nonlinear optimization. In recent years his main focus has been on robust optimization, and recently he started research on Optimization with Machine Learning. He is also active in applying the theory in real-life applications. In particular, he is interested in applications that contribute to a better society.

    He received the INFORMS Franz Edelman Award twice: in 2013 for his research on optimal flood protection, and in 2021 for his research on optimizing the food supply chain for the UN World Food Programme. Currently, he is doing research to develop better optimization models and techniques for cancer treatment together with researchers from Harvard Medical School and Massachusetts General Hospital (Boston, USA), and he is involved in research to optimize the locations of health facilities in Timor-Leste and Vietnam together with the World Bank. He is associate editor of Operations Research, and INFORMS Journal on Optimization. Since 2019, he is Visiting Professor at MIT (Cambridge, USA). 

    Publications and citations (Google Scholar)

  • Analytics for a Better World

    Our society is facing many challenges. We believe that Analytics can be of much value to improve our world. It is encouraging to see that more and more (especially young) Analytics researchers are enthusiastic to use Analytics to make this world a better place. Analytics has already contributed significantly to, e.g., the Sustainable Development Goals of the United Nations.

    To make these societal contributions of Analytics more visible, and to stimulate research in this area, Dimitris Bertsimas (MIT, Cambridge, USA) and Dick den Hertog have started a new initiative: “Analytics for a Better World (ABW)”.

    Read more about Analytics for a Better World

  • Inspiration from PhD candidates

    I consider supervising PhD candidates as the most important part of my scientific life. A significant part of my inspiration I receive from the PhD candidates who I (co-)supervise. Here is the list of these PhD candidates: 

    Justin Starreveld

    I am a PhD candidate at the University of Amsterdam. My research focuses on the optimization of investment decisions regarding the production and deployment of hydrogen, which is predicted to play an important role in the transition to a sustainable energy system. For such large and complex problems, there is much uncertainty regarding future developments in technology, economics, policymaking, etc. The main goal of this PhD project is to develop a robust optimization methodology for dealing with such uncertainties while optimizing long-term, large-scale energy system models.

    Zihang Qiu

    I am Zihang Qiu, a PhD candidate at the University of Amsterdam. After obtaining my Master's degree in Physics at ETH Zürich, I am working on the EU-funded RAPTOR project to realize online treatment adaption for proton radiotherapy. My research focuses on the daily adaption of proton treatment plan to the patient's latest anatomy to minimize the uncertainty caused by the disagreement between the patient's treatment plan and their anatomy, using mathematical optimization and machine learning. I am carrying out my research project under the supervision of Professor Dick den Hertog (the University of Amsterdam) and Professor Thomas Bortfeld (Massachusetts General Hospital).

    Parvathy Krishnan

    I am Parvathy Krishnan, a Data Science Consultant in the public sector working for organisations such as World Bank and UNDP to employ data science tools and techniques to accelerate the achievement of Sustainable Development Goals. I have a Bachelor of Technology in Electrical and Electronics Engineering, Master of Technology in Energy Management & Climate Change Technology and a Professional Doctorate in Engineering (PDEng.) in Data Science. Under the guidance of Prof Dick den Hertog and Prof. Joaquim Gromicho, I am pursuing a part-time PhD on Analytics for a Better World.

    Donato Maragno

    I am a PhD candidate at the University of Amsterdam, and I work on the ENW-Groot project OPTIMAL (Optimisation for and with Machine Learning). My research focuses on the investigation of different techniques to embed Deep Learning into optimisation models. The goal is to start from data and use predictive models to build part of the optimisation model, making it data-centric and easier to develop. The two main applications of this project are related to the World Food Programme and the Radiotherapy Optimization.

    Merel Wemelsfelder

    During my bachelor in AI and a master in Data Science, I developed a particular interest in the application of Machine Learning to approach optimal solutions to optimization problems. As I am now a PhD student at the University of Amsterdam and Sanquin Blood Supply, I am using both mathematical optimization and Machine Learning in order to improve Sanquin's blood supply chain and the issuing of blood products to patients.

    Meike Reusken

    My name is Meike Reusken and I am a PhD candidate at the Zero Hunger Lab at Tilburg University. Here we use data science for food security. Before joining the Zero Hunger Lab, I obtained a Master’s degree in Economics at Erasmus University and a Master’s degree in Econometrics at Tilburg University. For my research, I am collaborating with the World Food Programme and the Dutch Food Bank. I aspire to improve their processes using (robust) optimisation techniques, with combating hunger as the main focus.  

    Danique de Moor

    I am a Phd candidate at the University of Amsterdam funded by the NWO. Previously, I obtained a Master’s degree in Mathematics at the Radboud University and a Master’s degree in Business at Tilburg University. My research focuses on finding new methods to approximately solve hard minimisation problems with concave parts in the objective and/or constraint functions. Such problems arise often in, e.g., logistics, where costs are concave functions due to economies of scale. An important test case is the optimal food supply-chain problem for the World Food Programme.  

    Wouter van Eekelen

    After obtaining a Master's degree in both Mathematics and Operations Management at Eindhoven University of Technology, I started as a PhD candidate in the OR department of Tilburg University. I am currently working on various topics that explore the merits of applying distributionally robust optimisation as a tool to analyze stochastic systems. My research mainly focuses on the distribution-free analysis of stochastic systems that are driven by some underlying stochastic process. Please take a look at my university profile page.

    Jorn Baayen

    I lead the model integration and real-time optimisation group at KISTERS, a global provider of software solutions for the water and energy business. Some of the water flow optimisation problems that arise in my work at KISTERS have strongly non-linear equality constraints and as such are not convex. Next to my primary function, I am refining the mathematics I have developed to tackle these problems as a PhD candidate at the University of Amsterdam.

    Valentijn Stienen

    I am a PhD candidate at the Zero Hunger Lab at Tilburg University. My research focuses on applying and developing operations research techniques that help humanitarian organizations to optimise their operations. Examples of my work are finding optimal depot locations for humanitarian logistics service providers using robust optimisation, and optimising route decisions in (partly) unknown road networks that may be heavily affected by weather conditions.

    Stefan ten Eikelder

    I am a PhD candidate at Tilburg University, working on various optimisation aspects of radiation therapy treatment planning for cancer patients. In particular, my research focuses on biologically-based treatment planning, where patient specific (biological response) information and its uncertainties are taken into account. We employ techniques from adjustable robust optimisation and conic optimisation, amongst others. On these projects we collaborate with researchers from Massachusetts General Hospital and Harvard Medical School (Boston, USA), whom I have visited several times. Please take a look at my university profile page.

    Ernst Roos

    I am a PhD candidate at Tilburg University funded by the NWO Research Talent grant titled 'A new optimization framework to solve hard decision problems under uncertainty’. My research focuses on the analysis of existing methods to handle uncertainty in continuous optimization as well as designing new methods to treat such problems. Examples of my work are ‘Reducing conservatism in robust optimization’ and ‘Tractable approximation of hard uncertain optimization problems’. In 2019 I was a visiting researcher at Imperial College London for three months and in 2020 I visited Technion in Haifa, Israel for two weeks.

    Koen Peters

    Koen Peters is a project manager in the Supply Chain Planning & Optimization unit of the World Food Programme. After finishing a Master’s degree in Operations Research at Tilburg University, he decided to apply his optimisation knowledge to support humanitarian operations. For the last few years, he has been leading optimisation initiatives at the World Food Programme, developing user-friendly tools to ensure that WFP can reach as many beneficiaries as possible. Under the guidance of professors Hein Fleuren and Dick den Hertog he is pursuing a PhD at Tilburg University’s Zero Hunger Lab.

  • Publications

    2020

    • Roos, E., & den Hertog, D. (2020). Reducing conservatism in robust optimization. INFORMS Journal on Computing, 32(4), 1109-1127. https://doi.org/10.1287/ijoc.2019.0913
    • Roos, K., Balvert, M., Gorissen, B. L., & den Hertog, D. (2020). A universal and structured way to derive dual optimization problem formulations. INFORMA Journal on Optimization, 2(4), 229-346. https://doi.org/10.1287/ijoo.2019.0034
    • Zhen, J., de Ruiter, F. J. C. T., Roos, E., & den Hertog, D. (2020). Robust optimization for models with uncertain SOC and SDP constraints. INFORMS Journal on Computing.
    • ten Eikelder, S. C. M., Ferjancic, P., Bortfeld, T., & den Hertog, D. (2020). Optimal treatment plan adaptation using mid-treatment imaging biomarkers. Physics in Medicine and Biology, 65(24). https://doi.org/10.1088/1361-6560/abc130

    2019

    • Balvert, M., den Hertog, D., & Hoffmann, A. L. (2019). Robust optimization of dose-volume metrics for prostate HDR-brachytherapy incorporating target- and OAR volume delineation uncertainties. INFORMS Journal on Computing, 31(1), 100-114. https://doi.org/10.1287/ijoc.2018.0815
    • ten Eikelder, S. C. M., den Hertog, D., Bortfeld, T., & Perko, Z. (2019). Optimal combined proton-photon therapy schemes based on the standard BED model. Physics in Medicine and Biology, 64(6). https://doi.org/10.1088/1361-6560/aafe52

    2018

    • Marandi, A., & den Hertog, D. (2018). When are static and adjustable robust optimization problems with constraint-wise uncertainty equivalent? Mathematical programming, 170(2), 555-568. https://doi.org/10.1007/s10107-017-1166-z
    • Postek, K., Ben-Tal, A., den Hertog, D., & Melenberg, B. (2018). Robust optimization with ambiguous stochastic constraints under mean and dispersion information. Operations Research, 66(3), 814-833. https://doi.org/10.1287/opre.2017.1688
    • Zhen, J., & den Hertog, D. (2018). Computing the maximum volume inscribed ellipsoid of a polytopic projection. INFORMS Journal on Computing, 30(1), 31-42. https://doi.org/10.1287/ijoc.2017.0763
    • Zhen, J., den Hertog, D., & Sim, M. (2018). Adjustable robust optimization via Fourier-Motzkin elimination. Operations Research, 66(4), 1086-1100. https://doi.org/10.1287/opre.2017.1714

    2017

    • Balvert, M., Breedveld, S., Unkelbach, J., den Hertog, D., & Petit, S. (2017). EP-1696: Dose-painting planning with uncertainties in dose-response parameters and in patient positioning. Radiotherapy and oncology, 123(S1), S927-S928. https://doi.org/10.1016/S0167-8140(17)32228-4
    • Ben-Tal, A., Brekelmans, R., den Hertog, D., & Vial, J. P. (2017). Globalized robust optimization for nonlinear uncertain inequalities. INFORMS Journal on Computing, 29(2), 350-366. https://doi.org/10.1287/ijoc.2016.0735
    • Buhayenko, V., & den Hertog, D. (2017). Adjustable robust optimisation approach to optimise discounts for multi-period supply chain coordination under demand uncertainty. International Journal of Production Research, 55(22), 6801-6823. https://doi.org/10.1080/00207543.2017.1351635
    • Eijgenraam, C., Brekelmans, R., den Hertog, D., & Roos, C. (2017). Optimal strategies for flood prevention. Management Science, 63(5), 1644-1656. https://doi.org/10.1287/mnsc.2015.2395
    • Zhen, J., & den Hertog, D. (2017). Centered solutions for uncertain linear equations. Computational Management Science, 14(4), 585-610. https://doi.org/10.1007/s10287-017-0290-9
    • de Ruiter, F., Ben-Tal, A., Brekelmans, R., & den Hertog, D. (2017). Robust optimization of uncertain multistage inventory systems with inexact data in decision rules. Computational Management Science, 14(1), 45-66. https://doi.org/10.1007/s10287-016-0253-6
    • van Hulst, D., den Hertog, D., & Nuijten, W. (2017). Robust shift generation in workforce planning. Computational Management Science, 14(1), 115-134. https://doi.org/10.1007/s10287-016-0265-2

    2016

    • Postek, K., & den Hertog, D. (2016). Multi-stage adjustable robust mixed-integer optimization via iterative splitting of the uncertainty set. INFORMS Journal on Computing, 28(3), 553-574. https://doi.org/10.1287/ijoc.2016.0696
    • Postek, K., den Hertog, D., & Melenberg, B. (2016). Computationally tractable counterparts of distributionally robust constraints on risk measures. SIAM review, 58(4), 603-650. https://doi.org/10.1137/151005221
    • Yanikoglu, I., den Hertog, D., & Kleijnen, J. P. C. (2016). Robust dual-response optimization. IIE Transactions, 48(3), 298-312. https://doi.org/10.1080/0740817X.2015.1067737
    • de Ruiter, F., Brekelmans, R., & den Hertog, D. (2016). The impact of the existence of multiple adjustable robust solutions. Mathematical programming, 160(1-2), 531-545. https://doi.org/10.1007/s10107-016-0978-6

    2015

    • Balvert, M., Gorissen, B. L., den Hertog, D., & Hoffmann, A. L. (2015). Dwell time modulation restrictions do not necessarily improve treatment plan quality for prostate HDR brachytherapy. Physics in Medicine and Biology, 60(2), 537-548. https://doi.org/10.1088/0031-9155/60/2/537
    • Balvert, M., van Hoof, S. J., Granton, P. V., Trani, D., den Hertog, D., Hoffmann, A. L., & Verhaegen, F. (2015). A framework for inverse planning of beam-on times for 3D small animal radiotherapy using interactive multi-objective optimization. Physics in Medicine and Biology, 60(14), 5681-5698. https://doi.org/10.1088/0031-9155/60/14/5681
    • Ben-Tal, A., den Hertog, D., & Vial, J. P. (2015). Deriving robust counterparts of nonlinear uncertain inequalities. Mathematical programming, 149(1-2), 265-299. https://doi.org/10.1007/s10107-014-0750-8
    • Gorissen, B., Yanikoglu, I., & den Hertog, D. (2015). A practical guide to robust optimization. Omega, 53, 124-137. https://doi.org/10.1016/j.omega.2014.12.006

    2014

    • Ben-Tal, A., & den Hertog, D. (2014). Hidden conic quadratic representation of some nonconvex quadratic optimization problems. Mathematical programming, 143(1-2), 1-29. https://doi.org/10.1007/s10107-013-0710-8
    • Eijgenraam, C., Kind, J., Bak, C., Brekelmans, R. C. M., den Hertog, D., Duits, M., Roos, C., Vermeer, P. J., & Kuijken, W. (2014). Economically efficient standards to protect the Netherlands against flooding. Interfaces, 44(1), 7-21. https://doi.org/10.1287/inte.2013.0721
    • Gorissen, B. L., Blanc, J. P. C., den Hertog, D., & Ben-Tal, A. (2014). Deriving robust and globalized robust solutions of uncertain linear programs having general convex uncertainty sets. Operations Research, 62(3), 672-679. https://doi.org/10.1287/opre.2014.1265

    2013

    • Ben-Tal, A., den Hertog, D., De Waegenaere, A. M. B., Melenberg, B., & Rennen, G. (2013). Robust solutions of optimization problems affected by uncertain probabilities. Management Science, 59(2), 341-357. https://doi.org/10.1287/mnsc.1120.1641
    • Chahim, M., Brekelmans, R. C. M., den Hertog, D., & Kort, P. M. (2013). An impulse control approach to dike height optimization. Optimization Methods and Software, 28(3), 458-477. https://doi.org/10.1080/10556788.2012.737326
    • Gorissen, B. L., & den Hertog, D. (2013). Robust counterparts of inequalities containing sums of maxima of linear functions. European Journal of Operational Research, 227(1), 30-43. https://doi.org/10.1016/j.ejor.2012.10.007
    • Gorissen, B. L., den Hertog, D., & Hoffmann, A. L. (2013). Mixed integer programming improves comprehensibility and plan quality in inverse optimization of prostate HDR Brachytherapy. Physics in Medicine and Biology, 58(4), 1041-1058.
    • Yanikoglu, I., & den Hertog, D. (2013). Safe approximations of ambiguous chance constraints using historical data. INFORMS Journal on Computing, 25(4), 666-681. https://doi.org/10.1287/ijoc.1120.0529

    2012

    • Balvert, M., Gorissen, B. L., den Hertog, D., & Hoffmann, A. L. (2012). Modulation restrictions do not necessarily improve treatment plan quality for HDR prostate brachytherapy. Radiotherapy and oncology, 103, S142.
    • Brekelmans, R. C. M., den Hertog, D., Roos, C., & Eijgenraam, C. (2012). Safe dike heights at minimal costs: The nonhomogeneous case. Operations Research, 60(6), 1342-1355.
    • Gorissen, B. L., & den Hertog, D. (2012). Approximating the Pareto set of multiobjective linear programs via robust optimization. Operations Research Letters, 40(5), 319-324. https://doi.org/10.1016/j.orl.2012.05.007
    • Gorissen, B. L., den Hertog, D., & Hoffmann, A. L. (2012). Mixed integer programming for dose and dose-volume based optimization in prostate HDR-brachytherapy. Radiotherapy and oncology, 103, S130.
    • Steenbergen, F., den Hertog, D., Gorissen, B. L., & Hoffmann, A. L. (2012). Feasibility of convex optimisation with radiobiological models for prostate HDR-brachytherapy. Radiotherapy and oncology, 103, S30-S31.

    2011

    • Husslage, B. G. M., Rennen, G., van Dam, E. R., & den Hertog, D. (2011). Space-filling Latin hypercube designs for computer experiments. Optimization and Engineering, 12(4), 611-630. https://doi.org/10.1007/s11081-010-9129-8
    • Rennen, G., van Dam, E. R., & den Hertog, D. (2011). Enhancement of sandwich algorithms for approximating higher dimensional convex Pareto sets. INFORMS Journal on Computing, 23(4), 493-517. https://doi.org/10.1287/ijoc.1100.0419

    2010

    • Meuffels, W. J. M., & den Hertog, D. (2010). Solving the Battleship puzzle as an integer programming problem. INFORMS Transactions on Education, 10(3), 156-162. https://doi.org/10.1287/ited.1100.0047
    • Rennen, G., Husslage, B. G. M., van Dam, E. R., & den Hertog, D. (2010). Nested maximin Latin hypercube designs. Structural and Multidisciplinary Optimization, 41, 371-395. https://doi.org/10.1007/s00158-009-0432-y
    • Vladislavleva, E., Smits, G., & den Hertog, D. (2010). On the importance of data balancing for symbolic regression. IEEE Transactions on Evolutionary Computation, 14(2), 252-277.
    • van Dam, E. R., Husslage, B. G. M., & den Hertog, D. (2010). One-dimensional nested maximin designs. Journal of Global Optimization, 46(2), 287-306. https://doi.org/10.1007/s10898-009-9426-y

    2009

    • Angun, M. E., Kleijnen, J. P. C., den Hertog, D., & Gürkan, G. (2009). Response surface methodology with stochastic constraints for expensive simulation. Journal of the Operational Research Society, 60(6), 735-746. https://doi.org/10.1057/palgrave.jors.2602614
    • Vladislavleva, E., Smits, G. F., & den Hertog, D. (2009). Order of nonlinearity as a complexity measure for models generated by symbolic regression via Pareto genetic programming. IEEE Transactions on Evolutionary Computation, 13(2), 333-349.

    2008

    • Brekelmans, R. C. M., Driessen, L., Hamers, H. J. M., & den Hertog, D. (2008). Gradient estimation using Lagrange interpolation polynomials. Journal of Optimization Theory and Applications, 136(3), 341-357. https://doi.org/10.1007/s10957-007-9315-9
    • Hoffmann, A. L., Siem, A. Y. D., den Hertog, D., Kaanders, J. H. A. M., & Huizenga, H. (2008). Convex reformulation of biologically-based multi-crtiteria intensity-modulated radiation therapy optimization including fractionation effects. Physics in Medicine and Biology, 53, 6345-6362. https://doi.org/10.1088/0031-9155/53/22/006
    • Siem, A. Y. D., de Klerk, E., & den Hertog, D. (2008). Discrete least-norm approximation by nonnegative (trigonometric) polynomials and rational functions. Structural and Multidisciplinary Optimization, 35(4), 327-339. https://doi.org/10.1007/s00158-007-0135-1
    • Siem, A. Y. D., den Hertog, D., & Hoffmann, A. L. (2008). The effect of transformations on the approximation of univariate (convex) functions with applications to Pareto curves. European Journal of Operational Research, 189(2), 347-362. https://doi.org/10.1016/j.ejor.2007.06.010Get rights and content
    • Stinstra, E., & den Hertog, D. (2008). Robust optimization using computer experiments. European Journal of Operational Research, 191(3), 816-837. https://doi.org/10.1016/j.ejor.2007.03.048
    • de Klerk, E., den Hertog, D., & Elfadul, G. E. E. (2008). On the complexity of optimization over the standard simplex. European Journal of Operational Research, 191, 773-785. https://doi.org/10.1016/j.ejor.2007.01.055

    2007

    • den Boef, E., & den Hertog, D. (2007). Efficient line searching for convex functions. SIAM Journal on Optimization, 18(1), 338-363. https://doi.org/10.1137/​04061115X
    • van Dam, E. R., den Hertog, D., Husslage, B. G. M., & Melissen, H. (2007). Maximin Latin hypercube designs in two dimensions. Operations Research, 55(1), 158-169. https://doi.org/10.1287/opre.1060.0317
    • van Zante-de Fokkert, J. I., den Hertog, D., van den Berg, F. J., & Verhoeven, J. H. M. (2007). The Netherlands schedules track maintenance to improve track workers' safety. Interfaces, 37(2), 133-142. https://doi.org/10.1287/inte.1060.0246

    2006

    • Driessen, L., Brekelmans, R. C. M., Hamers, H. J. M., & den Hertog, D. (2006). On D-optimality based trust regions for black-box optimization problems. Structural and Multidisciplinary Optimization, 31(1), 40-48. https://doi.org/10.1007/s00158-005-0541-1
    • Hoffmann, A. L., Siem, A. Y. D., den Hertog, D., Kaanders, J. H. A. M., & Huizenga, H. (2006). Derivative-free generation and interpolation of convex Pareto optimal IMRT plans. Physics in Medicine and Biology, 51(24), 6349-6369. https://doi.org/10.1088/0031-9155/51/24/005
    • Kleijnen, J. P. C., den Hertog, D., & Angun, M. E. (2006). Response surface methodology's steepest ascent and step size revisited: Correction. European Journal of Operational Research, 170(2), 664-666. https://doi.org/10.1016/j.ejor.2004.10.024
    • Siem, A. Y. D., den Hertog, D., Hoffmann, A. L., Gavrilova, M. (Ed.), Gervasi, O. (Ed.), Kumar, V., Tan, C. J. K. (Ed.), Taniar, A. (Ed.), Mun, Y. (Ed.), & Choo, H. (2006). Multivariate convex approximation and least-norm convex data-smoothing. In Computational Science and Its Applications – ICCSA 2006 Springer Verlag. https://doi.org/10.1007/11751595_86
    • den Hertog, D., & Hulshof, P. B. (2006). Solving Rummikub problems by integer linear programming. Computer Journal, 49(6), 665-669. https://doi.org/10.1093/comjnl/bxl033
    • den Hertog, D., Kleijnen, J. P. C., & Siem, A. Y. D. (2006). The correct Kriging variance estimated by bootstrapping. Journal of the Operational Research Society, 57(4), 400-409. https://doi.org/10.1057/palgrave.jors.2601997

    2005

    • Brekelmans, R. C. M., Driessen, L., Hamers, H. J. M., & den Hertog, D. (2005). Constrained optimization involving expensive function evaluations: A sequential approach. European Journal of Operational Research, 160(1), 121-138. https://doi.org/10.1016/j.ejor.2003.10.009
    • Brekelmans, R. C. M., Driessen, L., Hamers, H. J. M., & den Hertog, D. (2005). Gradient estimation schemes for noisy functions. Journal of Optimization Theory and Applications, 126(3), 529-551. https://doi.org/10.1007/s10957-005-5496-2
    • Fleuren, H. A., den Hertog, D. (Ed.), & Kort, P. M. (2005). Operations Research Proceedings 2004. Springer Verlag. https://doi.org/10.1007/3-540-27679-3
    • den Hertog, D., van Zante-de Fokkert, J. I., Sjamaar, S. A., & Beusmans, R. (2005). Optimal working zone division for safe track maintenance in the Netherlands. Accident Analysis and Prevention, 37(5), 890-893. https://doi.org/10.1016/j.aap.2005.04.006

    2004

    • Kleijnen, J. P. C., den Hertog, D., & Angun, M. E. (2004). Response surface methodology's steepest ascent and step size revisited. European Journal of Operational Research, 159(1), 121-131. https://doi.org/10.1016/S0377-2217(03)00414-4

    2003

    • Husslage, B. G. M., van Dam, E. R., den Hertog, D., Stehouwer, H. P., & Stinstra, E. (2003). Collaborative metamodelling: Coordinating simulation-based product design. Concurrent Engineering: Research and Applications, 11(4), 267-278. https://doi.org/10.1177/1063293X03039895
    • Stinstra, E., den Hertog, D., Stehouwer, H. P., & Vestjens, A. (2003). Constrained maximum designs for computer experiments. Technometrics, 45(4), 340-346.
    • den Boef, E., & den Hertog, D. (2003). Scheduling variable-bit-rate data to minimize total resource costs. In Book of Abstracts of 6th Workshop on Models and Algorithms for Planning and Scheduling Problems (pp. 103-104). INFORMS Institute for Operations Research and the Management Sciences.

    2002

    • Angun, M. E., Gürkan, G., den Hertog, D., Kleijnen, J. P. C., Yucesan, E. (Ed.), Chen, C. H. (Ed.), Snowdon, J. L. (Ed.), & Charnes, J. M. (Ed.) (2002). Response surface methodology revisited. In Proceedings of the Winter Simulation Conference (pp. 377-383). WSC. https://doi.org/10.1109/WSC.2002.1172907
    • den Hertog, D. (2002). Hart en rede in de besliskunde. Proefschrift Katholieke Universiteit Brabant.
    • den Hertog, D., & Stehouwer, H. P. (2002). Optimizing color picture tubes by high-cost non-linear programming. European Journal of Operational Research, 140(2), 197-211. https://doi.org/10.1016/S0377-2217(02)00063-2
    • den Hertog, D., de Klerk, E., & Roos, C. (2002). On convex quadratic approximation. Statistica Neerlandica, 56(3), 376-385. https://doi.org/10.1111/1467-9574.t01-1-00075

    2001

    • Brekelmans, R. C. M., Driessen, L., Hamers, H. J. M., den Hertog, D., & Querin, O. M. (Ed.) (2001). A sequential approach to product and process design involving expensive function evaluation. In Proceedings of the 3rd ASMO/ISSMO Conference on Engineering Design Optimization University Press Leeds.

    2000

    1999

    • Lasance, C. J. M., den Hertog, D., & Stehouwer, H. P. (1999). Creation and evaluation of compact models for thermal characterisation using dedicated optimisation software. In Fifteenth Annual IEEE Semiconductor Thermal Measurement and Management Symposium IEEE. https://doi.org/10.1109/STHERM.1999.762447
    • Stehouwer, H. P., & den Hertog, D. (1999). Simulation-based design optimisation: Methodology and applications (Extended abstract). In Proceedings of the first ASMO UK / ISSMO conference on engineering design optimization Association for Structural and Multidisciplinary Optimization in the UK (ASMO UK).

    1995

    • den Hertog, D., Jarre, F., Roos, C., & Terlaky, T. (1995). A sufficient condition for self-concordance with application to some classes of structured convex programming problems. Mathematical programming, 69, 75-88. https://doi.org/10.1007/BF01585553
    • den Hertog, D., Kaliski, J. A., Roos, C., & Terlaky, T. (1995). A logarithmic barrier cutting plane method for convex programming. Annals of Operations Research, 58, 67-98. https://doi.org/10.1007/BF02032162

    1994

    • den Hertog, D. (1994). Interior point approach to linear, quadratic and convex programming: Algorithms and complexity. (Mathematics and its applications). Kluwer Academic Publishers. https://doi.org/10.1007/978-94-011-1134-8

    1993

    • Anstreicher, K. M., den Hertog, D., Roos, C., & Terlaky, T. (1993). A long-step barrier method for convex quadratic programming. Algorithmica, 10, 365-382. https://doi.org/10.1007/BF01769704
    • Güler, O., den Hertog, D., Roos, C., Terlaky, T., & Tsuchiya, T. (1993). Degeneracy in interior point methods for linear programming: A survey. Annals of Operations Research, 46(1), 107-138. https://doi.org/10.1007/BF02096259
    • den Hertog, D., Roos, C., & Terlaky, T. (1993). The linear complementarity problem, sufficient matrices, and the criss-cross method. Linear Algebra and Its Applications, 187, 1-14. https://doi.org/10.1016/0024-3795(93)90124-7

    1992

    • den Hertog, D., Roos, C., & Terlaky, T. (1992). A build-up variant of the path-following method for LP. Operations Research Letters, 12(3), 181-186. https://doi.org/10.1016/0167-6377(92)90104-B
    • den Hertog, D., Roos, C., & Terlaky, T. (1992). A large-step analytic center method for a class of smooth convex programming problems. SIAM Journal on Optimization, 2(1), 55-70. https://doi.org/10.1137/0802005
    • den Hertog, D., Roos, C., & Terlaky, T. (1992). On the classical logarithmic barrier method for a class of smooth convex programming problems. Journal of Optimization Theory and Applications, 73(1), 1-25. https://doi.org/10.1007/BF00940075
    • den Hertog, D., Roos, C., & Vial, J-P. (1992). A complexity reduction for the long-step path-following algorithm for linear programming. SIAM Journal on Optimization, 2(1), 71-87. https://doi.org/10.1137/0802006

    1991

    • den Hertog, D., & Roos, C. (1991). A survey of search directions in interior point methods for linear programming. Mathematical programming, 52, 481-509. https://doi.org/10.1007/BF01582902
    • den Hertog, D., Roos, C., & Terlaky, T. (1991). A polynomial method of weighted centers for convex quadratic programming. Journal of Information and Optimization Sciences, 12(2), 187-205. https://doi.org/10.1080/02522667.1991.10699062
    • den Hertog, D., Roos, C., & Terlaky, T. (1991). A potential-reduction variant of renegar's short-step path-following method for linear programming. Linear Algebra and Its Applications, 152(1), 43-68. https://doi.org/10.1016/0024-3795(91)90266-Y

    2017

    2015

    • Balvert, M., van Hoof, S. J., Granton, P. V., Trani, D., den Hertog, D., Hoffmann, A. L., & Verhaegen, F. (2015). Inverse planning of beam-on times for precision image-guided 3D small animal radiotherapy treatments. Radiotherapy and oncology, 115(Supplement 1), S392. [PO-0785]. https://doi.org/10.1016/S0167-8140(15)40777-7

    2014

    • Brekelmans, R., Eijgenraam, C., den Hertog, D., & Roos, C. (2014). A mixed integer nonlinear optimization approach to optimize dike heights in the Netherlands. Optima: Mathematical Optimization Society Newsletter, 94.

    1997

    • den Hertog, D. (1997). Efficient optimization technics implemented in DOT-software of CQM. Philips Natlab Journaal, 9, 6-7.
    • den Hertog, D., & Jansen, B. (1997). OR-consultants at CQM. In O. J. Vrieze, L. C. M. Kallenberg, & W. K Haneveld (Eds.), Make optimization work! (pp. 317-322). (CWI Tract). Centrum voor Wiskunde en Informatica.

    1996

    • Fleuren, H. A., & den Hertog, D. (1996). Uw ideale fysieke distributiestructuur bepalen? Hoe kwantitatieve analyse u kan helpen. In E. W. Ploos van Amstel (Ed.), Handboek Logistiek Samson.

    1995

    • Benders, J. F., den Hertog, D., & Lukkassen, R. W. J. (1995). Configuration of telephone exchanges. In OR at wORk, practical experiences of operational research (pp. 39-52). Taylor & Francis.
    • den Hertog, D. (1995). Interview with D. den Hertog. Optima, 5-6.

    1992

    • den Hertog, D., Roos, C., & Terlaky, T. (1992). Adding and deleting constraints in the path-following method for lp. (SHELL Report AMER). Koninklijke/ Shell-Laboratorium.

    1991

    • den Hertog, D., Roos, C., & Terlaky, T. (1991). Inverse barrier methods for linear programming. (Reports of the Faculty of Technical Mathematics and Informatics). Delft University of Technology, faculty of mathematics and informatics.
    • den Hertog, D., Roos, C., & Terlaky, T. (1991). Iri's polynomiality proof for the iri-imai method recast. (Faculty of Mathematics and Informatics/Computer Science). Delft University of Technology.

    1989

    • Roos, C., & den Hertog, D. (1989). A polynomial method of approximate weighted centers for linear programming. (Reports of the faculty of technical mathematics and informatics). Eindhover University of Technology.

    2010

    • Brekelmans, R. C. M., & den Hertog, D. (2010). Optimaliseren? Doe het robuust! STAtOR, dec 2010, 24-27.

    2008

    • Hoffmann, A. L., & den Hertog, D. (2008). Optimalisatie van bestralingsbehandeling tegen kanker; Operations Research toegepast voor radiotherapie. STAtOR, dec 2008, 4-8.

    2004

    • den Hertog, D. (2004). Statistiek en besliskunde: Een gedwongen huwelijk? STAtOR, 1.

    1999

    • den Hertog, D., Stehouwer, H. P., Bouwman, V., Ter Weeme, J. W., & Zhang, K. (1999). Integrale productoptimalisatie: haal meer uit uw simulatiemodellen. De Constructeur, 38(2), 19-21.

    1995

    • Fleuren, H. A., den Hertog, D., & Tuyt, H. L. (1995). Optimalisatie van het produkt- en procesontwerp: Efficiënte zoektechniek geïmplementeerd in DOT-software. CA Techniek: Tijdschrift voor Industriële Automatisering, 14(10), 24-28.

    1992

    • den Hertog, D., Roos, C., & Terlaky, T. (1992). On the monotonicity of the dual objective along barrier paths. COAL Bulletin, 20, 2-7.

    1992

    • den Hertog, D. (1992). Interior point approach to linear, quadratic and convex programming. s.n.

    2016

    • Zhen, J., & den Hertog, D. (2016). Centered Solutions for Uncertain Linear Equations (revision of CentER DP 2015-044). (CentER Discussion Paper). CentER, Center for Economic Research.

    2014

    • de Ruiter, F. J. C. T., Ben-Tal, A., Brekelmans, R. C. M., & den Hertog, D. (2014). Adjustable Robust Optimizations with Decision Rules Based on Inexact Revealed Data. (CentER Discussion Paper). Tilburg University - Operations research.

    2013

    • Yanikoglu, I., den Hertog, D., & Kleijnen, J. P. C. (2013). Adjustable Robust Parameter Design with Unknown Distributions. (CentER Discussion Paper). Department of Quantitative Econometrics.

    2012

    • Chahim, M., Brekelmans, R. C. M., den Hertog, D., & Kort, P. M. (2012). An Impulse Control Approach to Dike Height Optimization (Revised version of CentER DP 2011-097). (CentER Discussion Paper; No. 2012-079). Tilburg University - Operations research.

    2011

    • Ben-Tal, A., den Hertog, D., & Laurent, M. (2011). Hidden Convexity in Partially Separable Optimization. (CentER Discussion Paper). Tilburg University - Operations research.

    2008

    • Blanc, J. P. C., & den Hertog, D. (2008). On Markov Chains with Uncertain Data. (CentER Discussion Paper). Tilburg University - Operations research.

    2007

    • Siem, A. Y. D., & den Hertog, D. (2007). Kriging Models That Are Robust With Respect to Simulation Errors. (CentER Discussion Paper). Tilburg University - Operations research.
    • Siem, A. Y. D., den Hertog, D., & Hoffmann, A. L. (2007). A Method For Approximating Univariate Convex Functions Using Only Function Value Evaluations. (CentER Discussion Paper). Tilburg University - Operations research.

    2006

    • Driessen, L., Brekelmans, R. C. M., Gerichhausen, M., Hamers, H. J. M., & den Hertog, D. (2006). Why Methods for Optimization Problems with Time-Consuming Function Evaluations and Integer Variables Should Use Global Approximation Models. (CentER Discussion Paper). Tilburg University - Operations research.
    • de Klerk, E., Elfadul, G. E. E., & den Hertog, D. (2006). Optimization of Univariate Functions on Bounded Intervals by Interpolation and Semidefinite Programming. (CentER Discussion Paper). Tilburg University - Operations research.

    2005

    • Husslage, B. G. M., van Dam, E. R., & den Hertog, D. (2005). Nested Maximin Latin Hypercube Designs in Two Dimensions. (CentER Discussion Paper). Tilburg University - Operations research.
    • Siem, A. Y. D., de Klerk, E., & den Hertog, D. (2005). Discrete Least-norm Approximation by Nonnegative (Trigonomtric) Polynomials and Rational Functions. (CentER Discussion Paper). Tilburg University - Operations research.
    • Stinstra, E., & den Hertog, D. (2005). Robust Optimization Using Computer Experiments. (CentER Discussion Paper). Tilburg University - Operations research.

    2003

    • Brekelmans, R. C. M., Driessen, L., Hamers, H. J. M., & den Hertog, D. (2003). Gradient Estimation Schemes for Noisy Functions. (CentER Discussion Paper). Tilburg University - Operations research.
    • Husslage, B. G. M., van Dam, E. R., den Hertog, D., Stehouwer, H. P., & Stinstra, E. (2003). Coordination of Coupled Black Box Simulations in the Construction of Metamodels. (CentER Discussion Paper). Tilburg University - Operations research.
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  • Ancillary activities
    • Massachusetts Institute of Technology
      Visiting Professorship