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Uzay Kaymak

Personal Details

First Name:Uzay
Middle Name:
Last Name:Kaymak
Suffix:
RePEc Short-ID:pka115
https://www.tue.nl/en/research/researchers/uzay-kaymak/

Affiliation

Technische Universiteit Eindhoven (Eindhoven University of Technology, School of Industrial Engineering)

https://www.tue.nl/en/our-university/departments/industrial-engineering-innovation-sciences/
The Netherlands, Eindhoven

Research output

as
Jump to: Working papers Articles

Working papers

  1. Almeida e Santos Nogueira, R.J. & Basturk, N. & Kaymak, U. & Costa Sousa, J.M., 2013. "Estimation of flexible fuzzy GARCH models for conditional density estimation," ERIM Report Series Research in Management ERS-2013-013-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  2. van den Berg, J.H. & Kaymak, U. & Almeida e Santos Nogueira, R.J., 2011. "Function Approximation Using Probabilistic Fuzzy Systems," ERIM Report Series Research in Management ERS-2011-026-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  3. Waltman, L. & van Eck, N.J.P. & Dekker, R. & Kaymak, U., 2011. "An evolutionary model of price competition among spatially distributed firms," Econometric Institute Research Papers EI 2011-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  4. Kaymak, U. & Ben-David, A. & Potharst, R., 2010. "AUK: a simple alternative to the AUC," ERIM Report Series Research in Management ERS-2010-024-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  5. Milea, V. & Frasincar, F. & Kaymak, U., 2009. "A Temporal Web Ontology Language," ERIM Report Series Research in Management ERS-2009-050-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  6. Waltman, L. & van Eck, N.J.P. & Dekker, R. & Kaymak, U., 2009. "Economic Modeling Using Evolutionary Algorithms: The Effect of a Binary Encoding of Strategies," ERIM Report Series Research in Management ERS-2009-028-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  7. Budai-Balke, G. & Dekker, R. & Kaymak, U., 2009. "Genetic and memetic algorithms for scheduling railway maintenance activities," Econometric Institute Research Papers EI 2009-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  8. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  9. Larco Martinelli, J.A. & Dekker, R. & Kaymak, U., 2007. "Distributed Services with Foreseen and Unforeseen Tasks: The Mobile Re-allocation Problem," ERIM Report Series Research in Management ERS-2007-087-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  10. Waltman, L. & Kaymak, U., 2006. "A Theoretical Analysis of Cooperative Behavior in Multi-Agent Q-learning," ERIM Report Series Research in Management ERS-2006-006-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  11. Boer-Sorban, K. & Kaymak, U. & Spiering, J., 2006. "From Discrete-Time Models to Continuous-Time, Asynchronous Models of Financial Markets," ERIM Report Series Research in Management ERS-2006-009-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  12. Surico, M. & Kaymak, U. & Naso, D. & Dekker, R., 2006. "Hybrid Meta-Heuristics for Robust Scheduling," ERIM Report Series Research in Management ERS-2006-018-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  13. Groenen, P.J.F. & Kaymak, U. & van Rosmalen, J.M., 2006. "Fuzzy clustering with Minkowski distance," Econometric Institute Research Papers EI 2006-24, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  14. Boer-Sorban, K. & Kaymak, U. & de Bruin, A., 2005. "A Modular Agent-Based Environment for Studying Stock Markets," ERIM Report Series Research in Management ERS-2005-017-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  15. Boer-Sorban, K. & de Bruin, A. & Kaymak, U., 2005. "On the Design of Artificial Stock Markets," ERIM Report Series Research in Management ERS-2005-001-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  16. Naso, D. & Surico, M. & Turchiano, B. & Kaymak, U., 2004. "Genetic Algorithms in Supply Chain Scheduling of Ready-Mixed Concrete," ERIM Report Series Research in Management ERS-2004-096-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  17. Kaymak, U., 2003. "A Lotting Method for Electronic Reverse Auctions," ERIM Report Series Research in Management ERS-2003-042-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  18. van den Berg, J.H. & van den Bergh, W.-M. & Kaymak, U., 2003. "Financial Markets Analysis by Probabilistic Fuzzy Modelling," ERIM Report Series Research in Management ERS-2003-036-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  19. Cornelissen, A.M.G. & van den Berg, J.H. & Koops, W.J. & Kaymak, U., 2002. "Eliciting Expert Knowledge for Fuzzy Evaluation of Agricultural Production Systems," ERIM Report Series Research in Management ERS-2002-108-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  20. Pijls, W.H.L.M. & Potharst, R. & Kaymak, U., 2001. "Pattern-Based Target Selection Applied to Fund Raising," ERIM Report Series Research in Management ERS-2001-56-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  21. Potharst, R. & Kaymak, U. & Pijls, W.H.L.M., 2001. "Neural Networks for Target Selection in Direct Marketing," ERIM Report Series Research in Management ERS-2001-14-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  22. Kaymak, U. & Sousa, J.M., 2001. "Weighted Constraints in Fuzzy Optimization," ERIM Report Series Research in Management ERS-2001-19-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  23. van den Berg, J.H. & van den Bergh, W.-M. & Kaymak, U., 2001. "Probabilistic and Statistical Fuzzy Set Foundations of Competitive Exception Learning," ERIM Report Series Research in Management ERS-2001-40-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  24. Kaymak, U. & Setnes, M., 2000. "Extended Fuzzy Clustering Algorithms," ERIM Report Series Research in Management ERS-2000-51-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  25. Setnes, M. & Kaymak, U., 2000. "Fuzzy Modeling of Client Preference in Data-Rich Marketing Environments," ERIM Report Series Research in Management ERS-2000-49-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

Articles

  1. da Costa, Paulo Roberto de Oliveira & Akçay, Alp & Zhang, Yingqian & Kaymak, Uzay, 2020. "Remaining useful lifetime prediction via deep domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
  2. Ludo Waltman & Nees Eck & Rommert Dekker & Uzay Kaymak, 2013. "An Evolutionary Model of Price Competition Among Spatially Distributed Firms," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 373-391, December.
  3. Ludo Waltman & Nees Eck & Rommert Dekker & Uzay Kaymak, 2011. "Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 737-756, December.
  4. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.
  5. Naso, David & Surico, Michele & Turchiano, Biagio & Kaymak, Uzay, 2007. "Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete," European Journal of Operational Research, Elsevier, vol. 177(3), pages 2069-2099, March.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Almeida e Santos Nogueira, R.J. & Basturk, N. & Kaymak, U. & Costa Sousa, J.M., 2013. "Estimation of flexible fuzzy GARCH models for conditional density estimation," ERIM Report Series Research in Management ERS-2013-013-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Roy Cerqueti & Massimiliano Giacalone & Raffaele Mattera, 2020. "Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling," Papers 2004.11674, arXiv.org.

  2. Waltman, L. & van Eck, N.J.P. & Dekker, R. & Kaymak, U., 2009. "Economic Modeling Using Evolutionary Algorithms: The Effect of a Binary Encoding of Strategies," ERIM Report Series Research in Management ERS-2009-028-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Haijun Yang & Harry Wang & Gui Sun & Li Wang, 2015. "A comparison of U.S and Chinese financial market microstructure: heterogeneous agent-based multi-asset artificial stock markets approach," Journal of Evolutionary Economics, Springer, vol. 25(5), pages 901-924, November.
    2. Chernomaz, K. & Goertz, J.M.M., 2023. "(A)symmetric equilibria and adaptive learning dynamics in small-committee voting," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).
    3. Michael K. Maschek, 2015. "Particle Swarm Optimization in Agent‐Based Economic Simulations of the Cournot Market Model," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 22(2), pages 133-152, April.
    4. Salle, Isabelle & Seppecher, Pascal, 2016. "Social Learning About Consumption," Macroeconomic Dynamics, Cambridge University Press, vol. 20(7), pages 1795-1825, October.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," IREA Working Papers 201801, University of Barcelona, Research Institute of Applied Economics, revised Jan 2018.
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    7. Zeng, Weijun & Ai, Hongfeng & Zhao, Man, 2019. "Asymmetrical expectations of future interaction and cooperation in the iterated prisoner's dilemma game," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 148-164.

  3. Budai-Balke, G. & Dekker, R. & Kaymak, U., 2009. "Genetic and memetic algorithms for scheduling railway maintenance activities," Econometric Institute Research Papers EI 2009-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Zhang, Chuntian & Gao, Yuan & Yang, Lixing & Gao, Ziyou & Qi, Jianguo, 2020. "Joint optimization of train scheduling and maintenance planning in a railway network: A heuristic algorithm using Lagrangian relaxation," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 64-92.
    2. Urbani, Michele & Brunelli, Matteo & Punkka, Antti, 2023. "An approach for bi-objective maintenance scheduling on a networked system with limited resources," European Journal of Operational Research, Elsevier, vol. 305(1), pages 101-113.
    3. Baldi, Mauro M. & Heinicke, Franziska & Simroth, Axel & Tadei, Roberto, 2016. "New heuristics for the Stochastic Tactical Railway Maintenance Problem," Omega, Elsevier, vol. 63(C), pages 94-102.

  4. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Ionela Ancuța IANCU, 2015. "Investing Strategies Of Romanian Retail Investors Before And During Crisis (2006-2009)," SEA - Practical Application of Science, Romanian Foundation for Business Intelligence, Editorial Department, issue 9, pages 23-28, December.
    2. Arpan Jani, 2021. "An agent-based model of repeated decision making under risk: modeling the role of alternate reference points and risk behavior on long-run outcomes," Journal of Business Economics, Springer, vol. 91(9), pages 1271-1297, November.
    3. Amadin Victor Idehen, 2021. "Capital investment decisions of small and medium enterprises in Benin-City, Nigeria," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 10(3), pages 101-108, April.
    4. Egidijus Bikas & Vitalija Saponaitė, 2018. "Behavior of the Lithuanian investors at the period of economic growth," Post-Print hal-02121012, HAL.
    5. Egidijus Bikas & Vitalija Saponaitė, 2018. "Behavior of the Lithuanian investors at the period of economic growth," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 6(1), pages 44-59, September.
    6. Yun Wang & Renhai Hua & Zongcheng Zhang, 2011. "The investor behavior and futures market volatility," China Finance Review International, Emerald Group Publishing Limited, vol. 1(4), pages 388-407, September.
    7. Victor Dragotă & Camelia Delcea, 2019. "How Long Does It Last to Systematically Make Bad Decisions? An Agent-Based Application for Dividend Policy," JRFM, MDPI, vol. 12(4), pages 1-34, November.
    8. Pascual-Ezama, David & Paredes, Mercedes Rodríguez & Sanchez-Martín, María-del-Pilar & de Liaño, Beatriz Gil-Gómez, 2018. "Shorter and easier is more useful: A longitudinal analysis of how financial report enforcement affects individual investors," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 74(C), pages 29-37.
    9. Syed Aliya Zahera & Rohit Bansal, 2018. "Do investors exhibit behavioral biases in investment decision making? A systematic review," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 10(2), pages 210-251, May.
    10. Lect. Aurora Murgea Ph. D, 2010. "Classical Lassical And Behavioural Finance In Investor Decision," Annals of University of Craiova - Economic Sciences Series, University of Craiova, Faculty of Economics and Business Administration, vol. 2(38), pages 1-12, May.
    11. Victor DRAGOTĂ, 2016. "When Making Bad Decisions Becomes Habit: Modelling The Duration Of Making Systematically Bad Decisions," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(1), pages 123-140.

  5. Waltman, L. & Kaymak, U., 2006. "A Theoretical Analysis of Cooperative Behavior in Multi-Agent Q-learning," ERIM Report Series Research in Management ERS-2006-006-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.

  6. Boer-Sorban, K. & Kaymak, U. & Spiering, J., 2006. "From Discrete-Time Models to Continuous-Time, Asynchronous Models of Financial Markets," ERIM Report Series Research in Management ERS-2006-009-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Kazuto Sasai & Yukio-Pegio Gunji & Tetsuo Kinoshita, 2017. "Intermittent Behavior Induced By Asynchronous Interactions In A Continuous Double Auction Model," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 20(02n03), pages 1-21, March.

  7. Surico, M. & Kaymak, U. & Naso, D. & Dekker, R., 2006. "Hybrid Meta-Heuristics for Robust Scheduling," ERIM Report Series Research in Management ERS-2006-018-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Al-Hinai, Nasr & ElMekkawy, T.Y., 2011. "Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm," International Journal of Production Economics, Elsevier, vol. 132(2), pages 279-291, August.

  8. Groenen, P.J.F. & Kaymak, U. & van Rosmalen, J.M., 2006. "Fuzzy clustering with Minkowski distance," Econometric Institute Research Papers EI 2006-24, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Abder-Rahman Ali & Jingpeng Li & Sally Jane O’Shea, 2020. "Towards the automatic detection of skin lesion shape asymmetry, color variegation and diameter in dermoscopic images," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-21, June.

  9. Boer-Sorban, K. & Kaymak, U. & de Bruin, A., 2005. "A Modular Agent-Based Environment for Studying Stock Markets," ERIM Report Series Research in Management ERS-2005-017-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Boer-Sorban, K. & Kaymak, U. & Spiering, J., 2006. "From Discrete-Time Models to Continuous-Time, Asynchronous Models of Financial Markets," ERIM Report Series Research in Management ERS-2006-009-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

  10. Boer-Sorban, K. & de Bruin, A. & Kaymak, U., 2005. "On the Design of Artificial Stock Markets," ERIM Report Series Research in Management ERS-2005-001-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Lect. Aurora Murgea Ph. D, 2010. "Classical Lassical And Behavioural Finance In Investor Decision," Annals of University of Craiova - Economic Sciences Series, University of Craiova, Faculty of Economics and Business Administration, vol. 2(38), pages 1-12, May.
    3. Boer-Sorban, K. & Kaymak, U. & Spiering, J., 2006. "From Discrete-Time Models to Continuous-Time, Asynchronous Models of Financial Markets," ERIM Report Series Research in Management ERS-2006-009-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

  11. Naso, D. & Surico, M. & Turchiano, B. & Kaymak, U., 2004. "Genetic Algorithms in Supply Chain Scheduling of Ready-Mixed Concrete," ERIM Report Series Research in Management ERS-2004-096-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Asbach, Lasse & Dorndorf, Ulrich & Pesch, Erwin, 2009. "Analysis, modeling and solution of the concrete delivery problem," European Journal of Operational Research, Elsevier, vol. 193(3), pages 820-835, March.
    2. Chi, Hoi-Ming & Ersoy, Okan K. & Moskowitz, Herbert & Ward, Jim, 2007. "Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms," European Journal of Operational Research, Elsevier, vol. 180(1), pages 174-193, July.
    3. Surico, M. & Kaymak, U. & Naso, D. & Dekker, R., 2006. "Hybrid Meta-Heuristics for Robust Scheduling," ERIM Report Series Research in Management ERS-2006-018-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

  12. van den Berg, J.H. & van den Bergh, W.-M. & Kaymak, U., 2003. "Financial Markets Analysis by Probabilistic Fuzzy Modelling," ERIM Report Series Research in Management ERS-2003-036-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. R. J. Almeida & U. Kaymak, 2009. "Probabilistic fuzzy systems in value‐at‐risk estimation," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(1‐2), pages 49-70, January.
    2. Iris Lucas & Michel Cotsaftis & Cyrille Bertelle, 2017. "Heterogeneity and Self-Organization of Complex Systems Through an Application to Financial Market with Multiagent Systems," Post-Print hal-02114933, HAL.
    3. von Corswant, F. & Wynstra, J.Y.F. & Wetzels, M., 2003. "In Chains? Automotive Suppliers and Their Product Development Activities," ERIM Report Series Research in Management ERS-2003-027-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

  13. Cornelissen, A.M.G. & van den Berg, J.H. & Koops, W.J. & Kaymak, U., 2002. "Eliciting Expert Knowledge for Fuzzy Evaluation of Agricultural Production Systems," ERIM Report Series Research in Management ERS-2002-108-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Berrang-Ford, Lea & Garton, Kelly, 2013. "Expert knowledge sourcing for public health surveillance: National tsetse mapping in Uganda," Social Science & Medicine, Elsevier, vol. 91(C), pages 246-255.
    2. Sauvenier, Xavier & Valckx, Jan & Van Cauwenbergh, Nora & Wauters, Erwin & Bachev, Hrabrin & Biala, K. & Bielders, Charles & Brouckaert, Veronique & Garcia-Cidad, V. & Goyens, S. & Hermy, Martin & Mat, 2005. "Framework for assessing sustainability levels in Belgium agricultural systems - SAFE," MPRA Paper 99616, University Library of Munich, Germany.

  14. Potharst, R. & Kaymak, U. & Pijls, W.H.L.M., 2001. "Neural Networks for Target Selection in Direct Marketing," ERIM Report Series Research in Management ERS-2001-14-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Jonker, J.-J. & Piersma, N. & Potharst, R., 2002. "Direct Mailing Decisions for a Dutch Fundraiser," ERIM Report Series Research in Management ERS-2002-111-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Jonker, J.-J. & Piersma, N. & Potharst, R., 2002. "Direct Mailing Decisions for a Dutch Fundraiser," Econometric Institute Research Papers ERS-2002-111-LIS, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Potharst, R. & van Rijthoven, M. & van Wezel, M.C., 2005. "Modeling brand choice using boosted and stacked neural networks," Econometric Institute Research Papers EI 2005-05, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.

  15. Kaymak, U. & Sousa, J.M., 2001. "Weighted Constraints in Fuzzy Optimization," ERIM Report Series Research in Management ERS-2001-19-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Canós, L. & Liern, V., 2008. "Soft computing-based aggregation methods for human resource management," European Journal of Operational Research, Elsevier, vol. 189(3), pages 669-681, September.

  16. van den Berg, J.H. & van den Bergh, W.-M. & Kaymak, U., 2001. "Probabilistic and Statistical Fuzzy Set Foundations of Competitive Exception Learning," ERIM Report Series Research in Management ERS-2001-40-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Loquin, Kevin & Strauss, Olivier, 2008. "Histogram density estimators based upon a fuzzy partition," Statistics & Probability Letters, Elsevier, vol. 78(13), pages 1863-1868, September.
    2. van den Berg, J.H. & Kaymak, U. & Almeida e Santos Nogueira, R.J., 2011. "Function Approximation Using Probabilistic Fuzzy Systems," ERIM Report Series Research in Management ERS-2011-026-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    3. van den Berg, J.H. & van den Bergh, W.-M. & Kaymak, U., 2003. "Financial Markets Analysis by Probabilistic Fuzzy Modelling," ERIM Report Series Research in Management ERS-2003-036-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

  17. Kaymak, U. & Setnes, M., 2000. "Extended Fuzzy Clustering Algorithms," ERIM Report Series Research in Management ERS-2000-51-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    Cited by:

    1. Nesrin Alptekin, 2014. "Comparison of Turkey and European Union Countries’ Health Indicators by Using Fuzzy Clustering Analysis," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(10), pages 68-74, October.
    2. Nesrin Alptekin, 2014. "Comparison of Turkey and European Union Countries’ Health Indicators by Using Fuzzy Clustering Analysis," International Journal of Business and Social Research, LAR Center Press, vol. 4(10), pages 68-74, October.

Articles

  1. da Costa, Paulo Roberto de Oliveira & Akçay, Alp & Zhang, Yingqian & Kaymak, Uzay, 2020. "Remaining useful lifetime prediction via deep domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 195(C).

    Cited by:

    1. Xu, Danyang & Qiu, Haobo & Gao, Liang & Yang, Zan & Wang, Dapeng, 2022. "A novel dual-stream self-attention neural network for remaining useful life estimation of mechanical systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Zhu, Yongmeng & Wu, Jiechang & Wu, Jun & Liu, Shuyong, 2022. "Dimensionality reduce-based for remaining useful life prediction of machining tools with multisensor fusion," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    3. Bae, Jinwoo & Xi, Zhimin, 2022. "Learning of physical health timestep using the LSTM network for remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    5. Zhang, Huixin & Xi, Xiaopeng & Pan, Rong, 2023. "A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    6. Xiao, Lei & Tang, Junxuan & Zhang, Xinghui & Bechhoefer, Eric & Ding, Siyi, 2021. "Remaining useful life prediction based on intentional noise injection and feature reconstruction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    7. Liu, Shaowei & Jiang, Hongkai & Wu, Zhenghong & Yi, Zichun & Wang, Ruixin, 2023. "Intelligent fault diagnosis of rotating machinery using a multi-source domain adaptation network with adversarial discrepancy matching," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    8. Liu, Shujie & Fan, Lexian, 2022. "An adaptive prediction approach for rolling bearing remaining useful life based on multistage model with three-source variability," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    9. Wang, Yuan & Lei, Yaguo & Li, Naipeng & Yan, Tao & Si, Xiaosheng, 2023. "Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    10. Li, Yuanfu & Chen, Yao & Hu, Zhenchao & Zhang, Huisheng, 2023. "Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    11. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Li, Wenqiang & Peng, Pei, 2021. "A hybrid deep transfer learning strategy for short term cross-building energy prediction," Energy, Elsevier, vol. 215(PB).
    12. Zhou, Taotao & Zhang, Laibin & Han, Te & Droguett, Enrique Lopez & Mosleh, Ali & Chan, Felix T.S., 2023. "An uncertainty-informed framework for trustworthy fault diagnosis in safety-critical applications," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    13. Yan, Jianhai & He, Zhen & He, Shuguang, 2023. "Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    14. Li, Qi & Chen, Liang & Kong, Lin & Wang, Dong & Xia, Min & Shen, Changqing, 2023. "Cross-domain augmentation diagnosis: An adversarial domain-augmented generalization method for fault diagnosis under unseen working conditions," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    15. Cheng, Han & Kong, Xianguang & Wang, Qibin & Ma, Hongbo & Yang, Shengkang, 2022. "The two-stage RUL prediction across operation conditions using deep transfer learning and insufficient degradation data," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    16. Zhuang, Jichao & Jia, Minping & Zhao, Xiaoli, 2022. "An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    17. Chen, Jiaxian & Li, Dongpeng & Huang, Ruyi & Chen, Zhuyun & Li, Weihua, 2023. "Aero-engine remaining useful life prediction method with self-adaptive multimodal data fusion and cluster-ensemble transfer regression," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    18. Zhou, Tuqiang & Wu, Wanting & Peng, Liqun & Zhang, Mingyang & Li, Zhixiong & Xiong, Yubing & Bai, Yuelong, 2022. "Evaluation of urban bus service reliability on variable time horizons using a hybrid deep learning method," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    19. Arias Chao, Manuel & Kulkarni, Chetan & Goebel, Kai & Fink, Olga, 2022. "Fusing physics-based and deep learning models for prognostics," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    20. Zhuang, Jichao & Jia, Minping & Ding, Yifei & Ding, Peng, 2021. "Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    21. Li, Wanxiang & Shang, Zhiwu & Gao, Maosheng & Qian, Shiqi & Feng, Zehua, 2022. "Remaining useful life prediction based on transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    22. Han Cheng & Xianguang Kong & Qibin Wang & Hongbo Ma & Shengkang Yang & Gaige Chen, 2023. "Deep transfer learning based on dynamic domain adaptation for remaining useful life prediction under different working conditions," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 587-613, February.
    23. Wenbai Chen & Weizhao Chen & Huixiang Liu & Yiqun Wang & Chunli Bi & Yu Gu, 2022. "A RUL Prediction Method of Small Sample Equipment Based on DCNN-BiLSTM and Domain Adaptation," Mathematics, MDPI, vol. 10(7), pages 1-14, March.
    24. Zhang, Wei & Li, Xiang & Ma, Hui & Luo, Zhong & Li, Xu, 2021. "Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    25. Ding, Yifei & Jia, Minping & Miao, Qiuhua & Huang, Peng, 2021. "Remaining useful life estimation using deep metric transfer learning for kernel regression," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    26. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Jiang, Yuchen & Luo, Hao & Yin, Shen, 2023. "A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    27. Lee, Jinwook & Kim, Myungyon & Ko, Jin Uk & Jung, Joon Ha & Sun, Kyung Ho & Youn, Byeng D., 2022. "Asymmetric inter-intra domain alignments (AIIDA) method for intelligent fault diagnosis of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    28. Fallahdizcheh, Amirhossein & Wang, Chao, 2022. "Transfer learning of degradation modeling and prognosis based on multivariate functional analysis with heterogeneous sampling rates," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    29. Li, Zhanhang & Zhou, Jian & Nassif, Hani & Coit, David & Bae, Jinwoo, 2023. "Fusing physics-inferred information from stochastic model with machine learning approaches for degradation prediction," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    30. Fu, Song & Lin, Lin & Wang, Yue & Guo, Feng & Zhao, Minghang & Zhong, Baihong & Zhong, Shisheng, 2024. "MCA-DTCN: A novel dual-task temporal convolutional network with multi-channel attention for first prediction time detection and remaining useful life prediction," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    31. Fu, Song & Zhang, Yongjian & Lin, Lin & Zhao, Minghang & Zhong, Shi-sheng, 2021. "Deep residual LSTM with domain-invariance for remaining useful life prediction across domains," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    32. Cheng, Han & Kong, Xianguang & Wang, Qibin & Ma, Hongbo & Yang, Shengkang & Xu, Kun, 2023. "Remaining useful life prediction combined dynamic model with transfer learning under insufficient degradation data," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    33. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    34. Hu, Tao & Guo, Yiming & Gu, Liudong & Zhou, Yifan & Zhang, Zhisheng & Zhou, Zhiting, 2022. "Remaining useful life estimation of bearings under different working conditions via Wasserstein distance-based weighted domain adaptation," Reliability Engineering and System Safety, Elsevier, vol. 224(C).

  2. Ludo Waltman & Nees Eck & Rommert Dekker & Uzay Kaymak, 2011. "Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 737-756, December.
    See citations under working paper version above.
  3. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.

    Cited by:

    1. Inkoo Cho & Noah Williams, 2024. "Collusive Outcomes Without Collusion," Papers 2403.07177, arXiv.org.
    2. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    3. Kshitija Taywade & Brent Harrison & Judy Goldsmith, 2022. "Using Non-Stationary Bandits for Learning in Repeated Cournot Games with Non-Stationary Demand," Papers 2201.00486, arXiv.org.
    4. David M. Newbery & Thomas Greve, 2013. "The Strategic Robustness of Mark-up Equilibria," Cambridge Working Papers in Economics 1341, Faculty of Economics, University of Cambridge.
    5. Junyi Xu, 2021. "Reinforcement Learning in a Cournot Oligopoly Model," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1001-1024, December.
    6. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2020. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," Working Paper 1438, Economics Department, Queen's University.
    7. César García-Díaz & Gábor Péli & Arjen van Witteloostuijn, 2020. "The coevolution of the firm and the product attribute space," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-25, June.
    8. Arthur Charpentier & Romuald Elie & Carl Remlinger, 2020. "Reinforcement Learning in Economics and Finance," Papers 2003.10014, arXiv.org.
    9. Zhang Xu & Mingsheng Zhang & Wei Zhao, 2024. "Algorithmic Collusion and Price Discrimination: The Over-Usage of Data," Papers 2403.06150, arXiv.org.
    10. Jeschonneck, Malte, 2021. "Collusion among autonomous pricing algorithms utilizing function approximation methods," DICE Discussion Papers 370, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    11. Juan Manuel Sánchez-Cartas & Alberto Tejero & Gonzalo León, 2021. "Algorithmic Pricing and Price Gouging. Consequences of High-Impact, Low Probability Events," Sustainability, MDPI, vol. 13(5), pages 1-14, February.
    12. M. Bigoni & M. Fort, 2013. "Information and Learning in Oligopoly: an Experiment," Working Papers wp860, Dipartimento Scienze Economiche, Universita' di Bologna.
    13. Viehmann, Johannes & Lorenczik, Stefan & Malischek, Raimund, 2021. "Multi-unit multiple bid auctions in balancing markets: An agent-based Q-learning approach," Energy Economics, Elsevier, vol. 93(C).
    14. Xingchen Xu & Stephanie Lee & Yong Tan, 2023. "Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems," Papers 2309.14548, arXiv.org.
    15. Joseph E. Harrington, 2022. "The Effect of Outsourcing Pricing Algorithms on Market Competition," Management Science, INFORMS, vol. 68(9), pages 6889-6906, September.
    16. Steven Kimbrough & Frederic Murphy, 2009. "Learning to Collude Tacitly on Production Levels by Oligopolistic Agents," Computational Economics, Springer;Society for Computational Economics, vol. 33(1), pages 47-78, February.
    17. Solferino, Nazaria & Solferino, Viviana & Taurino, Serena Fiona, 2015. "The economic analysis of a Q-learning model of Cooperation with punishment," MPRA Paper 66605, University Library of Munich, Germany.
    18. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2020. "Artificial Intelligence, Algorithmic Pricing, and Collusion," American Economic Review, American Economic Association, vol. 110(10), pages 3267-3297, October.
    19. Soria, Jorge & Moya, Jorge & Mohazab, Amin, 2023. "Optimal mining in proof-of-work blockchain protocols," Finance Research Letters, Elsevier, vol. 53(C).
    20. Bingyan Han, 2022. "Can maker-taker fees prevent algorithmic cooperation in market making?," Papers 2211.00496, arXiv.org.
    21. Timo Klein, 2018. "Autonomous Algorithmic Collusion: Q-Learning Under Sequantial Pricing," Tinbergen Institute Discussion Papers 18-056/VII, Tinbergen Institute, revised 01 Nov 2020.
    22. Johnson, Justin Pappas & Rhodes, Andrew & Wildenbeest, Matthijs, 2020. "Platform Design when Sellers Use Pricing Algorithms," TSE Working Papers 20-1146, Toulouse School of Economics (TSE).
    23. Nazaria Solferino & Viviana Solferino & Serena F. Taurino, 2018. "The economics analysis of a Q-learning model of cooperation with punishment and risk taking preferences," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 601-613, October.
    24. Calzolari, Giacomo & Calvano, Emilio & Denicolo, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," CEPR Discussion Papers 15738, C.E.P.R. Discussion Papers.
    25. Viehmann, Johannes & Lorenczik, Stefan & Malischek, Raimund, 2018. "Multi-unit multiple bid auctions in balancing markets: an agent-based Q-learning approach," EWI Working Papers 2018-3, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    26. Calvano, Emilio & Calzolari, Giacomo & Denicoló, Vincenzo & Pastorello, Sergio, 2021. "Algorithmic collusion with imperfect monitoring," International Journal of Industrial Organization, Elsevier, vol. 79(C).
    27. Axel Gautier & Ashwin Ittoo & Pieter Cleynenbreugel, 2020. "AI algorithms, price discrimination and collusion: a technological, economic and legal perspective," European Journal of Law and Economics, Springer, vol. 50(3), pages 405-435, December.
    28. Lucila Porto, 2022. "Q-Learning algorithms in a Hotelling model," Asociación Argentina de Economía Política: Working Papers 4587, Asociación Argentina de Economía Política.
    29. Daniele Condorelli & Massimiliano Furlan, 2023. "Cheap Talking Algorithms," Papers 2310.07867, arXiv.org, revised Dec 2023.
    30. Bingyan Han, 2022. "Cooperation between Independent Market Makers," Papers 2206.05410, arXiv.org.
    31. Tharakunnel, Kurian & Bhattacharyya, Siddhartha, 2009. "Single-leader-multiple-follower games with boundedly rational agents," Journal of Economic Dynamics and Control, Elsevier, vol. 33(8), pages 1593-1603, August.
    32. Hanaki, Nobuyuki & Ishikawa, Ryuichiro & Akiyama, Eizo, 2009. "Learning games," Journal of Economic Dynamics and Control, Elsevier, vol. 33(10), pages 1739-1756, October.
    33. Werner, Tobias, 2023. "Algorithmic and Human Collusion," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277573, Verein für Socialpolitik / German Economic Association.
    34. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2019. "Algorithmic Pricing What Implications for Competition Policy?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 55(1), pages 155-171, August.
    35. Calvano, Emilio & Calzolari, Giacomo & Denicolò, Vincenzo & Pastorello, Sergio, 2023. "Algorithmic collusion: Genuine or spurious?," International Journal of Industrial Organization, Elsevier, vol. 90(C).
    36. Tong Zhang & B. Brorsen, 2011. "Oligopoly firms with quantity-price strategic decisions," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 6(2), pages 157-170, November.
    37. Fourberg, Niklas & Marques-Magalhaes, Katrin & Wiewiorra, Lukas, 2022. "They are among us: Pricing behavior of algorithms in the field," WIK Working Papers 6, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH, Bad Honnef.
    38. Yaroslav Rosokha & Kenneth Younge, 2020. "Motivating Innovation: The Effect of Loss Aversion on the Willingness to Persist," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 569-582, July.
    39. Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
    40. Bingyan Han, 2021. "Understanding algorithmic collusion with experience replay," Papers 2102.09139, arXiv.org, revised Mar 2021.
    41. Fourberg, Niklas & Marques Magalhaes, Katrin & Wiewiorra, Lukas, 2023. "They Are Among Us: Pricing Behavior of Algorithms in the Field," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277958, International Telecommunications Society (ITS).
    42. Hans-Theo Normann & Martin Sternberg, 2021. "Human-Algorithm Interaction: Algorithmic Pricing in Hybrid Laboratory Markets," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2021_11, Max Planck Institute for Research on Collective Goods, revised 13 Apr 2022.
    43. Frédéric Marty, 2023. "Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement," Working Papers halshs-04363106, HAL.
    44. Kshitija Taywade & Brent Harrison & Adib Bagh, 2022. "Modelling Cournot Games as Multi-agent Multi-armed Bandits," Papers 2201.01182, arXiv.org.
    45. Segismundo S. Izquierdo & Luis R. Izquierdo, 2015. "The “Win-Continue, Lose-Reverse” Rule In Oligopolies: Robustness Of Collusive Outcomes," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 18(05n06), pages 1-23, August.

  4. Naso, David & Surico, Michele & Turchiano, Biagio & Kaymak, Uzay, 2007. "Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete," European Journal of Operational Research, Elsevier, vol. 177(3), pages 2069-2099, March.

    Cited by:

    1. Fahimnia, Behnam & Sarkis, Joseph & Eshragh, Ali, 2015. "A tradeoff model for green supply chain planning:A leanness-versus-greenness analysis," Omega, Elsevier, vol. 54(C), pages 173-190.
    2. Liu, Weihua & Wang, Qian & Mao, Qiaomei & Wang, Shuqing & Zhu, Donglei, 2015. "A scheduling model of logistics service supply chain based on the mass customization service and uncertainty of FLSP’s operation time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 83(C), pages 189-215.
    3. J. Behnamian & S. M. T. Fatemi Ghomi, 2016. "A survey of multi-factory scheduling," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 231-249, February.
    4. Tamás Bányai & Béla Illés & Miklós Gubán & Ákos Gubán & Fabian Schenk & Ágota Bányai, 2019. "Optimization of Just-In-Sequence Supply: A Flower Pollination Algorithm-Based Approach," Sustainability, MDPI, vol. 11(14), pages 1-26, July.
    5. Govindan, K. & Jafarian, A. & Khodaverdi, R. & Devika, K., 2014. "Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food," International Journal of Production Economics, Elsevier, vol. 152(C), pages 9-28.
    6. Oluseye Olugboyega & Obuks Ejohwomu & Emmanuel Dele Omopariola & Alohan Omoregie, 2023. "Sustainable Ready-Mixed Concrete (RMC) Production: A Case Study of Five RMC Plants in Nigeria," Sustainability, MDPI, vol. 15(10), pages 1-17, May.
    7. Chevroton, Hugo & Kergosien, Yannick & Berghman, Lotte & Billaut, Jean-Charles, 2021. "Solving an integrated scheduling and routing problem with inventory, routing and penalty costs," European Journal of Operational Research, Elsevier, vol. 294(2), pages 571-589.
    8. Verena Schmid & Karl F. Doerner & Richard F. Hartl & Martin W. P. Savelsbergh & Wolfgang Stoecher, 2009. "A Hybrid Solution Approach for Ready-Mixed Concrete Delivery," Transportation Science, INFORMS, vol. 43(1), pages 70-85, February.
    9. Low, Chinyao & Chang, Chien-Min & Li, Rong-Kwei & Huang, Chia-Ling, 2014. "Coordination of production scheduling and delivery problems with heterogeneous fleet," International Journal of Production Economics, Elsevier, vol. 153(C), pages 139-148.
    10. Shaza Hanif & Shahab Ud Din & Ning Gui & Tom Holvoet, 2023. "Multiagent Coordination and Teamwork: A Case Study for Large-Scale Dynamic Ready-Mixed Concrete Delivery Problem," Mathematics, MDPI, vol. 11(19), pages 1-25, September.
    11. Kofjač Davorin & Kljajić Miroljub & Knaflič Andrej, 2010. "Development of a Web Application for Dynamic Production Scheduling in Small and Medium Enterprises," Organizacija, Sciendo, vol. 43(3), pages 125-135, May.
    12. Cheng, Ba-Yi & Leung, Joseph Y-T. & Li, Kai, 2017. "Integrated scheduling on a batch machine to minimize production, inventory and distribution costs," European Journal of Operational Research, Elsevier, vol. 258(1), pages 104-112.
    13. Surico, M. & Kaymak, U. & Naso, D. & Dekker, R., 2006. "Hybrid Meta-Heuristics for Robust Scheduling," ERIM Report Series Research in Management ERS-2006-018-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    14. Jairo R. Montoya-Torres & Diego A. Ortiz-Vargas, 2014. "Collaboration and information sharing in dyadic supply chains: A literature review over the period 2000–2012," Estudios Gerenciales, Universidad Icesi, November.
    15. Chou, Jui-Sheng & Ongkowijoyo, Citra Satria, 2015. "Reliability-based decision making for selection of ready-mix concrete supply using stochastic superiority and inferiority ranking method," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 29-39.
    16. Zhang, Guoquan & Shang, Jennifer & Li, Wenli, 2011. "Collaborative production planning of supply chain under price and demand uncertainty," European Journal of Operational Research, Elsevier, vol. 215(3), pages 590-603, December.

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 12 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-CMP: Computational Economics (4) 2005-09-29 2006-09-11 2009-07-03 2010-01-16
  2. NEP-CBE: Cognitive and Behavioural Economics (3) 2005-09-29 2006-02-19 2008-06-21
  3. NEP-ETS: Econometric Time Series (2) 2013-08-16 2014-01-17
  4. NEP-FIN: Finance (2) 2003-12-07 2005-02-27
  5. NEP-FMK: Financial Markets (2) 2005-02-27 2006-03-18
  6. NEP-COM: Industrial Competition (1) 2003-12-07
  7. NEP-ECM: Econometrics (1) 2013-08-16
  8. NEP-GTH: Game Theory (1) 2005-09-29
  9. NEP-MIC: Microeconomics (1) 2006-02-19
  10. NEP-RMG: Risk Management (1) 2013-08-16
  11. NEP-SPO: Sports and Economics (1) 2013-08-16
  12. NEP-UPT: Utility Models and Prospect Theory (1) 2012-02-01

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