YÖNEYLEM ARAŞTIRMASI ALANINDA OPTİMİZASYON LİTERATÜRÜ KONUSUNDA BİR TARAMA

Yıl 2020, Cilt: 22 Sayı: 3, 1023 - 1044, 29.09.2020

Meryem Pulat , Dilayla Bayyurt , İpek Deveci Kocakoç

https://doi.org/10.16953/deusosbil.535425

Öz

Optimizasyon, matematiksel olarak ifade edilebilen problemlerde, belirli kısıt ya da sınırlandırılmış koşulları sağlayan bir amaç fonksiyonu için en iyi çözüm değerinin bulunmasını amaçlayan iteratif bir arama sürecidir. Bu alanda yazılmış yüzlerce kitap bulunmaktadır ve her geçen gün listeye yeni bir kitap daha eklenmektedir. Optimizasyon çok geniş bir alan olduğundan, her kitap farklı disiplinlere yönelik olarak yazılmaktadır. Bu alanla ilgili herhangi bir konuda çalışılmak istenildiğinde ilgili kitaba ulaşmak karmaşık ve zaman alıcı olabilmektedir. Araştırmada ulaşılmak istenen nokta optimizasyon konusundaki kitaplara ve kitapların içerdikleri konulara ilişkin kapsamlı bir araştırma yapmak ve ilgili istatistiklerin elde edilmesidir. Bu amaçla son 10 yıla ait ulaşılabilen optimizasyon kitapları taranmış ve yöneylem araştırması ile ilgili olanlar detaylı olarak konularına göre incelenmiştir. Bu çalışma, uzun süren bir emeğin ürünüdür ve yöneylem araştırması alanında optimizasyon konusu ile ilgili kitaplara yönelik literatür dökümünü çıkararak, bu konuyla ilgili çalışma yapmak isteyenlere yol göstermeyi amaçlamaktadır.

Anahtar Kelimeler

Optimizasyon, Yöneylem Araştırması, Literatür Araştırması

A REVIEW ON OPTIMIZATION LITERATURE RELATED TO OPERATIONS RESEARCH FIELD

Öz

Optimization is an iterative search process aimed at finding the best solution value for an objective function that satisfies constraints or bounded conditions in mathematically expressible problems. There are hundreds of books written in this field, and a new book is added to the list everyday. Since optimization is a very large field, each book is written for different disciplines. If you want to work on any subject related to this field, reaching the related book can be complicated and time consuming. The point to be reached in the research is to do an extensive research on the optimization books and to obtain relevant statistics. For this purpose, available optimization books related to the operations research of the last ten years were searched and examined in detail according to their topics. This work is aiming at leading the people who want to study about this topic by searching the literature about the optimization in the field of operations research.

Anahtar Kelimeler

Optimization, Operations Research, Literature Review

Kaynakça

• Abrão, T. (Ed.). (2013). Search Algorithms for Engineering Optimization.Croatia: InTech.
• Absil, P. A., Mahony, R., ve Sepulchre, R. (2008). Optimization algorithms on matrix manifolds. New Jersey: Princeton University Press.
• Aguiar e Oliveira, H. (2016). Evolutionary global optimization, manifolds and applications. Switzerland: Springer Publishing Company.
• Ahuja, R. K., Möhring, R. H., ve Zaroliagis, C. (Eds.). (2009). Robust and online large-scale optimization: models and techniques for transportation systems(Vol. 5868).Berlin Heidelberg: Springer.
• Alba, E., Blum, C., Asasi, P., Leon, C., ve Gomez, J. A. (Eds.). (2009). Optimization techniques for solving complex problems(Vol. 76).Hoboken, N. J.: John Wiley & Sons.
• Al-Mezel, S. A. R., Al-Solamy, F. R. M., ve Ansari, Q. H. (Eds.). (2014). Fixed point theory, variational analysis, and optimization. Boca Raton: CRC Press.
• Alves, C., Clautiaux, F., De Carvalho, J. V., ve Rietz, J. (2016). Dual-Feasible Functions for Integer Programming and Combinatorial Optimization: Basics, Extensions and Applications. Switzerland: Springer.
• Anjos, M. F., ve Lasserre, J. B. (2012). Handbook on semidefinite, conic and polynomial optimization, International Series in Operations Research & Management Science, vol. 166.New York: Springer.
• Ansari, Q. H., Lalitha, C. S., ve Mehta, M. (2013). Generalized Convexity, Nonsmooth Variational Inequalities, and Nonsmooth Optimization.Boca Raton: CRC Press.
• Arora, R. K. (2015). Optimization: algorithms and applications. Boca Raton: CRC Press.
• Bagirov, A., Karmitsa, N., ve Mäkelä, M. M. (2014). Introduction to Nonsmooth Optimization: theory, practice and software. Switzerland: Springer.
• Barbu, V., ve Precupanu, T. (2012). Convexity and optimization in Banach spaces. Springer Science & Business Media.
• Bartholomew-Biggs, M. (2008). Nonlinear optimization with engineering applications (Vol. 19).New York: Springer Science & Business Media.
• Bartz-Beielstein, T., Chiarandini, M., Paquete, L., ve Preuss, M. (Eds.). (2010). Experimental methods for the analysis of optimization algorithms (pp. 978-3642025372). New York: Springer.
• Bechikh, S., Datta, R., ve Gupta, A. K. (Eds.). (2017). Recent advances in evolutionary multi-objective optimization. Berlin Heidelberg: Springer.
• Beck, A. (2014). Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with MATLAB. Switzerland: Society for Industrial and Applied Mathematics.
• Belegundu, A. D., ve Chandrupatla, T. R. (2011). Optimization concepts and applications in engineering. USA: Cambridge University Press.
• Bensoussan, A. (2011). Dynamic Programming and Inventory Control: Volume 3 Studies in Probability. Optimization and Statistics. Amsterdam:IOS Press.
• Bernhard, K., ve Vygen, J. (2012). Combinatorial optimization: Theory and algorithms. Berlin Heidelberg: Springer.
• Bertsekas, D. P. (2009). Convex optimization theory(pp. 157-226). Belmont: Athena Scientific.
• Bertsekas, D. P., ve Scientific, A. (2015). Convex optimization algorithms. Belmont: Athena Scientific.
• Best, M. J. (2010). Portfolio optimization. Boca Raton: CRC Press.
• Biegler, L. T. (2010). Nonlinear programming: concepts, algorithms, and applications to chemical processes. USA: Society for industrial and applied mathematics.
• Blum, C., Roli, A., ve Sampels, M. (Eds.). (2008). Hybrid metaheuristics: an emerging approach to optimization(Vol. 114). Berlin Heidelberg: Springer.
• Borne, P., Popescu, D., Filip, F. G., ve Stefanoiu, D. (2013). Optimization in Engineering Sciences: Exact Methods. Hoboken, N. J.: John Wiley & Sons.
• Bot, R. I., Grad, S. M., ve Wanka, G. (2009). Duality in vector optimization. Berlin Heidelberg: Springer Science & Business Media.
• Boyd, S., ve Vandenberghe, L. (2009).Convex optimization. New York: Cambridge university press.
• Branke, J., Deb, K., ve Miettinen, K. (Eds.). (2008). Multiobjective optimization: Interactive and evolutionary approaches(Vol. 5252). Springer Science & Business Media.
• Buckley, J. J., ve Jowers, L. J. (2008). Monte Carlo methods in fuzzy optimization. BerlinHeidelberg: Springer.
• Byrne, C. L. (2014). A first course in optimization. Berlin Heidelberg: CRC Press.
• Byrne, C. L. (2014). Iterative Optimization in Inverse Problems. Boca Raton: CRC Press.
• Calafiore, G. C., ve El Ghaoui, L. (2014). Optimization models. USA: Cambridge university press.
• Cambini, A., ve Martein, L. (2009).Generalized convexity and optimization: theory and applications(Vol. 616). Berlin Heidelberg: Springer Science & Business Media.
• Carlos Cotta, C., ve Hemert, J. (Eds.). (2008). Recent Advances in Evolutionary Computation for Combinatorial Optimization (Vol.153). Berlin Heidelberg: Springer-Verlag.
• Chaovalitwongse, W., Furman, K. C., ve Pardalos, P. M. (Eds.). (2009). Optimization and logistics challenges in the enterprise. USA: Springer-Verlag.
• Chi, C. Y., Li, W. C., ve Lin, C. H. (2017). Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications. Boca Raton: CRC Press.
• Chinchuluun, A., Pardalos, P. M., Enkhbat, R., ve Tseveendorj, I. (Eds.). (2010). Optimization and optimal control (p. 510).New York: Springer.
• Chinchuluun, A.,Pardalos,P.M., Enkhbat,R.,ve Pistikopoulos,E.N.(Eds.). (2013).Optimization, Simulation, and Control. New York: Springer Science+Business Media.
• Chong, E. K., ve Zak, S. H. (2008). An introduction to optimization(Vol. 76).Hoboken, N. J.: John Wiley & Sons.
• Chong, E. K., ve Zak, S. H. (2013). An introduction to optimization(Vol. 76).Hoboken, N. J.: John Wiley & Sons.
• Christensen, P. W., ve Klarbring, A. (2009). An introduction to structural optimization(Vol. 153).Sweden: Springer Science & Business Media.
• Chvátal, V. (Ed.). (2011). Combinatorial Optimization Methods and Applications. Amsterdam: IOS Press.
• Čiegis, R., Henty, D., Kågström, B., ve Žilinskas, J. (2008). Parallel Scientific Computing and Optimization. New York: Springer Science & Business Media.
• Conn, A. R., Scheinberg, K., ve Vicente, L. N. (2009). Introduction to derivative-free optimization. USA: Society for Industrial and Applied Mathematics.
• Consiglio, A., Nielson, S. S., Zenios, S.A.(2009). Practical financial optimization: a library of GAMS models. John Wiley & Sons.
• Cortez, P. (2014). Modern optimization with R. Switzerland: Springer.
• Datta, R., ve Deb, K. (Eds.). (2015). Evolutionary constrained optimization. India: Springer.
• Dattorro, J. (2010). Convex optimization ve Euclidean distance geometry. USA: Mepoo.
• De los Reyes, J. C. (2015). Numerical PDE-constrained optimization. Springer.
• Delfour, M. C. (2012). Introduction to optimization and semidifferential calculus. USA: Society for Industrial and Applied Mathematics.
• Diwekar, U. (2008). Introduction to applied optimization(Vol. 22).NewYork: Springer Science & Business Media.
• Du, D. Z., ve Ko, K. I. (2014). Theory of computational complexity(Vol. 58).Hoboken, N. J.: John Wiley & Sons.
• Du, D. Z., Ko, K. I., ve Hu, X. (2012). Design and analysis of approximation algorithms (Vol. 62).New York: Springer Science & Business Media.
• Du, K. L., ve Swamy, M. N. S. (2016). Search and optimization by metaheuristics: techniques and algorithms inspired by nature. Switzerland: Birkhäuser.
• Eichfelder, G. (2008). Adaptive scalarization methods in multiobjective optimization(Vol. 436).Berlin Heidelberg: Springer.
• Elishakoff, I., ve Ohsaki, M. (2010). Optimization and anti-optimization of structures under uncertainty. USA: World Scientific.
• Emrouznejad, A., (Ed.). (2016). Big Data Optimization: Recent Developments and Challenges (Vol. 18). Switzerland:Springer.
• Fasano, G., ve Pintér, J. D. (Eds.). (2015). Optimized Packings with Applications (Vol. 105). Switzerland: Springer.
• Forst, W., ve Hoffmann, D. (2010). Optimization—Theory and Practice. New York: Springer Science & Business Media.
• Friesz, T. L., ve Bernstein, D. (2016). Foundations of Network Optimization and Games. New York: Springer.
• Gao, D. Y., ve Sherali, H. D. (Eds.). (2009). Advances in Applied Mathematics and Global Optimization. New York: Springer Science & Business Media.
• Gao, D., Ruan, N., ve Xing, W. (Eds.). (2014). Advances in Global Optimization (Vol. 95). Switzerland: Springer.
• Gaspar-Cunha, A., Antunes, C. H., ve Coello, C. A. C. (Eds.). (2015). Evolutionary Multi-Criterion Optimization: 8th International Conference, EMO 2015, Guimarães, Portugal, March 29--April 1, 2015. Proceedings (Vol. 9019). Switzerland: Springer.
• Gen, M., Cheng, R., ve Lin, L. (2008). Network models and optimization: Multiobjective genetic algorithm approach. London: Springer Science & Business Media.
• Goberna, M. A., ve López, M. A. (2014). Post-optimal analysis in linear semi-infinite optimization. Springer Science & Business Media.
• Guo, L., ve Wang, H. (2010). Stochastic distribution control system design: a convex optimization approach. London:Springer Science & Business Media.
• Güler, O. (2010). Foundations of optimization (Vol. 258). New York: Springer Science & Business Media.
• Günlük, O., ve Woeginger, G. J. (Eds.). (2011). Integer Programming and Combinatorial Optimization: 15th International Conference, IPCO 2011, New York, NY, USA, June 15-17, 2011. Proceedings (Vol. 6655). Berlin Heidelberg: Springer.
• Held.,H.(2009).Shape Optimization under Uncertainty from a Stochastic Programming Point of View.Wiesbaden: Vieweng+Teubner.
• Hendrix, E. M., ve Boglárka, G. (2010). Introduction to nonlinear and global optimization.New York: Springer.
• Hirsch, M., Commander, C. W., Pardalos, P. M., ve Murphey, R. (Eds.). (2009). Optimization and Cooperative Control Strategies: Proceedings of the 8th International Conference on Cooperative Control and Optimization (Vol. 381). Berlin Heidelberg: Springer Science & Business Media.
• Hooker, J. N. (2012). Integrated methods for optimization (Vol. 100). New York:Springer Science & Business Media.
• Hurlbert,G.H. (2010). Linear Optimization: The Simplex Workbook.New York: Springer Science+Business Media.
• Iqbal, K. (2013).Fundamental Engineering Optimization Methods-eBooks and textbooks. Bookboon. com.
• Jahn, J.(2011). Vector optimization. Berlin Heidelberg: Springer.
• Jaluria, Y. (2008). Design and optimization of thermal systems. Boca Raton: CRC press.
• Jeyakumar, V., Luc, D.T.(2008). Nonsmooth vector functions and continuous optimization (Vol. 10). New York: Springer Science & Business Media.
• Kaipa, K. N., ve Ghose, D. (2017). Glowworm Swarm Optimization: Theory, Algorithms, and Applications (Vol. 698). Switzerland: Springer.
• Kanno, Y. (2011). Nonsmooth mechanics and convex optimization. USA: CRC Press.
• Kasperski, A. (2008). Discrete optimization with interval data. Studies in fuzziness and soft computing, 228. Berlin Heidelberg: Springer.
• Kennigton J., Olinicik, E., veRajan, D. (Eds.).(2011). Wireless Network Design:Optimization Models and Solution Procedures.New York: Springer Science & Business Media.
• Korte, B., ve Vygen, J. (2008). Combinatorial optimization (Vol. 2). Berlin Heidelberg: Springer.
• Kosmol, P., ve Müller-Wichards, D. (2011). Optimization in function spaces: with stability considerations in Orlicz spaces (Vol. 13). Berlin: Walter de Gruyter.
• Koziel, S., ve Yang, X. S. (Eds.). (2011). Computational optimization, methods and algorithms (Vol. 356). Berlin Heidelberg: Springer.
• Köppen, M., Schaefer, G., ve Abraham, A. (Eds.). (2011). Intelligent Computational Optimization in Engineering: Techniques & Applications (Vol. 366). Berlin Heidelberg: Springer Science &Business Media.
• Krichen, S., ve Chaouachi, J. (2014). Graph-related Optimization and Decision Support Systems. Hoboken, N. J.: John Wiley & Sons.
• Kubiak, W. (2009). Proportional optimization and fairness (Vol. 127). New York: Springer Science & Business Media.
• Kulkarni, A. J., Krishnasamy, G., ve Abraham, A. (2017). Cohort intelligence: a socio-inspired optimization method. Switzerland: Springer.
• Kwon, R. H. (2014). Introduction to linear optimization and extensions with MATLAB®. Boca Raton:CRC Press.
• Lange, K. (2013). Optimization. New York: Springer.
• Lau, L. C., Ravi, R., ve Singh, M. (2011). Iterative methods in combinatorial optimization (Vol. 46). New York: Cambridge University Press.
• Lawrence, K. D., Klimberg, R. K., ve Miori, V. M. (Eds.). (2010). The Supply Chain in Manufacturing, Distribution, and Transportation: Modeling, Optimization, and Applications. Boca Raton: CRC press.
• Lazinica, A. (Ed.). (2009). Particle swarm optimization. Croatia: InTech.
• Lee, K. Y., ve El-Sharkawi, M. A. (Eds.). (2008). Modern heuristic optimization techniques: theory and applications to power systems (Vol. 39). Hoboken, N. J.: John Wiley & Sons.
• Leugering, G., Engell, S., Griewank, A., Hinze, M., Rannacher, R., Schulz, V., ve Ulbrich, S. (Eds.). (2012). Constrained optimization and optimal control for partial differential equations (Vol. 160). Basel: Springer Science & Business Media.
• Levy, A. B. (2009). The basics of practical optimization. USA: Society for Industrial and Applied Mathematics.
• Levy, A. B. (2012). Stationarity and Convergence in Reduce-or-retreat Minimization. USA: Springer Science & Business Media.
• Lisnianski, A., Frenkel, I., ve Ding, Y. (2010). Multi-state system reliability analysis and optimization for engineers and industrial managers. London: Springer Science & Business Media.
• Lodwick, W. A., ve Kacprzyk, J. (Eds.). (2010). Fuzzy optimization: Recent advances and applications (Vol. 254). Berlin Heidelberg: Springer.
• Lozovanu, D. Pickl, S. (2009). Optimization and multiobjective control of time-discrete systems: dynamic networks and multilayered structures. Berlin Heidelberg: Springer Science & Business Media.
• Lu, Y. Z., Chen, Y. W., Chen, M. R., Chen, P., ve Zeng, G. Q. (2016). Extremal Optimization: Fundamentals, Algorithms, and Applications. Boca Raton: CRC Press.Luptacik, M. (2010). Mathematical optimization and economic analysis (p. 307). New York: Springer.
• Martí, R., ve Reinelt, G. (2011). The linear ordering problem: exact and heuristic methods in combinatorial optimization (Vol. 175).Berlin Heidelberg: Springer Science & Business Media.
• Martin, A., Klamroth, K., Lang, J., Leugering, G., Morsi, A., Oberlack, M., ve Rosen, R. (Eds.). (2012). Mathematical optimization of water networks (Vol. 162). Basel: Springer Science & Business Media.
• Meisel, S. (2011). Anticipatory optimization for dynamic decision making (Vol. 51). New York: Springer Science & Business Media.
• Mester, D., Ronin, Y., Korostishevsky, M., Frenkel, Z., Bräysy, O., Dullaert, W.,ve Korol, A. (2010). Discrete optimization for TSP-like genome mapping problems.New York: Nova Science Publishers, Inc.
• Mishra, S. K. (Ed.). (2011). Topics in Nonconvex Optimization. New York: Springer.
• Mishra, S. K., ve Upadhyay, B. B. (2015). Pseudolinear Functions and Optimization. Boca Raton:CRC Press.
• Mishra, S. K., Wang, S. Y., ve Lai, K. K. (2008). Generalized convexity and vector optimization (Vol. 90). Berlin Heidelberg: Springer.
• Mohammadi, B., ve Pironneau, O. (2010). Applied shape optimization for fluids. New York: Oxford university press.
• Momoh, J. A. (2016). Adaptive stochastic optimization techniques with applications. Boca Raton:CRC Press.
• Morgan, P. B. (2015). An Explanation of Constrained Optimization for Economists. London:University of Toronto Press.
• Murty, K. G. (2010). Optimization for decision making. New York: Springer.
• Nakayama, H., Yun, Y., ve Yoon, M. (2009). Sequential approximate multiobjective optimization using computational intelligence. Berlin Heidelberg: Springer Science & Business Media.
• Nash, J. C. (2014). Nonlinear parameter optimization using R tools. United Kingdom: John Wiley & Sons.
• Neumann, F., ve Witt, C. (2010). Bioinspired computation in combinatorial optimization: algorithms and their computational complexity. Berlin Heidelberg: Springer.
• Ohsaki, M. (2011). Optimization of finite dimensional structures. Boca Raton: CRC Press.
• Oliveira, C. A., ve Pardalos, P. M. (2011). Mathematical aspects of network routing optimization. New York: Springer.
• Onn, S. (2010). Nonlinear discrete optimization.Germany: European Mathematical Society.
• Palomar, D. P., ve Eldar, Y. C. (Eds.). (2010). Convex optimization in signal processing and communications. New York: Cambridge university press.
• Parsopoulos, K. E.,ve Vrahatis, M.E. (2010). Particle swarm optimization and intelligence: advances and applications: advances and applications. New York: IGI global.
• Paschos V.T. (Ed.). (2010). Combinatorial Optimization: Concepts of Combinatorial Optimization (Vol. 1). Great Britain: John Wiley & Sons.
• Paschos, V. T. (Ed.). (2010). Paradigms of combinatorial optimization: problems andnew approaches (Vol. 2). Great Britain: John Wiley & Sons.
• Paschos, V. T. (Ed.). (2014). Applications of combinatorial optimization. Great Britain: John Wiley & Sons.
• Paschos, V. T. (Ed.). (2014). Paradigms of combinatorial optimization: problems andnew approaches (Vol. 2). Great Britain: John Wiley & Sons. Pearce, C. E., ve Hunt, E. (Eds.). (2009). Optimization: Structure and Applications (Vol. 32). New York: Springer Science & Business Media.
• Pop, P. C. (2012). Generalized network design problems: modeling and optimization (Vol. 1). Berlin/Boston:Walter de Gruyter.
• Pytlak, R. (2009). Conjugate gradient algorithms in nonconvex optimization (Vol. 89). Berlin Heidelberg: Springer Science & Business Media.
• Rachev, S., Stoyanov, S., ve Fabozzi, F. (2008). Advanced Stochastic Models, Risk Assessment and Portfolio Management: The ideal risk, uncertainty and performance measures.Hoboken, N. J.: John Wiley & Sons.
• Resendel, M. G., ve Ribeiro, C. C. (2016). GRASP with path-relinking: Recent advances and applications. In Metaheuristics: progress as real problem solvers (pp. 29-63). New York: Springer.
• Rothwell, A. (2017). Optimization Methods in Structural Design. Switzerland: Springer.
• Sandou, G. (2013). Metaheuristic optimization for the design of automatic control laws. Great Britain: John Wiley & Sons.
• Sarker, R. A., ve Newton, C. S. (2008). Optimization modelling: a practical approach. Boca Raton: CRC Press.
• Sarker, R., Mohammadian, M., ve Yao, X. (Eds.). (2008). Evolutionary optimization (Vol. 48). New York: Springer Science & Business Media.
• Saxena, P., Singh,D.,ve Pant,M. (2016). Problem Solving and Uncertainty Modeling through Optimization and Soft Computing Applications. New York: IGI Global.
• Schäffler, S. (2012). Global optimization: a stochastic approach. New York: Springer Science & Business Media.
• Schmidt, M. (2014). Integrating Routing Decisions in Public Transportation Problems, Volume 89 of Optimization and Its Applications.New York: Springer.
• Schrijver, A. (2008). A course in combinatorial optimization. Delft: TU Delft.
• Sergienko, I. V. (2014). Topical Directions of Informatics. New York:Springer-Verlag.
• Shkelzen, C. (Ed.). (2010). Modeling, simulation and optimization: focus on applications. India: In Tech.
• Shao, J., Sun, Y., ve Noche, B. (2015). Optimization of Integrated Supply Chain Planning Under Multiple Uncertainty. Berlin Heidelberg: Springer Berlin Heidelberg.
• Shikhman, V. (2012). Topological aspects of nonsmooth optimization (Vol. 64). New York: Springer Science & Business Media.
• Siarry, P. (2008). Advances in metaheuristics for hard optimization. Berlin Heidelberg: Springer Science & Business Media.
• Silva, A., Neves, R., ve Horta, N. (2016). Portfolio optimization using fundamental indicators based on multi-objective EA. Switzerland: Springer.
• Song, D. P. (2013). Optimal control and optimization of stochastic supply chain systems. London: Springer Science & Business Media.
• Spillers, W. R., ve MacBain, K. M. (2009). Structural optimization. New York: Springer Science & Business Media.
• Sra, S., Nowozin, S., ve Wright, S. J. (Eds.). (2012). Optimization for machine learning. Cambridge: Mit Press.
• Sumathi, S., ve Kumar, L. A. (2016). Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/SIMULINK®. Boca Raton: CRC Press.
• Tenne, Y., ve Goh, C. K. (Eds.). (2010). Computational intelligence in optimization: applications and implementations (Vol. 7). Berlin Heidelberg: Springer Science & Business Media.
• Thai, M. T., ve Pardalos, P. M. (Eds.). (2012). Handbook of optimization in complex networks: theory and applications (Vol. 57). New York: Springer Science & Business Media.
• Thevenin, D., ve Janiga, G. (Eds.). (2008). Optimization and Computational Fluid Dynamics. Berlin Heidelberg: Springer.
• Toscano, R. (2013). Structured controllers for uncertain systems. London: Springer.
• Toth, P., ve Vigo, D. (Eds.). (2014). Vehicle routing: problems, methods, and applications. USA: Society for Industrial and Applied Mathematics.
• Touati, S., ve De Dinechin, B. (2014). Advanced Backend Optimization. Great Britain: John Wiley & Sons.
• Treiber, M. A. (2013). Optimization for computer vision. Advances in Computer Vision and Pattern Recognition. London:Springer Science & Business Media.
• Trevisan, L. (2011). Combinatorial optimization: exact and approximate algorithms. San Francisco, California: Standford University.
• Tuy, H., (2016). Convex analysis and global optimization. Switzerland: Springer.
• Van Hentenryck, P., ve Milano, M. (Eds.). (2011). Hybrid optimization: the ten years of CPAIOR (Vol. 45). New York: Springer Science & Business Media.
• Varela, J.,Acuña, S. (Eds.). (2011). Handbook Of Optimization Theory: Decision Analysis And Application. New York: Nova Science Publishers, Inc.
• Wahde, M. (2008). Biologically inspired optimization methods: an introduction. Great Britain: WIT press.
• Walter, É. (2014). Numerical methods and optimization. Switzerland: Springer.
• Wang, S., ve Watada, J. (2012). Fuzzy stochastic optimization: theory, models and applications. New York: Springer Science & Business Media.
• Wang, Y., Yagola, A. G., ve Yang, C. (Eds.). (2010). Optimization and regularization for computational inverse problems and applications. Berlin Heidelberg: Springer.
• Wiesemann, W. (2012). Optimization of temporal networks under uncertainty(Vol. 10). Berlin Heidelberg:Springer Science & Business Media.
• Xie, L. (2015). Decision Support for Crew Rostering in Public Transit: Web-Based Optimization System for Cyclic and Non-Cyclic Rostering. Wiesbaden: Springer.
• Yalaoui, A., Chehade, H., Yalaoui, F., ve Amodeo, L. (2012). Optimization of logistics. Great Britain:John Wiley & Sons.
• Yang, X. (2008). Introduction to mathematical optimization. From Linear Programming to Metaheuristics. Cambridge: Cambridge International Science Publishing.
• Yang, X. S. (2010). Engineering optimization: an introduction with metaheuristic applications. Hoboken, N. J.: John Wiley & Sons.
• Yang, X. S., ve Koziel, S. (Eds.). (2011). Computational optimization and applications in engineering and industry (Vol. 359). Berlin Heidelberg: Springer Science & Business Media.
• Zaslavski, A. J. (2016). Numerical Optimization with Computational Errors (Vol. 108). Switzerland: Springer.
• Zhang, J., ve Sanderson, A. C. (2009). Adaptive differential evolution (pp. 83-93). Berlin Heidelberg: Springer
• Zhigljavsky, A., ve Žilinskas, A. (2008). Stochastic global optimization (Vol. 9). New York: Springer Science & Business Media.