Aalaei, A., & Davoudpour, H. (2017). A robust optimization model for cellular manufacturing system into supply chain management. International Journal of Production Economics, 183, 667–679.
Adida, E., & Joshi, P. (2009). A robust optimisation approach to project scheduling and resource allocation. International Journal of Services Operations and Informatics, 4(2), 169–193.
Ahmed, S., & Sahinidis, N. V. (1998). Robust Process Planning under Uncertainty. Industrial & Engineering Chemistry Research, 37(5), 1883–1892.
Ait-Alla, A., Teucke, M., Lutjen, M., Beheshti-Kashi, S., & Karimi, H. R. (2014). Robust production planning in fashion apparel industry under demand uncertainty via conditional value at risk. Mathematical Problems in Engineering, 2014.
Allahverdi, A. (2015). The third comprehensive survey on scheduling problems with setup times/costs. European Journal of Operational Research, 246(2), 345–378.
An, K., & Ouyang, Y. (2016). Robust grain supply chain design considering post-harvest loss and harvest timing equilibrium. Transportation Research Part E: Logistics and Transportation Review, 88, 110–128.
An, Q., Bai, G., Yang, Y., Wang, C., Huang, Q., Liu, C., … Xiang, B. (2016). Preparation optimization of ATO particles by robust parameter design. Materials Science in Semiconductor Processing, 42, 354–358.
Anderson, R., Wei, Z., Cox, I., Moore, M., & Kussener, F. (2015). Monte Carlo Simulation Experiments for Engineering Optimisation. Studies in Engineering and Technology, 2(1), 97–102.
Antoniol, G., Di Penta, M., & Harman, M. (2004). A robust search-based approach to project management in the presence of abandonment, rework, error and uncertainty. In Proceedings. 10th International Symposium on IEEE. (pp. 172–183).
Ardakani, M. K., & Noorossana, R. (2008). A new optimization criterion for robust parameter design - The case of target is best. International Journal of Advanced Manufacturing Technology, 38(9), 851–859.
Ardakani, M. K., Noorossana, R., Akhavan Niaki, S. T., & Lahijanian, H. (2009). Robust parameter design using the weighted metric method-the case of “the smaller the better.” International Journal of Applied Mathematics and Computer Science, 19(1), 59–68.
Artigues, C., Leus, R., & Talla Nobibon, F. (2013). Robust optimization for resource-constrained project scheduling with uncertain activity durations. Flexible Services and Manufacturing Journal, 25(1–2), 175–205.
Asafuddoula, M., Singh, H., & Ray, T. (2015). Six-Sigma Robust Design Optimization using a Many-objective Decomposition Based Evolutionary Algorithm. IEEE Transactions on Evolutionary Computation, 19(4), 490–507.
Asih, H. M., & Chong, K. E. (2015). An Integrated Robust Optimization Model of Capacity Planning under Demand Uncertainty in Electronic Industry. International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS, 15(3), 88–96.
Ateme-Nguema, B. H., Etsinda-Mpiga, F., Dao, T., & Songmene, V. (2012). An Approach-Based on Response Surfaces Method and Ant Colony System for Multi-Objective Optimization :A Case Study. In INTECH Open Access Publisher.
Auzins, J., Chate, A., Rikards, R., & Skukis, E. (2015). Metamodeling and robust minimization approach for the identification of elastic properties of composites by vibration method. ZAMM .Journal of Applied Mathematics and Mechanics, 95(10), 1012–1026.
Bandi, C., & Bertsimas, D. (2014). Robust option pricing. European Journal of Operational Research, 239(3), 842–853.
Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust Optimization. Princeton University Press.
Bergman, B., Maré, J. De, Svensson, T., & Loren, S. (2009). Robust design methodology for reliability: exploring the effects of variation and uncertainty. JohnWiley & Sons, Ltd.
Bertsekas, D. (1998). Network Optimization: Continuous and Discrete Models. Belmont: Athena Scientific.
Bertsimas, D., Brown, D. B. D., & Caramanis, C. (2011). Theory and Applications of Robust Optimization. SIAM Review, 53(3), 464–501.
Bertsimas, D., & Sim, M. (2004). The Price of robustness. Operations Research, 52(1), 35–53.
Beyer, H. G., & Sendhoff, B. (2007). Robust optimization - A comprehensive survey. Computer Methods in Applied Mechanics and Engineering, 196(33–34), 3190–3218.
Bossaghzadeh, I., Hejazi, S. R. and, & Pirmoradi, Z. (2015). Developing Robust Project Scheduling Methods for Uncertain Parameters. Amirkabir International Journal of Science and Research (Modelling, Identification, Simulation & Control), 47(1), 21–32.
Cai, L., Zhang, Z., Cheng, Q., Liu, Z., Gu, P., & Qi, Y. (2016). An approach to optimize the machining accuracy retainability of multi-axis NC machine tool based on robust design. Precision Engineering, 43, 370–386.
Can, C., Wu, T., Hu, M., Weir, D. J., & Chu, X. (2014). Accuracy vs. robustness: bi-criteria optimized ensemble of metamodels. In In Proceedings of the 2014 Winter Simulation Conference (pp. 616–627).
Cao, L., Jiang, P., Chen, Z., Zhou, Q., & Zhou, H. (2015). Metamodel Assisted Robust Optimization under Interval Uncertainly Based on Reverse Model. IFAC-PapersOnLine, 48(28), 1178–1183.
Carson, Y., & Maria, A. (1997). Simulation Optimization: Methods and Applications. In Proceedings of the 29th Conference on Winter Simulation, 118–126.
Celano, G., Faraz, A., & Saniga, E. (2014). Control charts monitoring product’s loss to society. Quality and Reliability Engineering International, 30(8), 1393–1407.
Charnes, A., & Cooper, W. W. (1977). Goal programming and multiple objective optimizations. European Journal of Operational Research, 1(1), 39–54.
Chen, C. (2004). Robust Design Based on Fuzzy Optimization. Tamsui Oxford Journal of Mathematical Sciences, 20(1), 65–72.
Chen, H.-W., Wong, W. K., & Xu, H. (2012). An augmented approach to the desirability function. Journal of Applied Statistics, 39(3), 599–613.
Chen, W., Wiecek, M. M., & Zhang, J. (1999). Quality utility : a Compromise Programming approach to robust design. Journal of Mechanical Design, 121(2), 179–187.
Cheng, Q., Xiao, C., Zhang, G., Gu, P., & Cai, L. (2013). An analytical robust design optimization methodology based on axiomatic design principles. Quality and Reliability Engineering International, 30(7), 1059–1073.
Chevalier, C., Bect, J., Ginsbourger, D., Vazquez, E., Picheny, V., & Richet, Y. (2014). Fast Parallel Kriging-Based Stepwise Uncertainty Reduction With Application to the Identification of an Excursion Set. Technometrics, 56(4), 455–465.
Chou, C. Y., & Chang, C. . (2001). Minimum-Loss Assembly Tolerance Allocation by Considering Product Degradation and Time Value of Money. International Journal of Advanced Manufacturing Technology, 17(2), 139–146.
Cohen, I., Golany, B., & Shtub, A. (2007). The Stochastic Time–Cost Tradeoff Problem: A Robust Optimization Approach. Networks, 45(2), 175–188.
Costa, N. R., Louren, J., & Pereira, Z. L. (2011). Desirability function approach: A review and performance evaluation in adverse conditions. Chemometrics and Intelligent Laboratory Systems, 107(2), 234–244.
Dalton, S. K., Atamturktur, S., Farajpour, I., & Juang, C. H. (2013). An optimization based approach for structural design considering safety, robustness, and cost. Engineering Structures, 57, 356–363.
Datta, S., & Mahapatra, S. S. (2010). Modeling, simulation and parametric optimization of wire EDM process using response surface methodology coupled with grey-Taguchi technique. MultiCraft International Journal of Engineering, Science and Technology Extension, 2(5), 162–183.
Deb, K. (2011). Multi-objective optimization using evolutionary algorithms: an introduction, 3–34.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Del Castillo, E., & Montgomery, D. C. (1993). A nonlinear programming solution to the dual response problem. Journal of Quality Technology, 25, 199–204.
Dellino, G., Kleijnen, Jack, P. C., & Meloni, C. (2015). Metamodel-Based Robust Simulation-Optimization: An Overview. In Uncertainty Management in Simulation-Optimization of Complex Systems, 27–54.
Dellino, G., Kleijnen, J. P. C., & Meloni, C. (2009). Robust simulation-optimization using metamodels. In Winter Simulation Conference (pp. 540–550).
Dellino, G., Kleijnen, J. P. C., & Meloni, C. (2010a). Parametric and distribution-free bootstrapping in robust simulation-optimization. In Proceedings - Winter Simulation Conference (pp. 1283–1294).
Dellino, G., Kleijnen, J. P. C., & Meloni, C. (2010). Robust optimization in simulation: Taguchi and Response Surface Methodology. International Journal of Production Economics, 125(1), 52–59.
Dellino, G., Kleijnen, J. P. C., & Meloni, C. (2010b). Simulation-optimization under uncertainty through metamodeling and bootstrapping. Procedia - Social and Behavioral Sciences, 2(6), 7640–7641.
Dellino, G., Kleijnen, J. P. C., & Meloni, C. (2012). Robust optimization in simulation: Taguchi and Krige combined. INFORMS Journal on Computing, 24(3), 471–484.
Dellino, G., Lino, P., Meloni, C., & Rizzo, A. (2009). Kriging metamodel management in the design optimization of a CNG injection system. Mathematics and Computers in Simulation, 79(8), 2345–2360.
Demeulemeester, E., & Herroelen, W. (2011). Robust Project Scheduling. Foundations and Trends® in Technology, Information and Operations Management, 3(3–4), 201–376.
Ehrgott, M., Ide, J., & Schöbel, A. (2014). Minmax robustness for multi-objective optimization problems. European Journal of Operational Research, 239(1), 17–31.
Erdbrügge, M., Kuhnt, S., & Rudak, N. (2011). Joint optimization of independent multiple responses. Quality and Reliability Engineering International, 27(5), 689–704.
Fu, H., Sendhoff, B., Tang, K., & Yao, X. (2012). Characterizing environmental changes in Robust Optimization Over Time. In WCCI 2012 IEEE Congress on Evolutionary Computation (pp. 10–15). IEEE.
Fu, H., Sendhoff, B., Tang, K., & Yao, X. (2015). Robust optimization over time: Problem difficulties and benchmark problems. IEEE Transactions on Evolutionary Computation, 19(5), 731–745.
Fu, N., Lau, H. C., & Varakantham, P. (2015). Robust execution strategies for project scheduling with unreliable resources and stochastic durations. Journal of Scheduling, 18(6), 607–622.
Gabrel, V., Murat, C., & Thiele, A. (2014). Recent advances in robust optimization: An overview. European Journal of Operational Research, 235(3), 471–483.
Gang, W., Wang, S., Augenbroe, G., & Xiao, F. (2015). Robust optimal design of district cooling systems and the impacts of uncertainty and reliability. Energy and Buildings, 159, 265–275.
Gasior, D., & Józefczyk, J. (2009). Application of uncertain variables to production planning in a class of manufacturing systems. Bulletin of the Polish Academy of Sciences: Technical Sciences, 57(3), 257–263.
Geletu, A., & Li, P. (2014). Recent Developments in Computational Approaches to Optimization under Uncertainty and Application in Process Systems Engineering. ChemBioEng Reviews, 1(4), 170–190.
Ghodratnama, A., Tavakkoli-Moghaddam, R., & Azaron, A. (2015). Robust and fuzzy goal programming optimization approaches for a novel multi-objective hub location-allocation problem: A supply chain overview. Applied Soft Computing Journal, 37, 255–276.
Goberna, M. A., Jeyakumar, V., Li, G., & Vicente-Perez, J. (2015). Robust solutions to multi-objective linear programs with uncertain data. European Journal of Operational Research, 242(3), 730–743.
Goerigk, M., & Schöbel, A. (2015). Algorithm Engineering in Robust Optimization. In Algorithm engineering (pp. 245–279). Springer International Publishing.
Gorissen, B. L. (2015). Robust Fractional Programming. Journal of Optimization Theory and Applications, 166(2), 508–528.
Grossmann, I. E., Apap, R. M., Calfa, B. A., Garcia-Herreros, P., & Zhang, Q. (2016). Recent Advances in Mathematical Programming Techniques for the Optimization of Process Systems under Uncertainty. Computer and Chemical Engineering, 91, 3–14.
Gul, G., & Zoubir, A. M. (2017). Minimax Robust Hypothesis Testing. IEEE Transactions on Information Theory.
Gulpinar, N., & Pachamanova, D. (2013). A robust optimization approach to asset-liability management under time-varying investment opportunities. Journal of Banking and Finance, 37(6), 2031–2041.
Gyulai, D., Kadar, B., & Monosotori, L. (2015). Robust production planning and capacity control for flexible assembly lines. IFAC Proceedings Volumes (IFAC-PapersOnline), 48(3), 2312–2317.
H.Myers, R., C.Montgomery, D., & Anderson-Cook, M, C. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments-Fourth Edition. Wiley Series in Probability and Statistics.
Hahn, E. D. (2008). Mixture densities for project management activity times: A robust approach to PERT. European Journal of Operational Research, 188(2), 450–459.
Hao, X., Lin, L., & Gen, M. (2014). An effective multi-objective EDA for robust resource constrained project scheduling with uncertain durations. Procedia Computer Science, 36, 571–578.
Hasani, A., & Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study. Transportation Research Part E: Logistics and Transportation Review, 87, 20–52.
Hasuike, T., & Ishii, H. (2009). Robust Expectation Optimization Model Using the Possibility Measure for the Fuzzy Random. Applications of Soft Computing, 58, 285–294.
Hazir, O., Haouari, M., & Erel, E. (2010). Robust scheduling and robustness measures for the discrete time/cost trade-off problem. European Journal of Operational Research, 207(2), 633–643.
He, Z., Wang, J., Jinho, O., & H. Park, S. (2010). Robust optimization for multiple responses using response surface methodology. Applied Stochastic Models in Business and Industry, 26, 157–171.
Herroelen, W., & Leus, R. (2004). Robust and reactive project scheduling: a review and classification of procedures. International Journal of Production Research, 42(8), 1599–1620.
Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research, 165(2), 289–306.
HO, C.-J. (1989). Evaluating the impact of operating environments on MRP system nervousness. The International Journal of Production Research, 27(7), 1115–1135.
Huang, Y., Zhang, Y., Li, N., & Chambers, J. (2016). A Robust Gaussian Approximate Fixed-Interval Smoother for Nonlinear Systems With Heavy-Tailed Process and Measurement Noises, 23(4), 468–472.
Hwang, C. ., & Masud, A. S. M. (2012). Multiple objective decision making—methods and applications: a state-of-the-art survey (Vol. 164). Springer Science & Business Media.
Iancu, D. a, & Trichakis, N. (2014). Pareto Efficiency in Robust Optimization. Management Science, 60(1), 130–147.
Ide, J., & Schobel, A. (2016). Robustness for uncertain multi-objective optimization: a survey and analysis of different concepts. OR Spectrum, 38(1), 235–271.
Janak, S. L., Lin, X., & Floudas, C. A. (2007). A new robust optimization approach for scheduling under uncertainty. II. Uncertainty with known probability distribution. Computers and Chemical Engineering, 31(3), 171–195.
Jin, L., Huang, G. H., Cong, D., & Fan, Y. R. (2014). A Robust Inexact Joint-optimal alfa cut Interval Type-2 Fuzzy Boundary Linear Programming (RIJ-IT2FBLP) for energy systems planning under uncertainty. International Journal of Electrical Power and Energy Systems, 56, 19–32.
Jin, R., Chen, W., & Simpson, T. W. (2001). Comparative studies of metamodelling techniques under multiple modelling criteria. Structural and Multidisciplinary Optimization, 23(1), 1–13.
Jin, R., Du, X., & Chen, W. (2003). The use of metamodeling techniques for optimization under uncertainty. Structural and Multidisciplinary Optimization, 25(2), 99–116.
Jin, Y., & Branke, J. (2005). Evolutionary optimization in uncertain environments-a survey. IEEE Transactions on Evolutionary Computation, 9(3), 303–317.
Jin, Y., Tang, K., Yu, X., Sendhoff, B., & Yao, X. (2013). A framework for finding robust optimal solutions over time. Memetic Computing, 5(1), 3–18.
Jurecka, F. (2007). Robust Design Optimization Based on Metamodeling Techniques. PhD Thesis.
Kartal-Koç, E., Batmaz, İ., & Weber, G.-W. (2012). Robust Regression Metamodeling of Complex Systems: The Case of Solid Rocket Motor Performance Metamodeling. In Advances in Intelligent Modelling and Simulation (pp. 221–251). Springer Berlin Heidelberg.
Khademi Zare, H., Fatemi Ghomi, S. M. T., & Karimi, B. (2006). Developing a heuristic algorithm for order production planning using network models under uncertainty conditions. Applied Mathematics and Computation, 182(2), 1208–1218.
Khaledi, K., Miro, S., König, M., & Schanz, T. (2014). Robust and reliable metamodels for mechanized tunnel simulations. Computers and Geotechnics, 61, 1–12.
Khan, J., Teli, S. ., & Hada, B. . (2015). Reduction Of Cost Of Quality By Using Robust Design : A Research Methodology. International Journal of Mechanical and Industrial Technology, 2(2), 122–128.
Klimek, M., & Lebkowski, P. (2011). Resource allocation for robust project scheduling. Bulletin of the Polish Academy of Sciences-Technical Sciences, 59(1), 51–55.
Ko, Y., Kim, K., & Jun, C. (2005). A New Loss Function-Based Method for Multiresponse Optimization. Journal of Quality Technology, 37(1), 50–59.
Kokkinos, O., & Papadopoulos, V. (2016). Robust design with Variability Response Functions; an alternative approach. Structural Safety, 59, 1–8.
Kolluri, S. S., Esfahani, I. J., & Yoo, C. (2016). Robust fuzzy and multi-objective optimization approaches to generate alternate solutions for resource conservation of eco-industrial park involving various future events. Process Safety and Environmental Protection, 103, 424–441.
Krige, D. G. (1951). A statistical approach to some mine valuation and allied problems on the Witwatersrand.
Kuhn, K., Raith, A., Schmidt, M., & Schobel, A. (2016). Bi-objective robust optimisation. European Journal of Operational Research, 252(2), 418–431.
Lambrechts, O., Demeulemeester, E., & Herroelen, W. (2011). Time slack-based techniques for robust project scheduling subject to resource uncertainty. Annals of Operations Research, 186(1), 443–464.
Lee, C. L., & Tang, G. R. (2000). Tolerance design for products with correlated characteristics. Mechanism and Machine Theory, 35(12), 1675–1687.
Lehman, J. S., Santner, T. J., & Notz, W. I. (2004). Designing Computer Experiments To Determine Robust Control Variables. Statistica Sinica, 14(1), 571–590.
Lersteau, C., Rossi, A., & Sevaux, M. (2016). Robust scheduling of wireless sensor networks for target tracking under uncertainty. European Journal of Operational Research, 252(2), 407–417.
Lin, D. K. J., & Tu, W. (1995). Dual response surface optimization. Journal of Quality Technology, 27(1), 34–39.
Ling, A., Sun, J., Xiu, N., & Yang, X. (2017). Robust Two-stage Stochastic Linear Optimization with Risk Aversion. European Journal of Operational Research, 256(1), 215–229.
Liu, X., Yue, R.-X., & Chatterjee, K. (2015). Model-robust R-optimal designs in linear regression models. Journal of Statistical Planning and Inference, 167, 135–143.
Lopez Martin, M. M., Garcia Garcia, C. B., Garcia Perez, J., & Sanchez Granero, M. A. (2012). An alternative for robust estimation in project management. European Journal of Operational Research, 220(2), 443–451.
Margellos, K., Goulart, P., & Lygeros, J. (2014). On the road between robust optimization and the scenario approach for chance constrained optimization problems. IEEE Transactions on Automatic Control, 59(8), 2258–2263.
Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26(6), 369–395.
Marti, K. (2015). Stochastic Optimization Methods- Third Edition. Berlin: Springer.
Martınez-Frutos, E. J., & Marti-Montrull, P. (2012). Metamodel-based multi-objective robust design optimization of structures. Computer Aided Optimum Design in Engineering XII, 125, 35–45.
Mavris, D. N., Bandte, O., & DeLaurentis, D. A. (1999). Robust Design Simulation: A Probabilistic Approach to Multidisciplinary Design. Journal of Aircraft, 36(1), 298–307.
Mavrotas, G., Figueira, J. R., & Siskos, E. (2015). Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection. Omega, 52, 142–155.
Messac, A., & Ismail-Yahaya, A. (2002). Multiobjective robust design using physical programming. Structural and Multidisciplinary Optimization, 23(5), 357–371.
Miettinen, K. (2012). Nonlinear multiobjective optimization (Vol. 12). Springer Science & Business Media.
Miranda, A. N. A. K., & Castillo, E. D. E. L. (2011). Robust Parameter Design Optimization of Simulation Experiments Using Stochastic Perturbation Methods. Journal of the Operational Research Society, 62(1), 198–205.
Mirmajlesi, S. R., & Shafaei, R. (2016). An integrated approach to solve a robust forward/reverse supply chain for short lifetime products. Computers & Industrial Engineering, 97, 222–239.
Mirzapour Al-e-Hashem, S. M. J., Malekly, H., & Aryanezhad, M. B. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International Journal of Production Economics, 134(1), 28–42.
Modarres, M., & Izadpanahi, E. (2016). Aggregate production planning by focusing on energy saving: A robust optimization approach. Journal of Cleaner Production, 133, 1074–1085.
Mula, J., Poler, R., García-Sabater, G. S., & Lario, F. C. (2006). Models for production planning under uncertainty: A review. International Journal of Production Economics, 103(1), 271–285.
Myers, R. H., Khuri, A. I., & Vining, G. (1990). Response Surface Alternatives to the Taguchi Robust Parameter Design Approach. The American Statistician, 46(2), 131–139.
Myers, R. H., KIM, Y., & Griffiths, K. L. (1997). Response Surface Methods and the Use of Noise Variables. Journal of Quality Technology, 29(4), 429–440.
Namazian, A., & Yakhchali, S. H. (2016). Modeling and Solving Project Portfolio and Contractor Selection Problem Based on Project Scheduling under Uncertainty. Procedia - Social and Behavioral Sciences, 226, 35–42.
Ng, L. Y., Chemmangattuvalappil, N. G., & Ng, D. K. S. (2015). Robust chemical product design via fuzzy optimisation approach. Computers and Chemical Engineering, 83, 186–202.
Nha, V. T., Shin, S., & Jeong, S. H. (2013). Lexicographical dynamic goal programming approach to a robust design optimization within the pharmaceutical environment. European Journal of Operational Research, 229(2), 505–517.
Oros, A., Topa, M., Amariutei, R. D., Buzo, A., & Rafaila, M. (2014). Robust Response Design for Heterogeneous Systems based on Metamodels. In Electronics, Circuits and Systems (ICECS), 2014 21st IEEE International Conference (pp. 602–605). IEEE.
Palacios, J. J., Puente, J., Vela, C. R., & Gonzalez-Rodrriguez, I. (2016). Robust multiobjective optimisation for fuzzy job shop problems. Applied Soft Computing.
Park, C., & Leeds, M. (2016). A Highly Efficient Robust Design Under Data Contamination. Computers & Industrial Engineering, 93, 131–142.
Park, G., & Lee, T. (2006). Robust Design : An Overview. Aiaa Journal, 44(1), 181–191.
Park, S. (1996). Robust design and Analysis for Quality Engineering. Boom Koninklijke Uitgevers.
Park, S., & Antony, J. (2008). Robust design for quality engineering and six sigma. World Scientific Publishing Co Inc.
Parsopoulos, K. E., & Vrahatis, M. N. (2002). Particle swarm optimization method in multiobjective problems. Proceedings of the 2002 ACM Symposium on Applied Computing, 603–607.
Peng, H. P., Jiang, X. Q., Xu, Z. G., & Liu, X. J. (2008). Optimal tolerance design for products with correlated characteristics by considering the present worth of quality loss. International Journal of Advanced Manufacturing Technology, 39(1), 1–8.
Peri, D. (2016). Robust Design Optimization for the refit of a cargo ship using real seagoing data. Ocean Engineering, 123, 103–115.
Persson, J., & Ölvander, J. (2013). Comparison of different uses of metamodels for robust design optimization. In American Institute of Aeronautics and … (p. 1039).
Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall PTR.
Pishvaee, M. S., & Fazli Khalaf, M. (2016). Novel robust fuzzy mathematical programming methods. Applied Mathematical Modelling, 40, 407–418.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637–649.
Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433–3446.
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets and Systems, 206, 1–20.
Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets and Systems, 161(20), 2668–2683.
Popescu, I. (2007). Robust Mean-Covariance Solutions for Stochastic Optimization. Operations Research, 55(1), 98–112.
Rathod, V., Yadav, O. P., Rathore, A., & Jain, R. (2013). Optimizing reliability-based robust design model using multi-objective genetic algorithm. Computers & Industrial Engineering, 66(2), 301–310.
Romano, D., Varetto, M., & Vicario, G. (2004). Multiresponse Robust Design: A general Framework Based on Combined Array. Journal of Quality Technology, 36(1), 27.
Sahali, M. A., Serra, R., Belaidi, I., & Chibane, H. (2015). Bi-objective robust optimization of machined surface quality and productivity under vibrations limitation. In MATEC Web of Conferences (Vol. 20). EDP Sciences.
Salmasnia, A., Ameri, E., & Niaki, S. T. A. (2016). A robust loss function approach for a multi-objective redundancy allocation problem. Applied Mathematical Modelling, 40(1), 635–645.
Salmasnia, A., Bashiri, M., & Salehi, M. (2013). A robust interactive approach to optimize correlated multiple responses. International Journal of Advanced Manufacturing Technology, 67(5–8), 1923–1935.
Salmasnia, A., Mokhtari, H., & Kamal Abadi, I. N. (2012). A robust scheduling of projects with time, cost, and quality considerations. International Journal of Advanced Manufacturing Technology, 60(5–8), 631–642.
Salomon, S., Avigad, G., Fleming, P. J., & Purshouse, R. C. (2014). Active robust optimization: Enhancing robustness to uncertain environments. IEEE Transactions on Cybernetics, 44(11), 2221–2231.
Sandgren, E., & Cameron, T. M. (2002). Robust design optimization of structures through consideration of variation. Computers and Structures, 80(20), 1605–1613.
Shahin, A. (2006). Robust design: an advanced quality engineering methodology for change management in the third millennium. In Proceedings of the 7th International Conference of Quality Managers (pp. 201–212).
Sharma, N. K., & Cudney, E. A. (2011). Signal-to-Noise ratio and design complexity based on Unified Loss Function – LTB case with Finite Target. International Journal of Engineering, Science and Technology, 3(7), 15–24.
Sharma, N. K., Cudney, E. A., Ragsdell, K. M., & Paryani, K. (2007). Quality Loss Function – A Common Methodology for Three Cases. Journal of Industrial and Systems Engineering, 1(3), 218–234.
Simpson, T. W., Poplinski, J. D., Koch, P. N., & Allen, J. K. (2001). Metamodels for Computer-based Engineering Design: Survey and recommendations. Engineering With Computers, 17(2), 129–150.
Singh, J., Frey, D. D., Soderborg, N., & Jugulum, R. (2007). Compound Noise: Evaluation as a Robust Design Method. Quality and Reliability Engineering International, 23(3), 517–543.
Steimel, J., & Engell, S. (2015). Conceptual design and optimization of chemical processes under uncertainty by two-stage programming. Computers and Chemical Engineering, 81, 200–217.
Stinstra, E., & den Hertog, D. (2008). Robust optimization using computer experiments. European Journal of Operational Research, 191(3), 816–837.
Su, J., & Renaud, E. . (1997). Automatic differentiation in robust optimization. AIAA Journal, 35(6), 1072–1079.
Suman, B., & Kumar, P. (2006). A Survey of Simulated Annealing as a Tool for Single and Multiobjective Optimization on JSTOR. Journal of the Operational Research Society, 57(10), 1143–1160.
Sun, G., Fang, J., Tian, X., Li, G., & Li, Q. (2015). Discrete robust optimization algorithm based on Taguchi method for structural crashworthiness design. Expert Systems with Applications, 42(9), 4482–4492.
Sun, W., Dong, R., & Xu, H. (2010). A novel non-probabilistic approach using interval analysis for robust design optimization. Journal of Mechanical Science and Technology, 23(12), 3199–3208.
Tabrizi, B. H., & Ghaderi, S. F. (2016). A robust bi-objective model for concurrent planning of project scheduling and material procurement. Computers & Industrial Engineering, 98, 11–29.
Takriti, S., & Ahmed, S. (2004). On robust optimization of two-stage systems. Mathematical Programming, 99(1), 109–126.
Talaei, M., Farhang Moghaddam, B., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry. Journal of Cleaner Production, 113, 662–673.
Tsai, T.-N., & Liukkonen, M. (2016). Robust parameter design for the micro-BGA stencil printing process using a fuzzy logic-based Taguchi method. Applied Soft Computing, 48, 124–136.
Ur Rehman, S., Langelaar, M., & van Keulen, F. (2014). Efficient Kriging-based robust optimization of unconstrained problems. Journal of Computational Science, 5(6), 872–881.
Wang, G., & Shan, S. (2007). Review of Metamodeling Techniques in Support of Engineering Design Optimization. Journal of Mechanical Design, 129(4), 370–380.
Wang, G., & Shan, S. (2011). Review of Metamodeling Techniques for Product Design with Computation-intensive Processes. Proceedings of the Canadian Engineering …, 1–11.
Wang, H., Lu, Z., Zhao, H., & Feng, H. (2015). Application of Parallel Computing in Robust Optimization Design Using MATLAB. In Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2015 Fifth International Conference (pp. 1228–1231). IEEE.
Wang, J., Ma, Y., Ouyang, L., & Tu, Y. (2016). A new Bayesian approach to multi-response surface optimization integrating loss function with posterior probability. European Journal of Operational Research, 249(1), 231–237.
Wang, S., & Pedrycz, W. (2015). Robust Granular Optimization: A Structured Approach for Optimization under Integrated Uncertainty. IEEE Transactions on Fuzzy Systems, 23(5), 1372–1386.
Wu, D., Gao, W., Wang, C., Tangaramvong, S., & Tin-Loi, F. (2016). Robust fuzzy structural safety assessment using mathematical programming approach. Fuzzy Sets and Systems, 293, 30–49.
Wu, F. (2015). Robust Design of Mixing Static and Dynamic Multiple Quality Characteristics. World Journal of Engineering and Technology, 3(3), 72–77.
Wu, F.-C., & Yeh, C.-H. (2009). Robust design of nonlinear multiple dynamic quality characteristics. Computers and Industrial Engineering, 56(4), 1328–1332.
Wu, J., Gao, J., Luo, Z., & Brown, T. (2016). Robust topology optimization for structures under interval uncertainty. Advances in Engineering Software, 99, 36–48.
Wu, J., Luo, Z., Zhang, Y., & Zhang, N. (2014). An interval uncertain optimization method for vehicle suspensions using Chebyshev metamodels. Applied Mathematical Modelling, 38(15), 3706–3723.
Wu, Y., Sun, Y., & Chen, L. (2016). Robust adaptive finite-time synchronization of nonlinear resource management system. Neurocomputing, 171, 1131–1138.
Yamashita, D. S., Armentano, V. A., & Laguna, M. (2007). Robust optimization models for project scheduling with resource availability cost. Journal of Scheduling, 10(1), 67–76.
Yanikoglu, I., Hertog, D. Den, & Kleijnen, J. P. C. (2016). Robust Dual Response Optimization. IIE Transactions, 48(3), 298–312.
Yu, A., & Zeng, B. (2015). Exploring the Modeling Capacity of Two-Stage Robust Optimization : Variants of Robust. IEEE Transactions on Power Systems, 30(1), 109–122.
Yu, X., Yaochu, J., Ke, T., & Xin, Y. Y. (2010). Robust optimization over time -A new perspective on dynamic optimization problems. In Evolutionary Computation (CEC), 2010 IEEE Congress (pp. 1–6). IEEE.
Zadeh, L. (1963). Optimality and non-scalar-valued performance criteria. Automatic Control, IEEE Transactions on, 8(1), 59–60.
Zhang, D., & Lu, Q. (2016). Robust Regression Analysis with LR-Type Fuzzy Input Variables and Fuzzy Output Variable. Journal of Data Analysis and Information Processing, 4(2), 64–80.
Zhang, J., & Qiao, C. (2015). A bi-objective Model for Robust Resource- constrained Project Scheduling Problem with Random Activity Durations. In Networking, Sensing and Control (ICNSC), 2015 IEEE 12th International Conference (pp. 28–32). IEEE.
Zhang, M., & Guan, Y. (2014). Two-stage robust unit commitment problem. European Journal of Operational Research, 234(3), 751–762.
Zhang, Q., Morari, M. F., Grossmann, I. E., Sundaramoorthy, A., & Pinto, J. M. (2016). An adjustable robust optimization approach to scheduling of continuous industrial processes providing interruptible load. Computers and Chemical Engineering, 86, 106–119.
Zhang, S., Zhu, P., Chen, W., & Arendt, P. (2013). Concurrent treatment of parametric uncertainty and metamodeling uncertainty in robust design. Structural and Multidisciplinary Optimization, 47(1), 63–76.
Zhang, W., Zhang, Y., Bai, X., Liu, J., Zeng, D., & Qiu, T. (2016). A robust fuzzy tree method with outlier detection for combustion models and optimization. Chemometrics and Intelligent Laboratory Systems, 158, 130–137.
Zhu, L., Ma, Y., & Zhang, L. (2013). The robust design of EOQ considering the compound effect of both noise uncertainty and meta-modeling uncertainty. In Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium (Vol. 1, pp. 173–177). IEEE.