Skip to main content
SpringerLink
Log in
Book cover

Design and Analysis of Simulation Experiments pp 241–300Cite as

Simulation Optimization

  • Chapter

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 230))

Abstract

This chapter is organized as follows. Section 6.1 introduces the optimization of real systems that are modeled through either deterministic or random simulation; this optimization we call simulation optimization or briefly optimization. There are many methods for this optimization, but we focus on methods that use specific metamodels of the underlying simulation models; these metamodels were detailed in the preceding chapters, and use either linear regression or Kriging. Section 6.2 discusses the use of linear regression metamodels for optimization. Section 6.2.1 summarizes basic response surface methodology (RSM), which uses linear regression; RSM was developed for experiments with real systems. Section 6.2.2 adapts this RSM to the needs of random simulation. Section 6.2.3 presents the adapted steepest descent (ASD) search direction. Section 6.2.4 summarizes generalized RSM (GRSM) for simulation with multiple responses. Section 6.2.5 summarizes a procedure for testing whether an estimated optimum is truly optimal—using the Karush-Kuhn-Tucker (KKT) conditions. Section 6.3 discusses the use of Kriging metamodels for optimization. Section 6.3.1 presents efficient global optimization (EGO), which uses Kriging. Section 6.3.2 presents Kriging and integer mathematical programming (KrIMP) for the solution of problems with constrained outputs. Section 6.4 discusses robust optimization (RO), which accounts for uncertainties in some inputs. Section 6.4.1 discusses RO using RSM, Sect. 6.4.2 discusses RO using Kriging, and Sect. 6.4.3 summarizes the Ben-Tal et al. approach to RO. Section 6.5 summarizes the major conclusions of this chapter, and suggests topics for future research. The chapter ends with Solutions of exercises, and a long list of references.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Ajdari A, Mahlooji H (2014) An adaptive hybrid algorithm for constructing an efficient sequential experimental design in simulation optimization. Commun Stat Simul Comput 43:947–968

    Article  Google Scholar 

  • Alaeddini A, Yang K, Mao H, Murat A, Ankenman B (2013) An adaptive sequential experimentation methodology for expensive response surface optimization—case study in traumatic brain injury modeling. Qual Reliab Eng Int 30(6): 767–793

    Article  Google Scholar 

  • Alrabghi A, Tiwari A (2015) State of the art in simulation-based optimisation for maintenance systems. Comput Ind Eng (in press)

    Google Scholar 

  • Andrieu L, Cohen G, Vázquez-Abad FJ (2011) Gradient-based simulation optimization under probability constraints. Eur J Oper Res 212:345–351

    Article  Google Scholar 

  • Angün ME (2004) Black box simulation optimization: generalized response surface methodology. CentER dissertation series, Tilburg University, Tilburg, Netherlands (also published by VDM Verlag Dr. Müller, Saarbrücken, Germany, 2011)

    Google Scholar 

  • Angün E (2011) A risk-averse approach to simulation optimization with multiple responses. Simul Model Pract Theory 19:911–923

    Article  Google Scholar 

  • Angün E, den Hertog D, Gürkan G, Kleijnen JPC (2009) Response surface methodology with stochastic constraints for expensive simulation. J Oper Res Soc 60(6):735–746

    Article  Google Scholar 

  • Apley DW, Kim J (2011) A cautious approach to robust design with model parameter uncertainty. IIE Trans 43(7):471–482

    Article  Google Scholar 

  • Ardakani MK, Wulff SS (2013) An overview of optimization formulations for multiresponse surface problems. Qual Reliab Eng Int 29:3–16

    Article  Google Scholar 

  • Atlason J, Epelman MA, Henderson SG (2008) Optimizing call center staffing using simulation and analytic center cutting-plane methods. Manag Sci 54(2):295–309

    Article  Google Scholar 

  • Barnes ER (1986) A variation on Karmarkar’s algorithm for solving linear programming problems. Math Program 36:174–182

    Article  Google Scholar 

  • Barton RR, Meckesheimer M (2006) Metamodel-based simulation optimization. In: Simulation. Handbooks in operations research and management science, vol 13. Elsevier/North Holland, Amsterdam, pp 535–574

    Google Scholar 

  • Bashyam S, Fu MC (1998) Optimization of (s, S) inventory systems with random lead times and a service level constraint. Manag Sci 44:243–256

    Article  Google Scholar 

  • Bassamboo A, Randhawa RS, Zeevi A (2010) Capacity sizing under parameter uncertainty: safety staffing principles revisited. Manag Sci 56(10):1668–1686

    Article  Google Scholar 

  • Bates RA, Kenett RS, Steinberg DM, Wynn HP (2006) Achieving robust design from computer simulations. Qual Technol Quant Manag 3(2):161–177

    Article  Google Scholar 

  • Ben-Tal A, Nemirovski A (1998) Robust convex optimization. Math Oper Res 23(4):769–805

    Article  Google Scholar 

  • Ben-Tal A, Nemirovski A (2008) Selected topics in robust convex optimization. Math Program 112(1):125–158

    Article  Google Scholar 

  • Bettonvil BWM, del Castillo E, Kleijnen JPC (2009) Statistical testing of optimality conditions in multiresponse simulation-based optimization. Eur J Oper Res 199(2):448–458

    Article  Google Scholar 

  • Beyer H, Sendhoff B (2007) Robust optimization—a comprehensive survey. Comput Methods Appl Mech Eng 196(33–34):3190–3218

    Article  Google Scholar 

  • Binois M, Ginsbourger D, Roustant O (2015) Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations. Eur J Oper Res 243: 386–394

    Article  Google Scholar 

  • Boukouvalas A, Cornford D, Stehlík M (2014) Optimal design for correlated processes with input-dependent noise. Comput Stat Data Anal 71:1088–1102

    Article  Google Scholar 

  • Box GEP (1999) Statistics as a catalyst to learning by scientific method, part II—a discussion. J Qual Technol 31(1):16–29

    Google Scholar 

  • Box GEP, Wilson KB (1951) On the experimental attainment of optimum conditions. J R Stat Soc Ser B 13(1):1–38

    Google Scholar 

  • Brekelmans R, Driessen L, Hamers H, den Hertog D (2005) Gradient estimation schemes for noisy functions. J Optim Theory Appl 126(3): 529–551

    Article  Google Scholar 

  • Broadie M, Du Y, Moallemi CC (2011) Efficient risk estimation via nested sequential simulation. Manag Sci 57:1172–1194

    Article  Google Scholar 

  • Bull AD (2011) Convergence rates of efficient global optimization algorithms. J Mach Learn Res 12:2879–2904

    Google Scholar 

  • Chang K-H, Hong J, Wan H (2013) Stochastic trust-region response-surface method (STRONG)—a new response-surface framework for simulation optimization. INFORMS J Comput 25(2):230–243

    Article  Google Scholar 

  • Chang K-H, Li M-K, Wan H (2014) Combining STRONG with screening designs for large-scale simulation optimization. IIE Trans 46(4):357–373

    Article  Google Scholar 

  • Chang K-H, Lin G (2015) Optimal design of hybrid renewable energy systems using simulation optimization. Simul Model Pract Theory 52:40–51

    Article  Google Scholar 

  • Chau M, Fu MC, Qu H, Ryzhov I (2014) Simulation optimization: a tutorial overview and recent developments in gradient-based and sequential allocation methods. In: Tolk A, Diallo SY, Ryzhov IO, Yilmaz L, Buckley S, Miller JA (eds) Proceedings of the 2014 winter simulation conference, Savannah, pp 21–35

    Google Scholar 

  • Chen X, Kim K-K (2014) Stochastic kriging with biased sample estimates. ACM Trans Model Comput Simul 24(2):8:1–8:23

    Google Scholar 

  • Chevalier C, Ginsbourger D, Bect J, 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

    Article  Google Scholar 

  • Chih M (2013) A more accurate second-order polynomial metamodel using a pseudo-random number assignment strategy. J Oper Res Soc 64: 198–207

    Article  Google Scholar 

  • Conn AR, Gould NLM, Toint PL (2000) Trust-region methods. SIAM, Philadelphia

    Book  Google Scholar 

  • Davis E, Ierapetritou M (2009) A kriging based method for the solution of mixed-integer nonlinear programs containing black-box functions. J Glob Optim 43:191–205

    Article  Google Scholar 

  • Dehlendorff C, Kulahci M, Andersen K (2011) Designing simulation experiments with controllable and uncontrollable factors for applications in health care. J R Stat Soc Ser C (Appl Stat) 60:31–49

    Article  Google Scholar 

  • Dellino G, Kleijnen JPC, Meloni C (2010) Robust optimization in simulation: Taguchi and response surface methodology. Int J Prod Econ 125(1):52–59

    Article  Google Scholar 

  • Dellino G, Kleijnen JPC, Meloni C (2012) Robust optimization in simulation: Taguchi and Krige combined. INFORMS J Comput 24(3):471–484

    Article  Google Scholar 

  • Dellino G, Meloni C (2013) Quantitative methods to analyze simulation metamodels variability. In: Spitaleri RM (ed) Proceedings of the 11th meeting on applied scientific computing and tools. IMACS series in computational and applied mathematics, vol 17, pp 91–100

    Google Scholar 

  • Dellino G, Meloni C (eds) (2015) Uncertainty management in simulation-optimization of complex systems. Algorithms and applications. Springer, New York

    Google Scholar 

  • Dengiz B (2009) Redesign of PCB production line with simulation and Taguchi design. In: Rossetti MD, Hill RR, Johansson B, Dunkin A, Ingalls RG (eds) Proceedings of the 2009 winter simulation conference, Austin, pp 2197–2204

    Google Scholar 

  • Dykstra RL (1970) Establishing the positive definiteness of the sample covariance matrix. Ann Math Stat 41(6):2153–2154

    Article  Google Scholar 

  • Echard B, Gayton N, Lemaire M (2011) Ak-mcs: an active learning reliability method combining Kriging and Monte Carlo simulation. Struct Saf 33(2):145–154

    Article  Google Scholar 

  • Fan S-KS, Huang K-N (2011) A new search procedure of steepest ascent in response surface exploration. J Stat Comput Simul 81(6):661–678

    Article  Google Scholar 

  • Feyzioğlu O, Pierreval H, Deflandre D (2005) A simulation-based optimization approach to size manufacturing systems. Int J Prod Res 43(2): 247–266

    Article  Google Scholar 

  • Feng Z, Zhang Q, Tang Q, Yang T, Ma Y (2015) A multiobjective optimization based framework to balance the global exploration and local exploitation in expensive optimization. J Glob Optimi, 61(4):677–694

    Article  Google Scholar 

  • Figueira G, Almada-Lobo B (2014) Hybrid simulation-optimization methods: a taxonomy and discussion. Simul Model Pract Theory 46:118–134

    Article  Google Scholar 

  • Forrester AIJ, Jones DR (2008) Global optimization of deceptive functions with sparse sampling. In: 12th AIAA/ISSMO multidisciplinary analysis and optimization conference, Victoria, pp 10–12

    Google Scholar 

  • Forrester AIJ, Keane AJ (2009) Recent advances in surrogate-based optimization. Prog Aerosp Sci 45(1–3):50–79

    Article  Google Scholar 

  • Forrester AIJ, Sóbester A, Keane AJ (2008) Engineering design via surrogate modelling; a practical guide. Wiley, Chichester, pp 79–102

    Book  Google Scholar 

  • Frazier PI (2010) Learning with dynamic programming. In: Cochran JJ, Cox LA, Keskinocak P, Kharoufeh JP, Smith JC (eds) Wiley encyclopedia of operations research and management science. Wiley, New York

    Google Scholar 

  • Frazier P, Powell W, Dayanik S (2009) The knowledge-gradient policy for correlated normal beliefs. INFORMS J Comput 21:599–613

    Article  Google Scholar 

  • Fu MC, Bayraksan G, Henderson SG, Nelson BL, Powell WB, Ryzhov IO, Thengvall B (2014) Simulation optimization: a panel on the state of the art in research and practice. In: Tolk A, Diallo SY, Ryzhov IO, Yilmaz L, Buckley S, Miller JA (eds) Proceedings of the 2014 winter simulation conference, Savannah, pp 3696–3706

    Google Scholar 

  • Gabrel V, Murat C, Thiele A (2014) Recent advances in robust optimization: an overview. Eur J Oper Res 235(3):471–483

    Article  Google Scholar 

  • Gano SE, Renaud JE, Martin JD, Simpson TW (2006) Update strategies for Kriging models for using in variable fidelity optimization. Struct Multidiscip Optim 32(4):287–298

    Article  Google Scholar 

  • Gan G, Lin XS (2015) Valuation of large variable annuity portfolios under nested simulation: a functional data approach. Insurance: Math Econ 62:138–150

    Google Scholar 

  • Gill PE, Murray W, Wright MH (2000) Practical optimization, 12th edn. Academic, London

    Google Scholar 

  • Ginsbourger D, Le Riche R, Carraro L (2010) Kriging is well-suited to parallelize optimization. In: Tenne Y, Goh C-K (eds) Computational intelligence in expensive optimization problems. Springer, Berlin, pp 131–162

    Chapter  Google Scholar 

  • Gordy MB, Juneja S (2010) Nested simulation in portfolio risk measurement. Manag Sci 56(11):1833–1848

    Article  Google Scholar 

  • Gorissen D (2010) Grid-enabled adaptive surrogate modeling for computer aided engineering. Ph. D. dissertation Ghent University, Ghent, Belgium

    Google Scholar 

  • Gosavi A (2015) Simulation-based optimization: parametric optimization techniques and reinforcement learning, 2nd edn. Springer, Boston

    Google Scholar 

  • Gramacy RB, Gray GA, Le Digabel S, Lee HKH, Ranjan P, Wells G, Wild SM (2015) Modeling an augmented Lagrangian for blackbox constrained optimization. Technometrics (in press)

    Google Scholar 

  • Gramacy RB, Lee HKH (2010) Optimization under unknown constraints. Bayesian Stat 9:1–18

    Google Scholar 

  • Grubler A, Ermoliev Y, and Kryazhimskiy A (2015) Coping with uncertainties examples of modeling approaches at IIASA. Technological Forecasting and Social Change (in press)

    Google Scholar 

  • Hamarat C, Kwakkel JH, Pruyt E, Loonen ET (2014) An exploratory approach for adaptive policymaking by using multi-objective robust optimization. Simul Model Pract Theory 46:25–39

    Article  Google Scholar 

  • Homem-de-Mello T, Bayraksan G (2014) Monte Carlo sampling-based methods for stochastic optimization. Surv Oper Res Manag Sci 19(1): 56–85

    Google Scholar 

  • Hong LJ, Luo J, Nelson BL (2015) Chance constrained selection of the best. INFORMS J Comput 27(2):317–334

    Article  Google Scholar 

  • Hu Z, Cao J, Hong LJ (2012) Robust simulation of global warming policies using the DICE model. Manag Sci 58(12):2190–2206

    Article  Google Scholar 

  • Huang D, Allen TT, Notz W, Zheng N (2006) Global optimization of stochastic black-box systems via sequential Kriging meta-models. J Glob Optim 34:441–466

    Article  Google Scholar 

  • Huang Y, Hsieh C-Y (2014) Influence analysis in response surface methodology. J Stat Plan Inference 147:188–203

    Article  Google Scholar 

  • Huerta A, Elizondo M (2014) Analysis of scientific collaboration patterns in co-authorship network of simulation-optimization of supply chains. Simul Model Pract Theory 46:135–148

    Article  Google Scholar 

  • Jala M, Lévy-Leduc C, Moulines É, Conil E, Wiart J (2014) Sequential design of computer experiments for the assessment of fetus exposure to electromagnetic fields. Technometrics (in press)

    Google Scholar 

  • Jalali H, van Nieuwenhuyse I (2014) Evaluation of Kriging-based methods for simulation optimization with homogeneous noise. In: Tolk A, Diallo SY, Ryzhov IO, Yilmaz L, Buckley S, Miller JA (eds) Proceedings of the 2014 winter simulation conference, pp 4057–4058

    Google Scholar 

  • Jalali H, Van Nieuwenhuyse I (2015, accepted) Simulation optimization in inventory replenishment: a classification. IIE Trans

    Google Scholar 

  • Janusevskis J, Le Riche R (2013) Simultaneous kriging-based estimation and optimization of mean response. J Glob Optim 55(2):313–336

    Article  Google Scholar 

  • Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13:455–492

    Article  Google Scholar 

  • Joshi S, Sherali HD, Tew JD (1998) An enhanced response surface methodology (RSM) algorithm using gradient deflection and second-order search strategies. Comput Oper Res 25(7/8):531–541

    Article  Google Scholar 

  • Kamiński B (2015) A method for updating of stochastic Kriging meta- models. Eur J Oper Res (accepted)

    Google Scholar 

  • Kasaie P, Kelton WD (2013) Simulation optimization for allocation of epidemic-control resources. IIE Trans Healthc Syst Eng 3(2):78–93

    Article  Google Scholar 

  • Kasaie P, Vaghefi A, Naieni G (2009) Optimal resource allocation for control of epidemics: an agent based simulation approach. Working Paper, Dept. of Industrial Engineering, Iran University of Science & Technology, Tehran, 16844, Iran

    Google Scholar 

  • Kelton WD, Sadowski RP, Sturrock DT (2007) Simulation with Arena, 4th edn. McGraw-Hill, Boston

    Google Scholar 

  • Kenett R, Steinberg D (2006) New frontiers in design of experiments. Qual Progress 61–65

    Google Scholar 

  • Khuri AI, Mukhopadhyay S (2010) Response surface methodology. Wiley Interdiscip Rev Comput Stat 2:128–149

    Article  Google Scholar 

  • Kleijnen JPC (1975) Statistical techniques in simulation, part II. Dekker, New York

    Google Scholar 

  • Kleijnen JPC (1993) Simulation and optimization in production planning: a case study. Decis Support Syst 9:269–280

    Article  Google Scholar 

  • Kleijnen JPC (2008) Design and analysis of simulation experiments. Springer, New York

    Google Scholar 

  • Kleijnen JPC (2014) Response surface methodology. In: Fu MC (ed) Handbook of simulation optimization. Springer, New York

    Google Scholar 

  • Kleijnen JPC, Den Hertog D, Angün E (2004) Response surface methodology’s steepest ascent and step size revisited. Eur J Oper Res 159:121–131

    Article  Google Scholar 

  • Kleijnen JPC, Den Hertog D, Angün E (2006) Response surface methodology’s steepest ascent and step size revisited: correction. Eur J Oper Res 170:664–666

    Article  Google Scholar 

  • Kleijnen JPC, Gaury EGA (2003) Short-term robustness of production-management systems: a case study. Eur J Oper Res 148(2):452–465

    Article  Google Scholar 

  • Kleijnen JPC, Mehdad E (2013) Conditional simulation for efficient global optimization. In: Proceedings of the 2013 winter simulation conference, Washington, pp 969–979

    Google Scholar 

  • Kleijnen JPC, Pierreval H, Zhang J (2011) Methodology for determining the acceptability of system designs in uncertain environments. Eur J Oper Res 209(2):176–183

    Article  Google Scholar 

  • Kleijnen JPC, Sargent RG (2000) A methodology for the fitting and validation of metamodels in simulation. Eur J Oper Res 120(1):14–29

    Article  Google Scholar 

  • Kleijnen JPC, Van Beers WCM, van Nieuwenhuyse I (2010) Constrained optimization in simulation: a novel approach. Eur J Oper Res 202: 164–174

    Article  Google Scholar 

  • Kleijnen JPC, Van Beers W, Van Nieuwenhuyse I (2012) Expected improvement in efficient global optimization through bootstrapped Kriging. J Glob Optim 54:59–73

    Article  Google Scholar 

  • Kleijnen JPC, Wan J (2007) Optimization of simulated systems: OptQuest and alternatives. Simul Model Pract Theory 15:354–362

    Article  Google Scholar 

  • Koch P, Wagner T, Emmerich MTM, Bäck T, Konen W (2015) Efficient multi-criteria optimization on noisy machine learning problems. Appl Soft Comput (in press)

    Google Scholar 

  • Kolaiti E, Koukouvinos C (2006) On the use of three level orthogonal arrays in robust parameter design. Stat Probab Lett 76(3):266–273

    Article  Google Scholar 

  • Koziel S, Bekasiewicz A, Couckuyt I, Dhaene T (2014) Efficient multi-objective simulation-driven antenna design using co-Kriging. IEEE Trans Antennas Propag 62(11):5901–5915

    Article  Google Scholar 

  • Lan H, Nelson BL, Staum J (2010) A confidence interval procedure for expected shortfall risk measurement via two-level simulation. Oper Res 58(5):1481–1490

    Article  Google Scholar 

  • Law AM (2015) Simulation modeling and analysis, 5th edn. McGraw-Hill, Boston

    Google Scholar 

  • Lee KH, Park GJ (2006) A global robust optimization using Kriging based approximation model. J Jpn Soc Mech Eng 49:779–788

    Google Scholar 

  • Lee LH, Chew EP, Frazier PI, Jia Q-S, Chen C-H (2013) Foreword: advances in simulation optimization and its applications. IIE Trans 45(7):683–684

    Article  Google Scholar 

  • Lee S, Nelson BL (2014) Bootstrap ranking & selection revisited. In: Tolk A, Diallo SY, Ryzhov IO, Yilmaz L, Buckley S, Miller JA (eds) Proceedings of the 2014 winter simulation conference, Savannah, pp 3857–3868

    Google Scholar 

  • Leijen MCF (2011) Response surface methodology for simulation optimization of a packaging line. Master’s thesis, Eindhoven University of Technology, Department of Mechanical Engineering, Systems Engineering Group, Eindhoven

    Google Scholar 

  • Mahdavi I, Shirazi B, Solimanpur M (2010) Development of a simulation-based decision support system for controlling stochastic flexible job shop manufacturing systems. Simul Model Pract Theory 18:768–786

    Article  Google Scholar 

  • Marzat J, Walter E, Piet-Lahanie H (2013) Worst-case global optimization of black-box functions through Kriging and relaxation. J Glob Optim 55:707–727

    Article  Google Scholar 

  • Mehdad E, Kleijnen JPC (2015) Classic Kriging versus Kriging with bootstrapping or conditional simulation: classic Kriging’s robust confidence intervals and optimization. J Oper Res Soc (in press)

    Google Scholar 

  • Mehdad E, Kleijnen JPC (2014) Global optimization for black-box simulation through sequential intrinsic Kriging. CentER Discussion Paper 2014-063, Tilburg University, Tilburg, Netherlands

    Google Scholar 

  • Meloni C, Dellino G (eds) (2015) Uncertainty management in simulation-optimization of complex systems; algorithms and applications. Springer

    Google Scholar 

  • Miller GA (1956) The magical number seven plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81–97

    Article  Google Scholar 

  • Montevechi JAB, de Almeida Filho RG, Paiva AP, Costa RFS, and A.L. Medeiros (2010) Sensitivity analysis in discrete-event simulation using fractional factorial designs. J Simul 4(2):128–142

    Article  Google Scholar 

  • Morales-Enciso S and Branke J (2015) Tracking global optima in dynamic environments with efficient global optimization. Eur J Oper Res 242(3):744–755

    Article  Google Scholar 

  • Müller J, Shoemaker CA (2014) Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems. J Glob Optim 60(2):123–144

    Article  Google Scholar 

  • Myers RH, Khuri AI, Carter WH (1989) Response surface methodology: 1966–1988. Technometrics 31(2):137–157

    Google Scholar 

  • Myers RH, Montgomery DC, Anderson-Cook CM (2009) Response surface methodology: process and product optimization using designed experiments, 3rd edn. Wiley, New York

    Google Scholar 

  • Nair VN (ed) (1992) Taguchi’s parameter design: a panel discussion. Technometrics 34(2):127–161

    Google Scholar 

  • Nakayama H, Yun Y, Yoon M (2009) Sequential approximate multiobjective optimization using computational intelligence. Springer, Berlin, pp 133–141

    Google Scholar 

  • Ng SH, Xu K, Wong WK (2007) Optimization of multiple response surfaces with secondary constraints for improving a radiography inspection process. Qual Eng 19(1):53–65

    Article  Google Scholar 

  • Oden JT (2006) Revolutionizing engineering science through simulation. National Science Foundation (NSF), Blue Ribbon Panel on Simulation-Based Engineering Science

    Google Scholar 

  • Park G-J, Lee T-H, Lee KH, Hwang K-H (2006) Robust design: an overview. AIAA J 44(1):181–191

    Article  Google Scholar 

  • Pasupathy R, Ghosh S (2014) Simulation optimization: a concise overview and implementation guide. INFORMS Tutorials in Operations Research, pp 122–150. http://pubsonline.informs.org/doi/book/10.1287/educ.2014#Chapters

  • Picheny V, Ginsbourger D, Richet Y, Caplin G (2013a) Quantile-based optimization of noisy computer experiments with tunable precision (including comments and rejoinder). Technometrics 55(1):1–36

    Article  Google Scholar 

  • Picheny V, Wagner T, Ginsbourger D (2013b) A benchmark of kriging-based infill criteria for noisy optimization. Struct Multidiscip Optim 48:607–626

    Article  Google Scholar 

  • Preuss M, Wagner T, Ginsbourger D (2012) High-dimensional model-based optimization based on noisy evaluations of computer games. In: Hamadi Y, Schoenauer M (eds) Learning and intelligent optimization: 6th international conference (LION 6), Paris. Springer, Berlin, pp 145–159

    Google Scholar 

  • Quan N, Yin J, Ng SH, Lee LH (2013), Simulation optimization via kriging: a sequential search using expected improvement with computing budget constraints. IIE Trans 45:763–780

    Article  Google Scholar 

  • Qu H, Ryzhov IO, Fu MC, Ding Z (2015) Sequential selection with unknown correlation structures. Oper Res 63(4):931–948

    Article  Google Scholar 

  • Rashid K, Bailey, WJ Couet B, and Wilkinson D (2013) An efficient procedure for expensive reservoir-simulation optimization under uncertainty. SPE Econ Manage 5(4):21–33

    Google Scholar 

  • Razavi S, Tolson BA, Burn DH (2012) Review of surrogate modeling in water resources. Water Resour Res 48, W07401:1–322

    Google Scholar 

  • Regis RG (2014) Locally-biased efficient global optimization using Kriging metamodels Working paper, Department of Mathematics, Saint Joseph’s University, Philadelphia

    Google Scholar 

  • Rikards R, Auzins J (2002) Response surface method for solution of structural identification problems. In: Fourth international conference on inverse problems in engineering, Rio de Janeiro

    Google Scholar 

  • Rosen SC, Harmonosky CM, Traband MT (2008) Optimization of systems with multiple performance measures via simulation: survey and recommendations. Comput Ind Eng 54(2):327–339

    Article  Google Scholar 

  • Roustant O, Ginsbourger D, Deville Y (2012) DiceKriging, DiceOptim: two R packages for the analysis of computer experiments by Kriging-based metamodeling and optimization. J Stat Softw 51(1):1–55

    Article  Google Scholar 

  • Safizadeh MH (2002) Minimizing the bias and variance of the gradient estimate in RSM simulation studies. Eur J Oper Res 136(1):121–135

    Article  Google Scholar 

  • SakallıÜS, Baykoç ÖF (2011) An optimization approach for brass casting blending problem under aletory and epistemic uncertainties. Int J Prod Econ 133(2):708–718

    Article  Google Scholar 

  • Salemi P, Nelson BL, Staum J (2014) Discrete optimization via simulation using Gaussian Markov random fields. In: Tolk A, Diallo SY, Ryzhov IO, Yilmaz L, Buckley S, Miller JA (eds) Proceedings of the 2014 winter simulation conference, Savannah, pp 3809–3820

    Google Scholar 

  • Saltelli A, Ratto M, Tarantola S, Campolongo F (2005) Sensitivity analysis of chemical models. Chem Rev 105(7):2811–2827

    Article  Google Scholar 

  • Samuelson D (2010) When close is better than optimal: combining simulation and stochastic optimization for better risk management. OR/MS Today 37(6):38–41

    Google Scholar 

  • Santos MI, Santos PM (2011) Construction and validation of distribution-based regression simulation metamodels. J Oper Res Soc 62:1376–1384

    Article  Google Scholar 

  • Sasena MJ, Papalambros P, Goovaerts P (2002) Exploration of metamodeling sampling criteria for constrained global optimization. Eng Optim 34(3):263–278

    Article  Google Scholar 

  • Scott W, Frazier P, Powell W (2011) The correlated knowledge gradient for simulation optimization of continuous parameters using Gaussian process regression. SIAM J Optim 21(3):996–1026

    Article  Google Scholar 

  • Scott WR, Powell WB, Simao HP (2010) Calibrating simulation models using the knowledge gradient with continuous parameters. In: Proceedings of the 2010 winter simulation conference, Baltimore, pp 1099–1109

    Google Scholar 

  • Shang JS, Li S, Tadikamalla P (2004) Operational design of a supply chain system using the Taguchi method, response surface methodology, simulation, and optimization. Int J Prod Res 42(18):3823–3849

    Article  Google Scholar 

  • Shahraki AF, Noorossana R (2014) Reliability-based robust design optimization: a general methodology using genetic algorithm. Comput Ind Eng 74:199–207

    Article  Google Scholar 

  • Shi W (2011) Design of pre-enhanced cross-docking distribution center under supply uncertainty: RSM robust optimization method. Working Paper, Huazhong University of Science & Technology, China

    Google Scholar 

  • Shi W, Shang J, Liu Z, Zuo X (2014) Optimal design of the auto parts supply chain for JIT operations: sequential bifurcation factor screening and multi-response surface methodology. Eur J Oper Res 236(2):664–676

    Article  Google Scholar 

  • Shin S, Samanlioglu F, Cho BR, Wiecek MM (2011) Computing trade-offs in robust design: perspectives of the mean squared error. Comput Ind Eng 60(2):248–255

    Article  Google Scholar 

  • Simar L, Wilson PW (1998) Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models. Manag Sci 44(1):49–61

    Article  Google Scholar 

  • Simon HA (1956) Rational choice and the structure of the environment. Psychol Rev 63(2):129–138

    Article  Google Scholar 

  • Simpson TW, Booker AJ, Ghosh D, Giunta AA, Koch PN, Yang R-J (2004) Approximation methods in multidisciplinary analysis and optimization: a panel discussion. Struct Multidiscip Optim 27(5):302–313

    Google Scholar 

  • Stinstra E, den Hertog D (2008) Robust optimization using computer experiments. Eur J Oper Res 191(3):816–837

    Article  Google Scholar 

  • Sun L, Hong LJ, Hu Z (2014) Balancing exploitation and exploration in discrete optimization via simulation through a Gaussian process-based search. Oper Res 62(6):1416–1438

    Article  Google Scholar 

  • Sun Y, Apley DW, Staum J (2011) Efficient nested simulation for estimating the variance of a conditional expectation. Oper Res 59(4):998–1007

    Article  Google Scholar 

  • Svenson JD, Santner TJ (2010) Multiobjective optimization of expensive black-box functions via expected maximin improvement. The Ohio State University, Columbus, Ohio

    Google Scholar 

  • Taddy MA, Lee HKH, Gray GA, Griffin JD (2009) Bayesian guided pattern search for robust local optimization. Technometrics 5(4):389–401

    Article  Google Scholar 

  • Taguchi G (1987) System of experimental designs, vols 1 and 2. UNIPUB/ Krauss International, White Plains

    Google Scholar 

  • Tajbakhsh S, del Castillo E, Rosenberger JL (2013) A fully Bayesian approach to the efficient global optimization algorithm. Working Paper, Pennsylvania State University

    Google Scholar 

  • Tan MHY (2014a) Robust parameter design with computer experiments using orthonormal polynomials. Technometrics (in press)

    Google Scholar 

  • Tan MHY (2014b) Stochastic polynomial interpolation for uncertainty quantification with computer experiments. Technometrics (in press)

    Google Scholar 

  • Tenne Y, Goh C-K (eds) (2010) Computational intelligence in expensive optimization problems. Springer, Berlin

    Google Scholar 

  • Tong C, Sun Z, Zhao Q, Wang Q, Wang S (2015) A hybrid algorithm for reliability analysis combining Kriging and subset simulation importance sampling. J Mech Sci Technol 29(8):3183–3193

    Article  Google Scholar 

  • Ur Rehman S, Langelaar M, van Keulen F (2014) Efficient Kriging-based robust optimization of unconstrained problems. J Comput Sci (in press)

    Google Scholar 

  • Van den Bogaard W, Kleijnen JPC (1977) Minimizing waiting times using priority classes: a case study in response surface methodology. Discussion Paper FEW 77.056. http://arno.uvt.nl/show.cgi?fid=105001. Accessed 12 Mar 2014

  • Van der Herten J, Couckuyt I, Deschrijver D, Dhaene T (2015) A fuzzy hybrid sequential design strategy for global surrogate modeling of high-dimensional computer experiments. SIAM J Sci Comput 37(2):A1020–A1039

    Article  Google Scholar 

  • Vazquez E, Bect J (2010) Convergence properties of the expected improvement algorithm with fixed mean and covariance functions. J Stat Plan Inference 140(11):3088–3095

    Article  Google Scholar 

  • Viana FAC, Haftka RT, Watson LT (2013) Efficient global optimization algorithm assisted by multiple surrogate techniques. J Glob Optim 56(2):669–689

    Article  Google Scholar 

  • Villemonteix J, Vazquez E, Sidorkiewicz M, Walter E (2009a) Global optimization of expensive-to-evaluate functions: an empirical comparison of two sampling criteria. J Glob Optim 43:373–389

    Article  Google Scholar 

  • Villemonteix J, Vazquez E, Walter E (2009b) An informational approach to the global optimization of expensive-to-evaluate functions. J Glob Optim 44(4):509–534

    Article  Google Scholar 

  • Wagner T (2013) Planning and multi-objective optimization of manufacturing processes by means of empirical surrogate models. Doctoral dissertation, Technische Universität Dortmund, Dortmund, Germany

    Google Scholar 

  • Wan J, Li L (2008) Simulation for constrained optimization of inventory system by using Arena and OptQuest. In: 2008 international conference on computer science and software engineering (CSSE 2008). IEEE, Wakefield, MA, pp 202–205

    Google Scholar 

  • Wiebenga JH (2014) Robust design and optimization of forming processes. Ph.D. thesis, University of Twente, Enschede, Netherlands

    Google Scholar 

  • Wiesemann W, Kuhn D, Sim M (2014) Distributionally robust convex optimization. Oper Res (in press)

    Google Scholar 

  • Williams BJ, Santner TJ, Notz WI, Lehman JS (2010) Sequential design of computer experiments for constrained optimization. In: Kneib T, Tutz G (eds) Festschrift for Ludwig Fahrmeir, Springer, Berlin, pp 449–471

    Google Scholar 

  • Wu CFJ, Hamada M (2009) Experiments; planning, analysis, and parameter design optimization, 2nd edn. Wiley, New York

    Google Scholar 

  • Xu J, Huang E, Chen C-H, Lee LH (2015) Simulation optimization: a review and exploration in the new era of cloud computing and big data. Asia Pacific J Oper Res (in press)

    Google Scholar 

  • Yalçinkaya, Ö, Bayhan GM (2009) Modelling and optimization of average travel time for a metro line by simulation and response surface methodology. Eur J Oper Res 196(1):225–233

    Article  Google Scholar 

  • Yanikoğlu İ, den Hertog D, and Kleijnen JPC (2015), Robust dual response optimization. IIE Trans (in press)

    Google Scholar 

  • Yarotsky D (2013) Examples of inconsistency in optimization by expected improvement. J Glob Optim 56, pp. 1773–1790

    Article  Google Scholar 

  • Yin H, Fang H, Xiao Y, Wen G, Qing Q (2015) Multi-objective robust optimization of foam-filled tapered multi-cell thin-walled structures. Struct Multidiscip Optim (in press)

    Google Scholar 

  • Ye W, You F (2015) A fast simulation-based optimization method for inventory control of general supply chain networks under uncertainty American control conference, Palmer House Hilton, Chicago, 1–3 July 2015, pp 2001–2006

    Google Scholar 

  • Zazanis MA, Suri R (1993) Convergence rates of finite-difference sensitivity estimates for stochastic systems. Oper Res 41(4):694–703

    Article  Google Scholar 

  • Zhang J, Ma Y (2015) Stochastic Kriging-assisted multi-objective simulation optimization and uncertainty analysis. Simul: Trans Soc Model Simul Int (in press)

    Google Scholar 

  • Zhou E, Bhatnagar S, Chen X (2014) Simulation optimization via gradient-based stochastic search. In: Tolk A, Diallo SY, Ryzhov IO, Yilmaz L, Buckley S, Miller JA (eds) Proceedings of the 2014 winter simulation conference, Savannah, pp 3869–3879

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management, Tilburg University, Tilburg, The Netherlands

    Jack P. C. Kleijnen

Authors
  1. Jack P. C. Kleijnen
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Kleijnen, J.P.C. (2015). Simulation Optimization. In: Design and Analysis of Simulation Experiments. International Series in Operations Research & Management Science, vol 230. Springer, Cham. https://doi.org/10.1007/978-3-319-18087-8_6

Download citation

Publish with us

Policies and ethics

Search

Navigation