Skip to main content
Log in

Towards logistics systems parameter optimisation through the use of response surfaces

  • Industry
  • Published:
4OR Aims and scope Submit manuscript

Abstract

Logistics systems have to cope with uncertainties in demand, in lead times, in transport times, in availability of resources and in quality. Management decisions have to take these uncertainties into consideration. An evaluation of decisions may be done by means of simulation. However, not all stochastic phenomena are of equal importance. By design of simulation experiments and making use of response surfaces, the most important phenomena are detected and their influence on performance estimated. Once the influence of the phenomena is known, this knowledge may be used to determine the optimal values of some decision parameters. An illustration is given on how to use response surfaces in a real-world case. A model is built in a logistics modelling software. The decision parameters have to be optimised for a specific objective function. Experiments are run to estimate the response surface. The validity of the response surface with few observations is also tested.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Andradottir S (1995) A stochastic approximation algorithm with varying bounds. Opera Res 43(6):1037–1048

    Google Scholar 

  • Andradottir S (1996) A scaled stochastic approximation algorithm. Manage Sci 42:475–498

    Article  Google Scholar 

  • Batmaz I, Tunali S (2003) Small response surface designs for metamodel estimation. Eur J Oper Res 145:455–470

    Article  Google Scholar 

  • Box GEP, Draper NR (1987) Empirical model-building and response surfaces. Wiley, New York

    Google Scholar 

  • Dengiz B, Alabas C (2000) Simulation optimization using tabu search. In: Proceedings of the 2000 winter simulation conference, Orlando, 10–13 December 2000, pp 805–810

  • Goetschalckx M, Vidal CJ, Dogan K (2002) Modeling and design of global logistics systems: a review of integrated strategic and tactical models and design algorithms. Eur J Oper Res 143:1–18

    Article  Google Scholar 

  • Goldsman D, Kim S-H, Marshall WS, Nelson BL (2002) Ranking and selection for steady-state simulation: procedures and perspectives. INFORMS J Comput 14(1):2–19

    Article  Google Scholar 

  • Haddock J, Mittenthal J (1992) Simulation optimization using simulated annealing. Comput Ind Eng 22(4):387–395

    Article  Google Scholar 

  • Ho Y-C, Cassandras CG, Chen C-H, Dai L (2000) Ordinal optimisation and simulation. J Oper Res Soc 51:490–500

    Article  Google Scholar 

  • Hodder JE, Dincer MC (1986) A multifactor model for international plant location and financing under uncertainty. Comput Oper Res 13(5):601–609

    Article  Google Scholar 

  • Hooke R, Jeeves TA (1961) A direct search solution of numerical and statistical problems. J Assoc Comput Mach 8:212–229

    Google Scholar 

  • Jacobson SH, Schruben LW (1989) Techniques for simulation response optimisation. Oper Res Lett 8:1–9

    Article  Google Scholar 

  • Khuri AI, Cornell JA (1988) Response surfaces: designs and analyses. Marcel Dekker Inc, New York

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Olafsson S, Kim J (2002) Simulation optimization. In: Proceedings of the 2002 winter simulation conference, San Diego, 8–11 December 2002, pp 79–84

  • Paul RJ, Chanev TS (1998) Simulation optimisation using a genetic algorithm. Simul Pract Theory 6:601–311

    Article  Google Scholar 

  • Pichitlamken J, Nelson BL (2001) Selection-of-the-best procedures for optimisation via simulation. In: Proceedings of the 2001 winter simulation conference, Arlington, 9–12 December 2001, pp 401–407

  • Ross PJ (1988) Taguchi techniques for quality engineering. McGraw-Hill Inc, New York

    Google Scholar 

  • Safizadeh MH, Thornton BM (1984) Optimization in simulation experiments using response surface methodology. Comput Ind Eng 8(1):11–27

    Article  Google Scholar 

  • Vanmaele H, Van Landeghem R (1995) The integration of optimization paradigms and simulation practice in industrial management. Belg J Oper Res Stat Comput Sci 35(1):43–62

    Google Scholar 

  • Vidal CJ, Goetschalckx M (1996) The role and limitations of quantitative techniques in the strategic design of global logistics systems. School of Industrial and Systems Engineering Research Report 96-023, Georgia Institute of Technology, Atlanta

  • Vidal CJ, Goetschalckx M (1997) Strategic production–distribution models: a critical review with emphasis on global supply chain models. Eur J Oper Res 98:1–18

    Article  Google Scholar 

  • Wardrop DM, Myers RH (1990) Some response surface designs for finding optimal conditions. J Stat Plan Inference 25:7–28

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Katrien Ramaekers.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ramaekers, K., Janssens, G.K. & Van Landeghem, H. Towards logistics systems parameter optimisation through the use of response surfaces. 4OR 4, 331–342 (2006). https://doi.org/10.1007/s10288-006-0024-2

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10288-006-0024-2

Keywords

Navigation