Detecting shifts in the mean of a simulation output process

Journal of the Operational Research Society

Volumn 53, Issue 5, 2002, Pages 559-573

  Robinson, S ^a   Brooks, R J ^b   Lewis, C D ^c  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b LANCASTER UNIVERSITY   (United Kingdom)

^c ASTON UNIVERSITY   (United Kingdom)

Author keywords

Discrete event simulation; Manhattan diagram; Output analysis; Shift of mean; Steady state

Indexed keywords

COMPUTER SIMULATION; HEURISTIC METHODS; MATHEMATICAL MODELS; NUMERICAL ANALYSIS; STATISTICAL METHODS;

DISCRETE EVENT SIMULATION; SHIFTING STEADY STATE;

OPERATIONS RESEARCH;

EID: 0036566513     PISSN: 01605682     EISSN: 14769360     Source Type: Journal    
DOI: 10.1057/palgrave.jors.2601323     Document Type: Article

References (14)

1. Welch PD (1983). The statistical analysis of simulation results. In: Lavenberg S (ed). The Computer Performance Modeling Handbook. Academic Press: New York, pp 268-328.
2. Lewis CD and Yeomans KA (1995). A heuristic search proce¬dure for detecting sudden shifts in stationary time series data. J Opl Res Soc 46: 1471-1480.
3. Law A and Kelton WD (2000). Simulation Modeling and Analysis, 3rd edn. McGraw-Hill: New York.
4. Goldsman D and Schruben L (1990). New confidence interval estimators using standardized time series. Mngt Sci 36: 393-397.
5. Kleijnen JPC (1987). Statistical Tools for Simulation Practi¬tioners. Marcel Dekker: New York.
6. Robinson S (1995). An heuristic technique for selecting the run- length of non-terminating steady-state simulations. Simulation 65: 170-179.
7. Kelton WD (1997). Statistical analysis of simulation output. In: Andradottir S, Healy KJ, Withers DH and Nelson BL (eds). Proceedings of the 1997 Winter Simulation Conference Asso¬ciation of Computing Machinery: New York, pp 23-30.
8. Friedman LW (1986). The analysis of multiple response simula¬tion output data—experiments of comparison. Comput Opns Res 13: 647-652.
9. Balmer DW (1987). Polishing the analysis of the statistical output of comparative simulation experiments. Simulation 49: 123-126.
10. Gleick J (1987). Chaos: Making a New Science. Abacus: London.
11. Billingsley P (1995). Probability and Measure, 3rd edn. Wiley: New York.
12. BS 5703 Parts 1 and 2 (1980). Data Analysis and Quality Control Using CUSUM Techniques. British Standards Institu¬tion: London.
13. Winer BJ (1971). Statistical Principles in Experimental Design. McGraw-Hill: New York.
14. Brooks RJ and Tobias AM (1996). Choosing the best model: level of detail, complexity and model performance. Math Comput Model 24: 1-14.