Constrained sequential-block search in simulation experimentation

ABSTRACT

This paper describes the application of sequential-block search techniques to simulation experimentation with constrained systems. Two basically different approaches are examined. One approach combines designed experiments, multiple regression, and mathematical optimization to predict a constrained optimum solution, which is then checked by further experimentation in the region of the predicted solution. A second approach employs a sequential optimum seeking-technique, such as gradient search or sequential simplex search, modified to accommodate constraints. These techniques are illustrated with a simple inventory system modeled with the GASP-II simulation language. A comparison of the effectiveness of these approaches is presented.

REFERENCES

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INDEX TERMS

Primary Classification:
  F. Theory of Computation
  F.2 ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY
      F.2.2 Nonnumerical Algorithms and Problems
          Subjects: Sequencing and scheduling

Additional Classification:
  F. Theory of Computation
  F.2 ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY
      F.2.2 Nonnumerical Algorithms and Problems
          Subjects: Sorting and searching

  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.4 Model Validation and Analysis

General Terms:
Algorithms, Management, Performance, Theory