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Although discrete-event simulation has been widely used in various engineering fields, its efficiency remains an issue. Ranking and selection (R&#x0026;S) procedures can solve this efficiency problem by allocating a limited simulation budget intelligently. While the existing R&#x0026;S procedures mostly aim to find only the best simulation input design, practitioners sometimes require the worst design as well to analyze systems requiring high reliability, such as military systems, municipal waste management, etc. Motivated by these practical needs, we propose a simulation budget allocation procedure for selecting both extreme designs simultaneously in the presence of large stochastic noise. To maximize the accuracy of the selections under a limited budget, the proposed procedure sequentially allocates a small budget and updates the simulation results such that they can be used as significant evidence for the correct selections. Our experimental results on benchmark and practical problems demonstrate improved efficiency compared to previous works. It is expected that the proposed procedure will be effectively utilized in the fields of the fourth industrial revolution, such as digital twins that demand quickly finding both extreme designs to maintain synchronization with the corresponding real systems.

A Simulation Budget Allocation Procedure for Finding Both Extreme Designs Simultaneously in Discrete-Event Simulation