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By invitation from the Editor of IIE Transactions, a research forum was established in 1987 to develop and prepare this article. The objective: to write on the directions, needs and challenges for research by the IE community in applying computer and information sciences. The motivation: realizing the major advancements in computer and information sciences in the recent decade and their significant impact on the IE profession, it is vital to examine how IE research activities can respond effectively to current and emerging needs. This article is viewed as a useful contribution to such an examination. Forum members were invited from academia, government and industry based on their experience in and commitment to research in this area. The forum was chaired by Shimon Y. Nof and the members are the co-authors of this article. Forum members communicated and deliberated throughout 1987 and met for a review and planning session during the IIE Conference in Washington, D.C. in May, 1987 (Forum, 1987). The following article summarizes the contributions and recommendations of the forum members, and was reviewed by four guest reviewers. The reviewers, invited by the Editor, have provided comments that have been incorporated into this article. [ABSTRACT FROM AUTHOR]

The use of simulation, as a technique for attacking difficult problems, has increased greatly with the availability of the digital computer. This is illustrated by the large number of references in Shubik's (1960) bibliography[sup 2] and in the large number of studies published since then. Simulation is essentially an extension of a technique known as empirical sampling, or distribution sampling, which has been used in the field of statistics for many years. The limitations of the technique, which are well known to statisticians, are apparently not as well known, or at least not as well recognized, by those using simulation today. The first part of this paper contains an historical survey of distribution sampling as used by statisticians. The material was originally prepared in 1953 and is reproduced here in slightly revised form to bring this history to the attention of present day simulators in order that the lessons that can be learned from this part can more readily be incorporated in the development of methodology today. The second part of this paper discusses the relevance of empirical sampling to the present day state of the art of simulation. The technique of generating random members, developed for empirical sampling can be applied directly to simulation. However in other aspects simulation is more difficult than empirical sampling and here the theory of distribution sampling does not have much to offer. The difficulties are due to lack of independence among time series, non-stationarity of the time series, and the large number of parameters. [ABSTRACT FROM AUTHOR]