Past challenges and the future of discrete event simulation

Abstract

The American scientist Carl Sagan once said: “You have to know the past to understand the present.” We argue that having a meaningful dialogue on the future of simulation requires a baseline understanding of previous discussions on its future. For this paper, we conduct a review of the discrete event simulation (DES) literature that focuses on its future to understand better the path that DES has been following, both in terms of who is using simulation and what directions they think DES should take. Our review involves a qualitative literature review of DES and a quantitative bibliometric analysis of the Modeling and Simulation (M&S) literature. The results from the bibliometric study imply that demographics of the M&S community are rapidly changing, both in terms of the nations that use M&S and the academic disciplines from which new simulationists hail. This change in demographics has the potential to help aid the community face some of its future challenges. Our qualitative literature review indicates that DES still faces some significant challenges: these include integrating human behavior; using simulation for exploration, not replication; determining return on investment; and communication issues across a splitting community.

A Century-Long Challenge in Teletraffic Theory: Blocking Probability Evaluation for Overflow Loss Systems With Mutual Overflow

“…There are two main branches of simulation methodology for the performance evaluation of discrete-state stochastic systems such as OLSs: discrete-event simulation (DES) [51], [52], [53] and Markov-chain simulation (MCS) [54]. In DES, a sorted list of pending events is maintained.…”

Discrete Event Simulation-Based Evaluation of a Single-Lane Synchronized Dual-Traffic Light Intersections

“…This study examined DEVS literature that specifically addresses its prospective advancements [17]. The study combines a quantitative bibliometric analysis of the literature on Modelling and Simulation with a qualitative evaluation of DEVS [5]. The issue of traffic congestion has been widely acknowledged as a significant problem in major urban areas, especially during times of higher usage [16].…”

“…In addition, it is difficult to validate and verify the accuracy of a DEVS model, especially when comparing simulation results with real-world data [4]. Model validation necessitates ensuring that the simulation output corresponds to real-world observations across a broad range of scenarios though this can be resource-intensive and error-prone [5]. Also, it has been observed that simulation execution can be computationally intensive and time-consuming, depending on the complexity of the simulation model and the level of detail included [6].…”