A Discrete-Event Simulation Model for Seaport OperationsNevins, Michael R.; Macal, Charles M.; Joines, Joseph C.
doi: 10.1177/003754979807000401pmid: N/A
Discrete-event simulation has been used to assess detailed processes at seaports. Seaport operations are labor-intensive and require extensive use of personnel and machinery. The ability to conduct seaport operations efficiently can be improved significantly through proper utilization of seaport assets. The simulation described has been written in MODSIM II andaddresses seaport operations in the context of military mobility. The simulation allows for multiple cargo types as well as multiple ship types. The overall goal of the simulation has been to determine seaport throughput capability.
Generating Matrix Exponential Random VariatesBrown, E.; Place, J.; Van de Liefvoort, A.
doi: 10.1177/003754979807000402pmid: N/A
In this paper we present a technique for generating random variates from an empirical distribution using the matrix exponential representation of the distribution. In our experience, a matrix exponential representation of an empirical distribution produces random variates with an excellent fit with the empirical distribution. This technique is particularly important when the empirical data is very bursty, i.e., has a high variance. In this paper we discuss how to find the matrix exponential representation of an empirical distribution and we present our technique for generating random variates from the empirical distribution using its matrix exponential representation. We show how the matrix exponential representation of an empirical distribution is found through an example and then we show that matrix exponential random variates are an excellent fit with the empirical data through an χ2 goodness-of-fit test.
Generating Large Data Sets for Simulation of Electronics ManufacturingPing Zhang, ; Pick, James B.
doi: 10.1177/003754979807000403pmid: N/A
Very often the data sets needed for large-scale system simulation and testing aren't available. Even when it's possible to collect and use the real-world data, they're not always suitable. In some situations, only a small portion of the data sets is actually needed for system testing. In others, the sets may involve many data variables and extensive data elements in each data variable, creating high complexity and difficulty. This is especially true in manufac turing production planning, where many fac tors must be considered, and the scope of the data sets is often very large. Here we introduce the procedure and methods we developed for generating large data sets in manufacturing using Monte Carlo techniques combined with the Extended Entity Relationship modeling method. We introduce an approach that can deal with complicated relationships and order ing among random variates. We generate the data sets for an IBM electronics manufactur ing facility. We examine use of the sets to test an information visualization system for pro duction planning. We discuss the goals of ran dom sample generation and the verification of the generation of the random variates.