Discrete Event Simulation (DES) has been widely used in modelling health-care systems for many years and a simple citation analysis shows that the number of papers published has increased markedly since 2004. Over the last 30 years several significant reviews of DES papers have been published and we build on these to focus on the most recent era, with an interest in performance modelling within hospitals. As there are few papers that propose or illustrate general approaches, we classify papers according to the areas of application evident in the literature, discussing the apparent lack of genericity. There is considerable diversity in the objectives of reported studies and in the consequent level of detail: We discuss why specificity dominates and why more generic approaches are rare.

/pdf/discrete-event-simulation-for-performance-modelling-in-2pbcbcdgca.pdf

Discrete event simulation for performance modelling in health care: a review of the literature

In this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems found in semiconductor manufacturing systems. We describe batch scheduling problems, parallel machine scheduling problems, job shop scheduling problems, scheduling problems with auxiliary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some of the challenges in scheduling semiconductor manufacturing operations.

A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations

A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems

This chapter reviews the principles of sample average approximation (SAA) for solving simulation optimization problems. We provide an accessible overview of the area and survey interesting recent developments. We explain when one might want to use SAA and when one might expect it to provide good-quality solutions. We also review some of the key theoretical properties of the solutions obtained through SAA. We contrast SAA with stochastic approximation (SA) methods in terms of the computational effort required to obtain solutions of a given quality, explaining why SA “wins” asymptotically. However, an extension of SAA known as retrospective optimization can match the asymptotic convergence rate of SA, at least up to a multiplicative constant.

/pdf/a-guide-to-sample-average-approximation-4n43hf3tl5.pdf

A Guide to Sample Average Approximation

Simulation models allow the user to understand system performance and assist in behavior prediction, to support system diagnostics and design. Iterative optimization methods are often used in conjunction with engineering simulation models to search for designs with desired properties. These optimization methods can be difficult to employ with a discrete-event simulation, due to the stochastic nature of the response(s) and the potentially extensive run times. A metamodel, or model of the simulation model, simplifies the simulation optimization in two ways: the metamodel response is deterministic rather than stochastic, and the run times are generally much shorter than the original simulation. Metamodels based on first- or second-order polynomials generally provide good fit only locally, and so a series of metamodels are fit as the optimization progresses. Other classes of metamodels can provide good global fit; in these cases one can fit a (global) metamodel once, at the start of the optimization, and use it to find a design that will meet the optimality criteria. Both approaches are discussed in this chapter and illustrated with an example.

Chapter 18 Metamodel-Based Simulation Optimization

Proceedings of the 2001 Winter Simulation Conference

The indifference-zone (IZ) formulation of ranking and selection (R&S) is the foundation of many procedures that have been useful for choosing the best among a finite number of simulated alternatives. Of course, simulation models are imperfect representations of reality, which means that a simulation-based decision, such as choosing the best alternative, is subject to model risk. In this paper we explore the impact of model risk due to input uncertainty on IZ R&S. “Input uncertainty” is the result of having estimated (“fit”) the simulation input models to observed real-world data. We find that input uncertainty may force the user to revise, or even abandon, their objectives when employing a R&S procedure, or it may have very little effect on selecting the best system even when the marginal input uncertainty is substantial.

Proceedings of the 2012 winter simulation conference

Shaping future with simulation

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Proceedings of the 2001 Winter Simulation Conference

Proceedings of the 2012 winter simulation conference

Shaping future with simulation