Edwin Valentin

An evaluation and selection methodology for discrete-event simulation software

For large international companies with their own simulation team it is often hard to select new discrete event simulation software. Often, preferences and application areas between countries differ, and simulation software already in use influences the outcome of the selection process. Available selection methods do not suffice in such cases. Therefore, a two-phase evaluation and selection methodology is proposed. Phase one quickly reduces the long-list to a short-list of packages. Phase two matches the requirements of the company with the features of the simulation package in detail. Different methods are used for a detailed evaluation of each package. Simulation software vendors participate in both phases. The approach was tested for the Accenture world-wide simulation team. After the study, we can conclude that the methodology was effective in terms of quality and efficient in terms of time. It can easily be applied for other large organizations with a team of simulation specialists.

Modeling emergency care in hospitals: a paradox - the patient should not drive the process

The objective in the creation of domain specific discrete event simulation environments is to facilitate model development in the chosen domain. In the creation of such environments, there has been a tendency to adopt a factory based world view. In this paper, we describe an approach to the creation of a generic modeling environment in the healthcare domain that breaks away from the conventional entity driven request for resource. Our approach has enabled us to create models of emergency care in four UK NHS hospitals that reflect more realistically the way emergency care is actually delivered. It appears, paradoxically, that in simulating emergency care, it is best if the patient does not come first

Simulation building blocks for airport terminal modeling

Airports are an ideal application area for simulation. The processes are in a continuous state of change, are complex and stochastic, involve many moving objects, and require a good performance that can be measured in several different performance indicators. Within airports, but also between airports, the same kind of questions are answered over and over again. Often, however, new simulation models are built for each question, if possible copying some parts of previous models. Structured reuse of simulation components is rarely seen. This paper shows an approach for airport terminal modeling that departs from the assumption that reusable simulation building blocks can form the core of a powerful airport modeling tool, which is able to answer different questions at airports better and faster than traditional models. The building blocks have been implemented in the commercially available simulation language eM-Plant. Several studies carried out with this library were very successful.

Design guidelines for simulation building blocks

Component based or building block based simulation model development is regularly mentioned as an interesting new development and a potential field of research. Most of the commercial simulation environments offer the users of their software functions to group model constructs and upgrade these to advanced model constructs that the users can use in future simulation studies. Unfortunately, the created model constructs are rarely reused and often stop being used after the first simulation study. In this paper we describe a list of guidelines to consider in the design of building blocks to enhance the reusability and the flexibility of the simulation building block to be used in multiple simulation studies, also by model developers who have not been involved in the design of the building blocks.

A Building Block Approach to Simulation: An Evaluation Using Containers Adrift

A building block approach to simulation uses modules that are easily reusable and therefore speed up the simulation process. The authors assume that this approach can enhance complex decision making between stakeholders on infrastructure planning and design. The authors combined insights from process management and a simulation building block approach into an experimental interactive decision-making procedure and developed a simulation building block tool. The authors tested the procedure and the tool in the game CONTAINERSADRIFT. Evaluation results indicate that the simulation tool is fast and easy to work with and that the combination of simulation building blocks and process management contributes to the quality and process of negotiation and generates mutual understanding.

Guidelines for designing simulation building blocks

Instinctively, it seems better to support decision making by simulation studies carried out with domain specific simulation building blocks, than by simulation studies that start without the knowledge captured in these building blocks. However, only a limited number of project examples using simulation building blocks exist, which showed improved results as a result of the use of building blocks. We identified a number of requirements to overcome the problems in complex simulation studies. We believe that these requirements can be met by using building blocks and by carrying out the simulation studies in a predefined way. First of all a good building block architecture should be developed that supports the complexities in simulation studies. In this paper we will describe a design approach that in our point of view results in a usable set of building blocks. A proof of concept of the design approach and the architecture are given using a case for passenger modeling at airports.

Requirements for domain specific discrete event simulation environments

Domain specific discrete event simulation environments are supposed to enable faster and easier model development and experimentation. Unfortunately, perceived disadvantages from simulation experts hinder the wide application of this technology. We have performed laboratory experiments and simulation studies in two different domains to learn what the difficulty of domain specific simulation environments is. The lessons that we learned from these experiments and simulation studies enabled us to formulate requirements for domain specific simulation environments, for the model constructs in these environments, for the design of these environments and for guidelines for the use of these environments in simulation studies.

Approach for modelling of large maritime infrastructure systems

Simulation studies for large infrastructure systems often consists of a large number of experiments. Performing all experiments, and the required adjustments to simulation models, is time consuming. In addition it is difficult to keep track of all performed experiments and compare the outcome of these experiments. These issues can be clearly identified by observing a simulation study at the port of Tanger which is performed in the traditional way. In this paper, we describe an alternative approach for performing simulation studies regarding large maritime infrastructure systems. This approach includes the use of a domain specific template developed in the simulation environment arena and a database tool that enables creation, evaluation and managing simulation experiments.

Domain specific model constructs in commercial simulation environments

Commercial simulation environments offer model developers the ability to compose simulation models using generic or domain specific model constructs. Most simulation environments even offer the possibility to compose custom extensions to the simulation environment for faster development of simulation models for a specific domain. This paper evaluates the functionalities for usage and development of custom domain specific extensions that 10 commonly used simulation environments provide to model developers. The findings are scored against a set of criteria, showing that currently more than half of the most used simulation environments offer support to model developers regarding domain specific extensions.