Adaptive protocols for parallel discrete event simulation

This paper reviews issues concerning the design of adaptive protocols for parallel discrete event simulation (PDES). The need for adaptive protocols are motivated in the background of the classical synchronisation problem that has driven much of the research in this field. Traditional conservative and optimistic protocols and their hybrid variants—that form the basis of adaptive protocols—are also discussed. Adaptive synchronisation protocols are reviewed with special reference to their characteristics regarding the aspects of the simulation state that influence the adaptive decision and the control parameters used. Finally, adaptive load management strategies and their relationship to the synchronisation protocol are discussed.     

Mathematical programming time-based decomposition algorithm for discrete event simulation

Mathematical programming has been proposed in the literature as an alternative technique to simulating a special class of Discrete Event Systems. There are several benefits to using mathematical programs for simulation, such as the possibility of performing sensitivity analysis and the ease of better integrating the simulation and optimisation. However, applications are limited by the usually long computational times. This paper proposes a time-based decomposition algorithm that splits the mathematical programming model into a number of submodels that can be solved sequentially to make the mathematical programming approach viable for long running simulations. The number of required submodels is the solution of an optimisation problem that minimises the expected time for solving all of the submodels. In this way, the solution time becomes a linear function of the number of simulated entities.     

Parallel discrete event simulation algorithm for manufacturing supply chains

Parallel discrete event simulation (PDES) is concerned with the distributed execution of large-scale system models on multiple processors. It is an enabler in the implementation of the virtual enterprise concept, integrating semi-autonomous models of production cells, factories, or units of a supply chain. The key issue in PDES is to maintain causality relationships between system events, while maximizing parallelism in their execution. Events can be executed conservatively only when it is safe to do so, sacrificing the extent to which potential parallelism of the system can be exploited. Alternatively, they can be processed optimistically without guarantee of correctness, but incurring the overhead of a rollback to an earlier saved state when causality error is detected. The paper proposes a modified optimistic scheme for distributed simulation of constituent models of a supply chain in manufacturing, which exploits the inherent operating characteristics of its domain.     

A discrete event simulation of the adult trabecular skeleton

A verified, but unvalidated discrete event computer simulation of the adult bone remodeling system is presented. It implements the unit-based theory of adult skeletal remodeling proposed by Frost. The simulation allows a scientist to design trials involving the adult skeleton by inputting the times of administration, and effects (remodeling cycle initiation, activity, lifespan, proliferation) on bone cells of drugs, according to his current understanding of their mechanisms of action. The simulation constantly tracks the contents and age of all bone structural units, updating each every 2.5 days, to reflect ongoing bone resorption, osteoid formation, osteoid mineralization, and the passage of time. In reality, the simulation tabulates the results of remodeling cycles which take place during the trial. The investigator may receive histomorphometric, densitometric, and biochemical data, as indicators of skeletal status, as often as every 2.5 days during the trial, far more frequently than is usually done in real experiments. At the conclusion of a trial, he may plot/review time-related graphs of the interim data. Validation trials of aging, “activation,” and clodronate administration, are presented. This simulation, when validated, could be a cost-saving device because it can increase the chance for success of any adult skeletal experiment by guiding investigators toward taking fewer, better selected measurements. Furthermore, when refined to include color graphics display of skeletal structure, it could be the basis for an educational device. This could help both the medical community and the public by providing a more ready understanding than can currently be had of adult skeletal remodeling, its relationship to adult metabolic bone disease, and the prevention/treatment of adult metabolic bone disease (e.g. osteoporosis).     

Evaluating the quality of online optimization algorithms by discrete event simulation

A key feature of dynamic problems which offer degrees of freedom to the decision maker is the necessity for a goal-oriented decision making routine which is employed every time the logic of the system requires a decision. In this paper, we look at optimization procedures which appear as subroutines in dynamic problems and show how discrete event simulation can be used to assess the quality of algorithms: after establishing a general link between online optimization and discrete event systems, we address performance measurement in dynamic settings and derive a corresponding tool kit. We then analyze several control strategies using the methodologies discussed previously in two real world examples of discrete event simulation models: a manual order picking system and a pickup and delivery service.     

Gradient estimates for the performance of markov chains and discrete event processes

In this paper, an algorithm for the estimation of the gradient of the stationary performance of a Markov chain w.r.t. a real parameter is presented. The method works for discrete and continuous state spaces. A comparison with the efficient score method and extensions to semi-Markov processes and discrete event dynamical systems (DEDS) are made.     

The costs and benefits of bowel cancer service developments using discrete event simulation

Colorectal cancer includes cancerous growths in the colon, rectum and appendix and affects around 30?000 people in England each year. Maximizing health benefits for patients with colorectal cancer requires consideration of costs and outcomes across the whole service. In an era of scarce healthcare resources, there is a need to consider not only whether technologies and services may be considered clinically effective, but also whether they are cost-effective, that is, whether they represent value for money for the health service. Through the development of a whole disease model, it is possible to evaluate the cost-effectiveness of a range of options for service development consistently within a common framework. Discrete event simulation has been used to model the complete colorectal cancer patient pathway from patient presentation through to referral and diagnosis, treatment, follow-up, potential recurrence, treatment of metastases and end-of-life care. This simulation model has been used to examine the potential cost-effectiveness of different options for change across the entire colorectal cancer pathway. This paper provides an empirical demonstration of the potential application of modelling entire disease areas to inform clinical policy and resource allocation decision-making.     

Demand uncertainty in construction supply chains: a discrete event simulation study

The delivery of construction projects is typically an assembly operation involving a high number of subassemblies and materials brought on site by the supply chain. However, although supply chain management in construction has attracted significant attention, paradoxically little focus has been placed on construction supply networks and operations. This paper places emphasis on supply chain operations by looking at the logistics function of construction material suppliers. Specifically, the paper examines the impact of demand uncertainty on supply chain performance in order to assess the capacity of material distribution companies to provide a timely and cost-efficient service to the construction industry. The study adopts a discrete event simulation approach to assess the impact of demand fluctuations on two crucial logistics performance measures; lead time and cost efficiency. The results show that lead times are particularly sensitive to fluctuations under conditions of low demand. The findings also reveal that, although transport is a significant cost element for lower demand levels, higher inventory costs result in a negative exponential relationship between increasing demand and cost efficiency.     

An Evolutionary Algorithm and discrete event simulation for optimizing inspection strategies for multi-stage processes

The problem of determining the optimal inspection strategy for a given multi-stage production process, i.e. the inspection strategy that results in the lowest total inspection cost, while still assuring a required output quality, is modelled as a joint optimization of inspection location, type and inspection limits. A fusion between a discrete event simulation to model the multi-stage process subject to inspection and to calculate the resulting inspection costs, and an Evolutionary Algorithm (EA) to optimize the inspection strategies, is suggested.     

A participative and facilitative conceptual modelling framework for discrete event simulation studies in healthcare

Existing approaches to conceptual modelling (CM) in discrete-event simulation do not formally support the participation of a group of stakeholders. Simulation in healthcare can benefit from stakeholder participation as it makes possible to share multiple views and tacit knowledge from different parts of the system. We put forward a framework tailored to healthcare that supports the interaction of simulation modellers with a group of stakeholders to arrive at a common conceptual model. The framework incorporates two facilitated workshops. It consists of a package including: three key stages and sub-stages; activities and guidance; tools and prescribed outputs. The CM framework is tested in a real case study of an obesity system. The benefits of using this framework in healthcare studies and more widely in simulation are discussed. The paper also considers how the framework meets the CM requirements.     

A discrete event simulation model in the case of managing a software project

The design and development of large-scale software projects is a complex endeavor, often facing problems like cost and schedule overruns as well as low quality. Over the last years the management of software development projects has been recognized as the cornerstone point of seeking improvement and solutions. Simulation modeling of the software project process is gaining interest among academics and practitioners, as a method to tackle the complex questions with which relevant enterprises are confronted. It offers support on several issues, such as defining software product development strategies, decision-making regarding process improvement and training, in a time span ranging from a short portion of the life cycle to long term product evolution, with organization-wide implications. The aim of this work is to implement a model simulating a core part of a software project process, enabling the estimation of several project development details such as delivery times and quality metrics. The purpose of the model is to assist project managers in control and monitoring, but also in identifying the best planning alternatives. The model scope covers a portion of the life cycle of an incremental software development venture.     

Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems

Recent research has demonstrated that ordinal comparison has fast convergence despite the possible presence of large estimation noise in the design of discrete event dynamic systems. In this paper, we address the fundamental problem of characterizing the convergence of ordinal comparison. To achieve this goal, an indicator process is formulated and its properties are examined. For several performance measures frequently used in simulation, the rate of convergence for the indicator process is proven to be exponential for regenerative simulations. Therefore, the fast convergence of ordinal comparison is supported and explained in a rigorous framework. Many performance measures of averaging type have asymptotic normal distributions. The results of this paper show that ordinal comparison converges monotonically in the case of averaging normal random variables. Such monotonicity is useful in simulation planning.The author would like to thank C. G. Cassandras, X. Chao, S. G. Strickland, X. Xie, and the reviewers for their helpful suggestions.     

Enabling better management of patients: discrete event simulation combined with the STAR approach

Squeezed budgets and funding cuts are expected to become a feature of the healthcare landscape in the future, forcing decision makers such as service managers, clinicians and commissioners to find effective ways of allocating scarce resources. This paper discusses the development of a decision support toolkit (DST) that facilitates the improvement of services by identifying cost savings and efficiencies within the pathway of care. With the help of National Health Service and commercial experts, we developed a discrete event simulation model for deep vein thrombosis (DVT) patients and adapted the socio-technical allocation of resources (STAR) approach to answer crucial questions like what sort of interventions should we spend our money on? Where will we get the most value for our investment? How will we explain the choices we have made? The DST enables users to model their own services by working with the DST interface allowing users to specify local DVT services. They can input local estimates, or data of service demands and capacities, thus creating a baseline discrete event simulation model. The user can then compare the baseline with potential changes in the patient pathway in the safety of a virtual environment. By making such changes key decision makers can easily understand the impact on activity, cost, staffing levels, skill-mix, utilisation of resources and, more importantly, it allows them to find the interventions that have the highest benefit to patients and provide best value for money.     

Perturbation analysis of discrete event systems: Concepts,algorithms, and applications

Perturbation analysis of discrete event systems is a technique that provides estimates of performance sensitivities with respect to system parameters by analyzing a single sample path of a discrete event system. This technique saves a great amount of computation in simulation and can be applied to on-line performance optimization of real world systems where changing the values of parameters is infeasible. This paper gives an overview of the main concepts, general methodologies, applications, and further directions of the area.     

Parametric modeling of protein–DNA binding kinetics: A discrete event based simulation approach

To understand the stochastic behavior of biological systems, we adopt an “in silico” stochastic event based simulation methodology that can determine the temporal dynamics of different molecules. The main requirement for this technique is the event execution time models for the different biological functions of the system. This paper presents a parametric model to determine the execution time of one such biological function: protein–DNA binding. This biological function is modeled as a combination of microlevel biological events using a coarse-grained probability measure to estimate the stochastic parameters of the function. Our model considers the actual binding mechanism along with some approximated protein and DNA structural information. We use a collision theory based approach to transform the thermal and concentration gradients of this biological function into their corresponding probability measure. This information theoretic approach significantly removes the complexity of the classical protein sliding along the DNA model, improves the speed of computation and can bypass the speed-stability paradox. This model can produce acceptable estimates of DNA–protein binding time necessary for our event based stochastic system simulator where the higher order (more than second order statistics) uncertainties can be ignored. The results show good correspondence with available experimental estimates. The model depends very little on experimentally generated rate constants and brings the important biological parameters and functions into consideration. We also present some “in silico” results showing the effects of protein–DNA binding on gene expression in prokaryotic cells.     

A simulation optimization method that considers uncertainty and multiple performance measures

Simulation optimization provides a structured approach to system design and configuration when analytical expressions for input/output relationships are unavailable. This research focuses on the development of a new simulation optimization technique applicable to systems having multiple performance measures. The aim of this research is to incorporate a simulation end user’s preference towards risk and uncertainty into the search process for the best decision alternative. Automation of the optimization procedure is a necessity. Therefore, this paper proposes a simulation optimization method that involves a preference model, specifically adapted for decision making with simulation models.     

Using Java to develop discrete event simulations

The rapid development of the Internet and the introduction of Java as a programming language has provided new possibilities for discrete event simulations. This paper surveys some of these developments and discusses the aspects of Java that make it attractive for developing such simulations. It then describes the development of two simulation systems in Java, one a straightforward application of the three-phase approach and the other a system that supports distributed client-server applications. The limitations and advantages of using Java in this way are discussed.     

Assessment of a 7-day turn-around for the reporting of cervical smear results using discrete event simulation

A discrete event simulation (DES) model has been used to analyse options with the potential to facilitate a 7-day turn-around of cervical screening results in England, with the aim of reducing the anxiety experienced by the participants. Detailed information regarding the cervical screening process collected from the NHS Cancer Screening Programme, research papers, current national guidelines and five cytology laboratories in England was used to inform a DES model representing a typical laboratory. A number of options for change were evaluated. The simulation model suggested that it would be feasible to improve the result turn-around times from 95% within 11 weeks to 95% within 2 weeks; and from an average current result turn-around time of around 6 weeks, to over 50% within 7 days. Moreover, the options for change should be cost saving in the long term.