Provider and customer expectations of successful simulation projects

It is crucial that simulation projects are successful, but defining what is meant by success is far from straightforward, surprising though this may seem. This paper describes an analysis of interviews with simulation providers and customers. What emerges is (a) a definition of success and (b) that the two parties have different views of what constitutes a successful simulation project. The definition of success is based on a four stage model of changing perceptions and it assumes that success is not a simple binary variable, but can vary considerably throughout the life of a project. This suggests that simulation providers need to be careful in managing the expectations of their customers and in how they deliver the work. It is the authors' belief that this may also be true for Operational Research interventions other than those involving computer simulation.     

Causal study of low stakeholder engagement in healthcare simulation projects

Stakeholder engagement plays a fundamental role in the success of ‘operational research’ initiatives including simulation projects. However, there is little empirical evidence of real engagement in the context of healthcare simulation. This paper principally examines this issue and aims to provide insights into the possible causes. The paper reports on the results of a literature review and 10 field studies within the UK healthcare settings, supplemented with the authors’ experience in order to arrive at an initial list of the causes, which will then be tested through a survey of expert opinions. Twelve primary and 26 secondary causal factors, which received statistically significant level of agreement from the experts, are presented in a fish-bone diagram. The findings indicate that communication gap between simulation and stakeholder groups is the top primary factor contributing the most to the poor stakeholder engagement in healthcare simulation projects, followed by ‘poor management support’, ‘clinician’s high workload’ and ‘failure in producing tangible and quick results’.     

Conceptual modelling and the project process in real simulation projects: a survey of simulation modellers

A survey was used to obtain information on the processes and methods used by simulation experts in real projects. The 102 survey respondents answered questions about their most recent simulation project. This paper presents some of the survey results, focussing mainly on conceptual modelling and the pattern of time allocation to different topics. There are a wide range of findings that include the modellers making changes to the initial conceptual model during subsequent tasks in most of the projects usually by adding complexity, model coding taking on average about twice the time of other topics, and the topics generally occurring in single blocks of time (at the resolution of the survey data collection) but with considerable overlaps. The results give an insight into the way experts approach simulation projects and their problem solving strategies. A potential application is in training novice modellers, particularly in developing ‘craft skills’. The results also provide an empirical basis for further research, especially in conceptual modelling.     

The FDTD simulation for the performance of dispersive cloak devices

In this paper, we defined the scattering energy intensity based on the Poynting vector to quantitatively study the cloak effect of electromagnetic waves in the time domain. The influences of the effective working frequency bands of four kinds of electromagnetic cloak materials, incidence angle of electromagnetic waves and the number of approximately cloak layers on the cloak effect are studied. To the best of our knowledge, this is the first time to use the time domain method to quantitatively study the effective working frequency band and the scattering energy intensity of cloak materials.     

Neural network as a simulation metamodel in economic analysis of risky projects

An artificial neural network (ANN) model for economic analysis of risky projects is presented in this paper. Outputs of conventional simulation models are used as neural network training inputs. The neural network model is then used to predict the potential returns from an investment project having stochastic parameters. The nondeterministic aspects of the project include the initial investment, the magnitude of the rate of return, and the investment period. Backpropagation method is used in the neural network modeling. Sigmoid and hyperbolic tangent functions are used in the learning aspect of the system. Analysis of the outputs of the neural network model indicates that more predictive capability can be achieved by coupling conventional simulation with neural network approaches. The trained network was able to predict simulation output based on the input values with very good accuracy for conditions not in its training set. This allowed an analysis of the future performance of the investment project without having to run additional expensive and time-consuming simulation experiments.     

Local indicators for plurisubharmonic functions

The notion of index, classical in number theory and its calculation by P. Lelong (1997) for plurisubharmonic functions, allows to define an indicator which is applied to the study of the Monge–Ampère operator and a pluricomplex Green function.     

A simulation tool for the performance evaluation of parallel branch and bound algorithms

Parallel computation offers a challenging opportunity to speed up the time consuming enumerative procedures that are necessary to solve hard combinatorial problems. Theoretical analysis of such a parallel branch and bound algorithm is very hard and empirical analysis is not straightforward because the performance of a parallel algorithm cannot be evaluated simply by executing the algorithm on a few parallel systems. Among the difficulties encountered are the noise produced by other users on the system, the limited variation in parallelism (the number of processors in the system is strictly bounded) and the waste of resources involved: most of the time, the outcomes of all computations are already known and the only issue of interest is when these outcomes are produced.We will describe a way to simulate the execution of parallel branch and bound algorithms on arbitrary parallel systems in such a way that the memory and cpu requirements are very reasonable. The use of simulation has only minor consequences for the formulation of the algorithm.     

A modified Hooke and Jeeves algorithm with likelihood ratio performance extrapolation for simulation optimization

The Hooke and Jeeves algorithm (HJ) is a pattern search procedure widely used to optimize non-linear functions that are not necessarily continuous or differentiable. The algorithm performs repeatedly two types of search routines; an exploratory search and a pattern search. The HJ algorithm requires deterministic evaluation of the function being optimized. In this paper we consider situations where the objective function is stochastic and can be evaluated only through Monte Carlo simulation. To overcome the problem of expensive use of function evaluations for Monte Carlo simulation, a likelihood ratio performance extrapolation (LRPE) technique is used. We extrapolate the performance measure for different values of the decision parameters while simulating a single sample path from the underlying system. Our modified Hooke and Jeeves algorithm uses a likelihood ratio performance extrapolation for simulation optimization. Computational results are provided to demonstrate the performance of the proposed modified HJ algorithm.     

Identifying indicators for aspects of care

This is the fourth article on applying the ten-step model for monitoring and evaluation developed by the Joint Commission on Accreditation of Healthcare Organizations.     

On-line control model for network construction projects

An activity-on-arc network project of the PERT type with random activity durations is considered. For each activity, its accomplishment is measured in percentages of the total project. When operated, each activity utilizes resources of a pregiven capacity and no resource reallocation is undertaken in the course of the project's realization. Each activity can be operated at several possible speeds that are subject to random disturbances and correspond to one and the same resource capacity; that is, these speeds depend only on the degree of intensity of the project's realization. For example, in construction projects partial accomplishments are usually measured in percentages of the total project, while different speeds correspond to different hours a day per worker. The number of possible speeds is common to all activities. For each activity, speeds are sorted in ascending order of their average values—namely speeds are indexed. It is assumed that at any moment t>0 activities, in operation at that moment, have to apply speeds of one and the same index that actually determines the project's speed. The progress of the project can be evaluated only via inspection at control points that have to be determined. The project's due date and the chance constraint to meet the deadline are pregiven. An on-line control model is suggested that, at each control point, faces a stochastic optimization problem. Two conflicting objectives are imbedded in the model:(1) to minimize the number of control points, and(2) to minimize the average index of the project's speeds which can be changed only at a control point.At each routine control point, decision-making centers on determining the next control point and the new index of the speeds (for all activities to be operated) up to that point. The model's performance is verified via simulation.The developed on-line control algorithm can be used for various PERT network projects which can be realized with different speeds, including construction projects and R&D projects.     

Business models for public emission reduction projects

This study investigates two business models for public emission reduction projects that promote low-carbon behavior, issue corresponding carbon credits, and facilitate the trade of carbon credits at the individual level. We construct two dynamic game-theoretical models to investigate the project owner's business model selection strategy, optimal operational decisions, and the effectiveness of the two business models in reducing carbon emissions.     

A programming environment for large software projects

Translated from Sistemnoe Programmirovanie i Modeli Issledovaniya Operatsii, pp. 22–27, 1993.     

Scheduling and control,performance measures and discrete event simulation

There are a growing number of finite capacity scheduling tools which are in use and available to industry. To be fully effective, a scheduling system needs to be incorporated within a control methodology which enables shop-floor performance to be analysed and, when necessary, corrective actions to be formulated. Manufacturing system performance, however, is not only dependent upon short-term planning decisions, but is also constrained by the capability for which it is designed. Hence, any control methodology should be based on a consistent set of performance measures and well defined procedures which help to integrate decision making at all levels in the order fulfilment process. The paper examines the distinctive roles of process optimisation and process control in the decision hierarchy; it classifies a set of measures of performance and other system variables according to the functions they serve in manufacturing control, and suggests a hierarchical process control methodology. The role of discrete event simulation techniques in linking decisions on manufacturing system design and production planning/control is also discussed.     

A posteriori error indicators for Maxwell''s equations

In this paper we shall analyze a class of a posteriori error indicators for an electromagnetic scattering problem for Maxwell's equations in the presence of a bounded, inhomogeneous and anisotropic scatterer. Problems of this type arise when computing the interaction of electromagnetic radiation with biological tissue. We briefly recall existence and uniqueness theory associated with this problem. Then we show how a posteriori error indicators can be derived using an adjoint equation approach. The error indicators use both the jump in normal and tangential components of the field across faces in the mesh.