Event Simulation Model Research Articles

Multi-Objective Design Optimization of Flexible Manufacturing Systems Using Design of Simulation Experiments: A Comparative Study

One of the basic components of Industry 4.0 is the design of a flexible manufacturing system (FMS), which involves the choice of parameters to optimize its performance. Discrete event simulation (DES) models allow the user to understand the operation of dynamic and stochastic system performance and to support FMS diagnostics and design. In combination with DES models, optimization methods are often used to search for the optimal designs, which, above all, involve more than one objective function to be optimized simultaneously. These methods are called the multi-objective simulation–optimization (MOSO) method. Numerous MOSO methods have been developed in the literature, which spawned many proposed MOSO methods classifications. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Moreover, previous classifications have been focused on general MOSO methods and rarely related to the specific area of manufacturing design. For this reason, a new conceptual classification of MOSO used in FMS design is proposed. After that, four MOSO methods are selected, according to this classification, and compared through a detailed case study related to the FMS design problem. All of these methods studied are based on Design of Experiments (DoE). Two of them are metamodel-based approaches that integrate Goal Programming (GP) and Desirability Function (DF), respectively. The other two methods are not metamodel-based approaches, which integrate Gray Relational Analysis (GRA) and the VIKOR method, respectively. The comparative results show that the GP and VIKOR methods can result in better optimization than DF and GRA methods. Thus, the use of the simulation metamodel cannot prove its superiority in all situations.

Performance improvement of supplier-side e-tender-driven marketing process

PurposeConsiderable amount of purchases in business-to-business (B2B) markets make through the tendering process. As technology keeps driving B2B procurement, both the supplier/contractor and buyer firms have settled down in their respective roles in the electronic-tendering environment. Researchers have ignored the supplier-side e-tender-driven marketing process that might lead to substantively successful financial performance. The purpose of this study is to improve the performance of an e-tender-driven marketing process of an original equipment manufacturer (OEM) incorporating the stakeholder's inputs.Design/methodology/approachDiscrete event simulation modelling (DESM) has been used as a methodology to model, analyse and improve the process with the involvement of stakeholders at every stage of the study. Different scenarios are analysed to identify the near-optimal scenario based on agreed-upon key performance indicators.FindingsScenario that incorporated man-power sharing and eliminating avoidable activities gives the near-optimal solution for implementation.Research limitations/implicationsThis study highlights that better insights can be gained by adopting the process-oriented view of the marketing–operations interface. Embracing a stakeholder-based consultative approach gives research a more practical outlook and reduces the gap between theory and practice. Suggestions for further research are provided.Practical implicationsB2B organizations, where lines between marketing and operations are blurred, can improve their marketing processes by implementing operations research tools.Originality/valueThis study provides an attempt to improve the performance of a supplier-side e-tender-driven marketing process of an OEM using the DESM methodology incorporating stakeholder's inputs.

Optimizing the Utilization Rate for Electric Power Generation Systems: A Discrete-Event Simulation Model

The problem of measuring the availability of generation units in electric power systems has been addressed in the literature by using a set of analytical equations and Monte Carlo Simulation (MCS). MCS, as a powerful simulation tool, is much easier to use than analytical approaches in measuring the availability of large applications. However, the simulated process using MCS entails deducing the operating state sequences for each component along the simulated time followed by combining all sequences for all components in order to deduce system availability. This simulated process is both lengthy and time-consuming. This paper introduces a new method to measure the availability of generation units and optimize their utilization using Discrete Event Simulation (DES) model. The experimental result of a real case study illustrates the efficiency of using DES.

Estimating manufacturing electricity costs by simulating dependence between production parameters

Abstract The electricity costs associated with manufacturing generally consist of an energy charge and a demand charge, which are calculated by total kWh × $/kWh and peak kW × $/kW, respectively. While these two charges have been extensively studied with production scheduling, dependence between production parameters has not been thoroughly investigated for production energy costs when the production parameters are assumed to be independent. Such dependence, however, can significantly affect the peak kW and resulting energy costs. Therefore, industrial practitioners need a tool to simulate the production power demand and cost to evaluate energy costs for each production plan and for any dependence in the parameters. In this paper, we propose a method for simulating the power demand at the machine and facility levels, focusing on the dependence in milling machine parameters. Using probabilistic techniques with copulas, we quantify the degree of dependence between two milling parameters (processing times and processing amounts) and build a power demand simulator combining discrete event simulation and numerical simulation models. From 35 illustrative examples, we show that a production strategy to adjust dependence can reduce the peak kW and energy costs by more than 15% and 4%, respectively. In addition to dependence, less variability in processing times results in less peak kW and energy costs. Other factors such as a marginal probability distribution and copula types having the same degree of variability have been found to have limited effects on reducing energy costs.

When does economic model type become a decisive factor in health technology appraisals? Learning from the expanding treatment options for relapsing–remitting multiple sclerosis

Objectives: Specific economic model types often become de facto standard for health technology appraisal over time. Markov and discrete event simulation (DES) models were compared to investigate the impact of innovative modeling on the cost-effectiveness of disease-modifying therapies (DMTs) in relapsing–remitting multiple sclerosis (RRMS). Fingolimod was compared to dimethyl fumarate (DMF; in highly active [HA] RRMS), alemtuzumab (in HA RRMS) and natalizumab (in rapidly evolving severe RRMS). Comparator DMTs were chosen to reflect different dosing regimens.Materials and methods: Markov and DES models used have been published previously. Inputs were aligned in all relevant respects, with differences in the modeling of event-triggered attributes, such as relapse-related retreatment, which is inherently difficult with a memoryless Markov approach. Outcomes were compared, with and without different attributes.Results: All results used list prices. For fingolimod and DMF, incremental cost-effectiveness ratios (ICERs) were comparable (Markov: £4206/quality-adjusted life year [QALY] gained versus DES: £3910/QALY gained). Deviations were observed when long-term adverse events (AEs) were incorporated in the DES (Markov: £25,412 saved/QALY lost, versus DES: £34,209 saved/QALY lost, fingolimod versus natalizumab; higher ICERs indicate greater cost-effectiveness). For fingolimod versus alemtuzumab, when relapse-triggered retreatment was included in the DES, large cost differences were observed (difference between incremental cost is £35,410 and QALY is 0.10).Limitations: UK payer perspective, therefore societal approach was not considered. Resource utilization and utilities for both models were not derived from the subpopulations; as the focus is on model type, input limitations that apply to both models are less relevant.Conclusions: Whilst no model can fully represent a disease, a DES allows an opportunity to include features excluded in a Markov structure. A DES may be more suitable for modeling in RRMS for health technology assessment purposes given the complexity of some DMTs. This analysis highlights the capabilities of different model structures to model event-triggered attributes.

A discrete event simulation approach for reserving capacity for emergency patients in the radiology department

BackgroundMany hospitals in China experience large volumes of emergency department (ED) radiology patients, thereby lengthening the wait times for non-emergency radiology patients. We examine whether an emergency reservation policy which deals with stochastic arrivals of ED patients can shorten wait times, and what effect it has on patient and hospital related metrics.MethodsIn this study, operations research models are used to develop an emergency reservation policy. First, we construct a discrete event simulation (DES) model based on the process of patients served by one computed tomography (CT) scanner at West China Hospital (WCH). Next, a newsvendor model is built to compute the daily reservation quantity for emergency patients. Based on the appointment scheduling rule and daily emergency reservation policies, the effects of the proposed policy on daily examination quantity, patient wait times, and equipment utilization are explicitly modeled. Finally, we evaluate the impact of different reservation policies on these system performance measures.ResultsOur analysis indicates that reserving capacity for emergency patients greatly shortens the delay for non-emergency patients with an average 43.9% reduction in total wait times. The pre-model utilization and average post-model utilization are 99.3% and 98.5%, respectively. In addition, the comparison of different reservation policies shows that there is no significant difference between any two policies in terms of patients’ wait times.ConclusionsReserving proper capacity for emergency patients not only positively affects the patients’ delay times, but also affects various aspects of the hospital. Our goal is to design a simple and implementable emergency reservation policy. DES proves to be an effective tool for studying the effects of proposed scenarios to optimize capacity allocation in radiology management.

When has service provision for transient ischaemic attack improved enough? A discrete event simulation economic modelling study

ObjectivesThe aim of this study was to examine the impact of transient ischaemic attack (TIA) service modification in two hospitals on costs and clinical outcomes.DesignDiscrete event simulation model using data...

Моделирование систем арктического морского транспорта: основы междисциплинарного подхода и опыт практических работ

Development of new offshore projects in the Arctic begins with comprehensive analysis of Marine Transport System’s (MTS) performance. At the same time researchers with different professional orientation (shipbuilders, logisticians, economists and managers etc.) are often have narrow view while making their investigations of MTS, focusing mostly at corresponding familiar sides of the system. It leads to specific “disproportional” description of MTS’s when some components considered in detail but other are out of investigation that in some cases may result in inaccurate conclusions. This makes authors sure that the only proper approach to investigate complex arctic MTS should be multidisciplinary. Article contains the description of integrated R&D project fulfilled at State Krylov Research Centre and devoted to realization of multidisciplinary methodology and corresponding software for arctic MTS design and analysis. Integrated software solution is based on object-oriented paradigm and combines such scientific areas as geographic information systems (GIS), shipbuilding disciplines, discrete event and agent-based simulation models. Theoretical base of the solution and its architecture envelops the following subject fields: ship design and fleet sizing, resistance and propulsion of ships including ice-going capabilities, vessel routing in ice, scheduling, downstream and upstream logistics, navigation process simulation, environment conditions stochastic modeling, operation research, queue theory, economy and management. Software has a modular structure with open and expansible architecture, which makes possible to add new functionality and upgrade existing program blocks in accordance with demands of specific project. Simulation model serve as the center of multidisciplinary integration because this technology allows reproducing behavior of different objects of MTS under dynamic conditions. Realized in AnyLogic ® framework simulation model replicates vessel operation process according to individual schedule generated by special planning component. Vessel’s activities simulated in modeled time with consideration of special aspects of operational planning and resolving accidents. Main model also includes specialized sub-models that provide opportunity to describe many local dynamic essences such as processes of offshore drilling and production, freeze-up of channel in fast ice, different ship interaction aspects, “weather windows” in ports etc. Number of instances are also given in the article, they allows to conclude that adequate and consistent model of MTS working in heavy ice-conditions or having complex organization scheme could be obtained only by means of simulation modeling under multidiscipline approach.

Using file system counters in modelling parallel I/O architectures

Keeping compute and I/O performance balanced is a major challenge for future cost-efficient HPC systems. Several architectural concepts and new technologies allow to address this challenge, however at the price of higher complexity. As a result, the need emerges to simulate these architectural concepts and new technologies to predict their impact on the overall performance. In this paper we propose a particular approach to explore the design space using event simulation models that take I/O server-side performance counters as input. In this way large quantities of real-life data measured over a large number of applications can be used to explore architectural modifications. We apply our approach using data collected by a GPFS file system serving a petascale Blue Gene/P installation.

A discrete event simulation to model the cost-utility of fingolimod and natalizumab in rapidly evolving severe relapsing-remitting multiple sclerosis in the UK

Objective: Two disease-modifying therapies are licensed in the EU for use in rapidly-evolving severe (RES) relapsing-remitting multiple sclerosis (RRMS), fingolimod and natalizumab. Here a discrete event simulation (DES) model to analyze the cost-effectiveness of natalizumab and fingolimod in the RES population, from the perspective of the National Health Service (NHS) in the UK, is reported.Methods: A DES model was developed to track individual RES patients, based on Expanded Disability Status Scale scores. Individual patient characteristics were taken from the RES sub-groups of the pivotal trials for fingolimod. Utility data were in line with previous models. Published costs were inflated to NHS cost year 2015. Owing to the confidential patient access scheme (PAS) discount applied to fingolimod in the UK, a range of discount levels were applied to the fingolimod list price, to capture the likelihood of natalizumab being cost-effective in a real-world setting.Results: At the lower National Institute of Health and Care Excellence (NICE) threshold of £20,000/quality-adjusted life year (QALY), fingolimod only required a discount greater than 0.8% of list price to be cost-effective. At the upper threshold of £30,000/QALY employed by the NICE, fingolimod was cost-effective if the confidential discount is greater than 2.5%. Sensitivity analyses conducted using fingolimod list-price showed the model to be most sensitive to changes in the cost of each drug, particularly fingolimod.Conclusions: The DES model shows that only a modest discount to the UK fingolimod list-price is required to make fingolimod a more cost-effective option than natalizumab in RES RRMS.

An early discharge approach for managing hospital capacity

The Kidney and Oncology Departments at Zagazig University Hospital are suffering from increased demand and limited capacity. Arrival patients who find all beds occupied are simply turned away, i.e., no waiting is allowed. This paper investigates the impact of an early discharge approach that can be applied to patients that have been scheduled to discharge within 5 h. A discrete event simulation (DES) model is built using empirical distributions based on real data. The model has been validated against real data and the results have shown that the proposed early discharge approach can reduce the number of turned away patients by 10% in the Kidney Department, equivalent to 182 patients annually and by 11% in the Oncology Department, equivalent to 150 patients annually.

Simulation model of delivering health care to the wounded in a special purpose medical unit at elimination of medical and sanitary consequences of emergency situations

Relevance. The scientific research conducted in the field of medical support at elimination of medical and sanitary consequences of the emergency situations (ES) is inseparably linked with process of modeling of complex systems. Modeling is a study of objects, processes and the phenomena for definition (specification) of the studied characteristics of the surveyed system or specification (improvement) of the offered new elements of this system. Simulation modeling is one option to study medical support system experimentally. Intention. Simulation modeling is necessary for developing respective models of special purpose medical group functioning, describing the main components of health care delivery to the wounded at ES taking into account their medical and evacuation characteristics. Methodology. For experiments, discrete and event simulation models were used which represented the system under study, its states changing with specific events in discrete time points. During research, the behavior of separate elements of the modelled system (a special purpose medical group) is described via Python programming algorithms and using the SimPy library. The following data were used as model parameters: victim in-flow structure with time-frames of various medical procedures and manipulations / surgeries as well as time-frames of non-transportability of victims with various lesions. For model simplification, in-flows of victims with penetrating and non-penetrating wounds of various severity were simulated. For modeling, averaged in-flow structures by severity grade in various ESs were used: 29.1 % – mild, 27.2 % – moderate, 42.1 % – severe and 1.6 % – extremely severe. Time-frames of medical manipulations and procedures, time-frames of nontransportability were described as random variables with triangular distribution (Simpson); its parameters were estimated by military-medical experts (surgeons, therapists). A simple Poisson process with various parameters was used for modeling the victim in-flow. During modeling, group work was assessed with victim in-flow of 40, 60, 80, 100 persons a day. Results and Discussion. Results of modeling indicated some potential bottlenecks of a special purpose medical group, i.e. surgical unit and intensive care unit. It was shown, for example, increased number of surgical teams can enlarge considerably the capacity of a special purpose surgical unit and medical group in general, but at the same time will demand augmentation of capacity of the intensive care unit due to increased flow of the postoperative wounded. Possible effects of such advanced technologies as Celox GauzeR bandage and maintaining wounds with low pressure bandages were studied (vacuum assistant closure – VAC, negative pressure wound therapy – NPWT). Preliminary conclusions were made on appreciable effectiveness of the specified technologies in work of a special purpose medical group. Conclusion. Simulation modeling is one of the most popular types of mathematical modeling and is used for research of complex systems. Therefore, it can be successfully used when modeling work of a special purpose medical group at elimination of medical and sanitary consequences of ESs. The developed simulation model helps analyze the changing workload of the group with various in-flows of wounded as a result of natural, technogenic and social ESs.

The Full Potential of Continuous System Simulation Modelling

Continuous System Simulation (CSS) is a powerful way to study the behaviour of differential-algebraic equation models. Differential-algebraic equation modelling goes back to Newton and works well for models of e.g. physical systems where stochasticity plays almost no role, and where only a single attribute of an object is studied over time. However, when results from a deterministic CSS model are compared with results from a Discrete Event Simulation (DES) model, they are often inconsistent. The reasons behind this CSS-DES inconsistency are nowadays well understood. In this paper, we demonstrate that a CSS model can contain both continuous state variables (compartments) that change continuously over time and discrete state variables (also compartments) that model discrete entities and change by integer amounts. In both cases, the time-slicing method is used to advance time. Furthermore, stochasticity can and should play the same role in CSS as it does in DES. This paper first explains how a well-defined conceptual model can be stepwise transformed in a consistent way into a CSS model. These transformation steps provide insights into how to construct a consistent CSS model. In short, this is about preserving uncertainties, attributes and dynamic properties. An additional benefit of this approach is that continuous and discrete sub-models can interact within the same model, without having to combine different types of simulation languages, types of time handling and incongruent concepts. To facilitate the use and understanding of stochastic CSS, this paper is also intended to serve as a guide to performing consistent CSS modelling and simulation. Furthermore, an open source tool for collecting and analyzing the outputs from a stochastic CSS model and analyzing and presenting the results in statistical form is also developed and made available to the reader.

Operations research applications in hospital operations: Part III

ABSTRACTHospital decision and policy makers are tasked with developing innovative strategies to provide patients with quality healthcare in an effective and efficient manner. Operations research (OR) methods have been applied to hospital operations to improve effectiveness and efficiency. In this three-part article, we review OR applications in hospital environments. In particular, we develop a timeline of events in US healthcare from the late 1940s to 2015 and separate the timeline into four eras: Expansion, Cost Control, Reform, and Accountability. Part I of the article describes the Eras of Expansion and Cost Control, Part II describes the Era of Reform, and Part III describes the Era of Accountability. Research performed during each era is contextualized and stratified by OR method and hospital operations application area. The series of three articles provides a comprehensive review of publications detailing operations research applications in hospital operations. The following is Part III, which details the OR applications in hospital operation area publications from 2010 to January 2015. During the Era of Accountability, the most commonly used OR methods were discrete event simulation and deterministic modeling (optimization), while the most common hospital operation areas where OR methods were applied were staff, room, and patient scheduling, as well as general patient flow assessment.

Operations research applications in hospital operations: Part II

ABSTRACTHospital managers are tasked with developing innovative strategies to provide patients with quality healthcare in an effective and efficient manner. Operations research (OR) methods have been applied to hospital operations to improve effectiveness and efficiency.In this three-part article, we review OR applications in hospital environments. In particular, we develop a timeline of events in US healthcare from the late 1940s to 2015 and separate the timeline into four eras: Expansion, Cost Control, Reform, and Accountability. Part I of the article describes the Eras of Expansion and Cost Control, Part II describes the Era of Reform, and Part III describes the Era of Accountability. Research performed during each era is contextualized and stratified by OR method and hospital operations application area.The following article is Part II of the three-part article, which details the OR applications in hospital operation area publications from 1990 to 2009. During the Era of Reform, the most commonly used OR methods were discrete event simulation and deterministic modeling (optimization), while the hospital operations areas where OR methods were applied were staff, room, and patient scheduling, as well as general patient flow assessment.

Parameter extraction of complex production systems via a kinetic approach

Continuum models of re-entrant production systems are developed that treat the flow of products in analogy to traffic flow. Specifically, the dynamics of material flow through a re-entrant factory via a parabolic conservation law is modeled describing the product density and flux in the factory. The basic idea underlying the approach is to obtain transport coefficients for fluid dynamic models in a multi-scale setting simultaneously from Monte Carlo simulations and actual observations of the physical system, i.e. the factory. Since partial differential equation (PDE) conservation laws are successfully used for modeling the dynamical behavior of product flow in manufacturing systems, a re-entrant manufacturing system is modeled using a diffusive PDE. The specifics of the production process enter into the velocity and diffusion coefficients of the conservation law. The resulting nonlinear parabolic conservation law model allows fast and accurate simulations. With the traffic flow-like PDE model, the transient behavior of the discrete event simulation (DES) model according to the averaged influx, which is obtained out of discrete event experiments, is predicted. The work brings out an almost universally applicable tool to provide rough estimates of the behavior of complex production systems in non-equilibrium regimes.

Effect of ignoring input correlation on truck–shovel simulation

This paper presents an approach for handling correlated input variables in discrete event simulation (DES) modelling of truck–shovel systems using commercial DES software and uses a case study to investigate the effect of ignoring correlation between input variables. Multivariate random vectors, instead of independent probability distributions, are used for variables found to be correlated. The authors prove that correlations do exist in truck–shovel haulage systems. The model with multivariate random vectors performs better than the original model. The significance of modelling correlation in input variables depends on the strength of the correlation and the output’s sensitivity to the input variables.

Guiding and modelling quality improvement in higher education institutions

The article considers the process of creating quality improvement in higher education institutions from the point of view of current organisational theory and social-science modelling techniques. The author considers the higher education institution as a functioning complex of rules, norms and other organisational features and reviews the social mechanisms and processes through which agencies can stimulate quality improvement. The article provides a few examples of how these social processes might be modelled using social simulation techniques, including agent-based models, discrete event simulation and other modelling techniques developed for representing complex social processes of coordination and cooperation. A better representation of universities as complexes of organisations will support more effective quality improvement by higher education leaders and external agencies.

Computer modeling of lung cancer diagnosis-to-treatment process.

We introduce an example of a rigorous, quantitative method for quality improvement in lung cancer care-delivery. Computer process modeling methods are introduced for lung cancer diagnosis, staging and treatment selection process. Two types of process modeling techniques, discrete event simulation (DES) and analytical models, are briefly reviewed. Recent developments in DES are outlined and the necessary data and procedures to develop a DES model for lung cancer diagnosis, leading up to surgical treatment process are summarized. The analytical models include both Markov chain model and closed formulas. The Markov chain models with its application in healthcare are introduced and the approach to derive a lung cancer diagnosis process model is presented. Similarly, the procedure to derive closed formulas evaluating the diagnosis process performance is outlined. Finally, the pros and cons of these methods are discussed.

Joint production, setup and preventive maintenance policies of unreliable two-product manufacturing systems

This article addresses the problem of joint optimisation of production, setup and maintenance activities of unreliable manufacturing system producing two products. Given the complexity of the problem in a dynamic and stochastic environment, the literature has treated the problem separately by considering each axis individually (setup, production and maintenance) or by combining two axes simultaneously (production-setup, production-maintenance). Following the trend of scientific research advances that supports the fact that an integrated control leads to best performances, the main objective of this paper is to provide a control policy that will simultaneously combine the production, the setup and the preventive maintenance activities. To tackle the problem, an experimental resolution approach using combined continuous/discrete event simulation models is considered. The aim is to accurately imitate the production system behaviour, and to optimise the control policy parameters which minimise the total cost incurred. An in-depth study of the effects of the system parameter variation on the performance of the studied policies is performed in order to draw meaningful conclusions and to illustrate the robustness of the proposed resolution approach.