Use Of Discrete Event Simulation Research Articles

A stochastic optimization algorithm for the supply vessel planning problem under uncertain demand and uncertain weather conditions

The Supply Vessel Planning Problem (SVPP) with stochastic demands and uncertain weather conditions is a transportation problem occurring in offshore oil and gas logistics. A fleet of supply vessels based at an onshore depot delivers supplies to a set of offshore oil platforms in a weekly sailing schedule. However, the schedules are frequently disrupted due to adverse weather conditions and uncertain demand for cargo from the oil platforms. The two sources of uncertainty are generally addressed in separate, most often through the use of two-phased methods, where simulation is combined with an optimization algorithm. The most common approach to incorporate robustness in the constructed schedules is to use a subjective penalized cost for non-robust voyages, with explicit modelling of recourse actions. In contrast, this paper proposes a two-stage stochastic programming algorithm accounting for both uncertain demand and uncertain weather conditions, allowing for the incorporation of the cost of recourse in the objective function. The cost of each solution is approximated through the use of discrete event simulation within a genetic algorithm. For the tested problem instances, the potential benefit from solving the stochastic program over solving the corresponding deterministic version leads to average relative annual cost savings of approximately 12%.

Choosing where to set the threshold between low- and high-risk patients: Evaluating a classification tool within a simulation

Health service providers must balance the needs of high-risk patients who require urgent medical attention against those of lower-risk patients who require reassurance or less urgent medical care. Based on their characteristics, we developed a tool to classify patients as low- or high-risk, with correspondingly different patient pathways through a service. Rather than choosing the threshold between low- and high-risk patients solely considering classification accuracy, we demonstrate the use of discrete-event simulation to find the best threshold from an operational perspective as well. Moreover, the predictors in classification tools are often categorical, and may be inter-dependent. Defining joint distributions of these variables from empirical data assumes that missing combinations are impossible. Our new approach involves using Poisson regression to estimate the joint distributions in the underlying population. We demonstrate our methods on a practical example: setting the threshold between low- and high-risk patients with proposed different pathways through a breast diagnostic clinic.

Use of DES to develop a decision support system for lot size decision-making in manufacturing companies

ABSTRACT There exists a wide range of optimization models in the Operations Management (OM) community to solve complex problems such as lot sizing. However, their practical performance is often criticized due to the complexity of implementation and insufficient applicability in real-world decision processes. These theory-driven approaches are either simple to compute, but only focus on single aspects of the decision without being able to capture the practical problem comprehensively, or are complex computational models with limited practicability. We apply a Design Science Research approach to resolve this issue and show how lot size decision-making models should be designed to thoroughly support managers. Our innovative model combines Discrete Event Simulation (DES) with OM methods and is developed and tested in a case study in the metal processing industry. Results reveal that the model is suitable to provide transparency about effects and a range of efficient solutions.

Use of discrete event simulation and genetic algorithms to estimate the necessary resources to respond in a timely manner in the Medical Emergency System in Bogotá.

Bogotá has a Medical Emergency System of public and private ambulances that respond to health incidents. However, its sufficiency in quantity, type and location of the resources demanded is not known. Based on the data from the Medical Emergency System of Bogotá, Colombia, we first sought to characterize the prehospital re- sponse in cardiac arrest and determine with the model which is the least number of resources necessary to respond within eight minutes, taking into account their location, number, and type. A database of incidents reported in administrative records of the district health authority of Bogotá (2014 to 2017) was obtained. Based on this information, a hybrid model based on discrete event simulation and genetic algorithms was designed to establish the amount, type and geographic location of resources according to the frequencies and typology of the events. From the database, Bogotá presented 938 671 ambulances dispatches in the period. 47.4% high priority, 18.9% medium and 33.74% low. 92% of these corresponded to 15 of 43 medical emergency codes. The response times recorded were longer than expected, especially in out-of-hospital cardiac arrest (median 19 minutes). In the proposed model, the best scenario required at least 281 ambulances, medicalized and basic in a 3:1 ratio, respectively, to respond in adequate time. Results suggest the need for an increase in the resources that respond to these incidents to bring these response times to the needs of our population.

Introducing uniform discrete event simulation (CSM2020) for modeling the TBM tunneling process

Several models have been developed to predict utilization of Tunnel Boring Machines (TBMs) based on statistical analysis of available project databases. Apart from the existing models, use of discrete event simulation (DES) for estimation has also been examined for utilization prediction. The results of these models demonstrate strong sensitivity to the influence of geological conditions and site logistics and offer the ability to take the site specific conditions into account for estimation of machine utilization. The present study proposes the stochastic simulation model CSM2020 using Arena© software that encompasses all major tunneling activities that impact machine utilization. CSM2020 model offers more accurate estimation of machine utilization and higher flexibility to customize the estimates based on equipment, logistics, and ground conditions. Given the observed limitations in various commercial software packages including Arena©, and lack of access/experience with such software in the typical tunneling projects, a DES model was also developed by using MATLAB. The accuracy of both the CSM2020 and MATLAB DES models depends on the availability of data to incorporate the complex interdependencies of various subsystems and different tunneling activities.The utilizations estimated for selected projects using the two simulation models were within 1% of each other and the results were verified by comparing the estimated values with those from field observation. The MATLAB code did not require a license, and it offers a better opportunity to keep track of the activities in a database that can be subsequently used for data visualization and analyzed to evaluate utilization and availability of individual subsystems and resources.

An Optimization Tool for Production Planning: A Case Study in a Textile Industry

The textile industry is an important sector of the Brazilian economy, being considered the fifth largest textile industry in the world. To support further growth and development in this sector, this document proposes a process for production analysis through the use of Discrete Event Simulation (DES) and optimization through genetic algorithms. The focus is on production planning for weaving processes and optimization to help make decisions about batch sizing and production scheduling activities. In addition, the correlations between some current technological trends and their implications for the textile industry are also highlighted. Another important contribution of this study is to detail the use of the commercial software Tecnomatix Plant Simulation 13®, to simulate and optimize a production problem by applying genetic algorithms with real production data.

Efficacy of antibiotic medication strategy following a bioterrorist attack involving Francisella tularensis

Francisella tularensis is a virulent bacterium which hijacks the host’s immune response, turning cells usually tasked with killing foreign organisms as nutrient-rich receptacles for rapid multiplication. It is designated a Tier 1 Select Agent by the US CDC due to its potential to cause widespread mortality upon deliberate release. In this study, the key biological mechanisms of host infection are modelled as a continuous-time Markov chain (CTMC) and solved via discrete event simulation (DES). The efficacy of various medication strategies at the behest of public health authorities are investigated. Without treatment, the median lethal dose – not previously considered in humans – is estimated at approximately 1,000 organisms. Results suggest that fatality can be averted if antibiotics are administered for at least 7 days, no later than 10 days post exposure. In a mass casualty setting, this can reduce symptomatic cases by 18% from the baseline involving no medication. Reductions of 59% are possible if medication can be disseminated no later than 5 days post exposure, mitigating otherwise severe pressure on healthcare services. Novel use of CTMC and DES highlight the potential for OR in the field of immunology, where further opportunities may present in a post COVID-19 world.

Proposal of Improvement for a Textile Finishing Company in the Medellin city Through of Discrete Simulation

The textile industry in Colombia is a source of employment for more than 200.000 people and more than 50% of this production is undertaken in Medellin. Modeling and improving textile processes allow this economic line to be competitive internationally. In this paper, we make a description about the use of discrete event simulation in a textile finishing company through the presentation of the results of four scenarios, which finally shows the potential of discrete simulations in productive environments and its high impact when modelling part of reality without the necessity of experimenting with the real system. The method used in this paper is summarized in three major stages: the first one is the simulation methodology, the second one is the data to support the simulation, and the final stage is an analysis of the results with the comparison of the four scenarios. The simulation was statistically validated and verified with the real behaviors of the company and it is executed by using software tools such as EasyFit®, Microsoft Excel® and Simul8®.

Comparison between discrete event simulation approach and various existing empirically-based models for estimation of TBM utilization

The fundamental components in predicting the TBM performance (advance rate) are the rate of penetration and utilization. Extensive studies have been conducted to accurately estimate these parameters. Inclusion of various tunneling activities and their interdependencies, the impact of the site set up and management, and the role of human factors on the operation make an estimation of utilization a complex task.A few models have been developed to predict machine utilization, and these models are based on a statistical analysis of available TBM project databases. Apart from the existing models, the use of discrete event simulation (DES) for estimation has also been examined for TBM performance prediction. The sensitivity and accuracy of all these prediction models demonstrate a strong influence of geological conditions and site settings. The present study compares outcome predictions from six empirical models and DES simulations for eight projects. The variability of the predictions is assessed, and the prediction outcomes are tested for “validation” by comparison with results from the recorded observation at the tunneling sites.The result showed that the difference between the predicted utilization using discrete-event simulations and the actual value available from site data was 7%. Comparing the utilization obtained from other empirical methods, the considerable deviation can be seen. Hence, it is concluded that the DES approach provides for a more accurate estimation of machine utilization while offering the flexibility to customize the model on the basis of both equipment selection and the availability of data to incorporate the complex interdependencies of different tunneling activities.

Maintenance optimization for a multi-unit system with digital twin simulation

Optimization of operations and maintenance (O&M) in the industry is a topic that has been largely studied in the literature. Many authors focused on reliability-based approaches to optimize O&M, but little attention has been given to study the influence of macroeconomic variables on the long-term maintenance policy. This work aims to optimize time-based maintenance (TBM) policy in the mining industry. The mine environment is reproduced employing a virtual model that resembles a digital twin (DT) of the system. The effect of maintenance decisions is replicated by a discrete event simulation (DES), whereas a model of the financial operability of the mine is realized through System Dynamics (SD). The simultaneous use of DES and the SD allows us to reproduce the environment with high-fidelity and to minimize the cost of O&M. The selected illustrative case example demonstrates that the proposed approach is feasible. The issues of using high dimensional simulation data from DT-models in managerial decision making is identified and discussed.

Improving materials logistics plan in road construction projects using discrete event simulation

PurposeMotivated by the high cost of material movements in road construction projects, past studies have used analytical methods to optimize materials logistics plans. A key shortcoming of these methods is their inability to capture the uncertain, dynamic and complex characteristics of the road construction material logistics. Failure to incorporate these characteristics can lead to sub-optimal results. The purpose of this study is to propose the use of discrete event simulation (DES) to address the existing shortfall.Design/methodology/approachDespite the powerful capabilities of DES models in capturing the operational complexities of construction projects, they have not been previously utilized to optimize the material logistics of road construction projects. The proposed DES-based method in this research captures the operational details of material logistics and uses a heuristic approach to overcome the combinatorial problem of numerous choices. The method was applied to a 63.5 km real-world road construction project case to demonstrate its capabilities.FindingsSix different material types from 28 material sources were used in the case. Approximately 1.5% of the material logistics costs were saved by following the proposed method and choosing appropriate material sources.Originality/valueThis research contributes to the body of knowledge by leveraging the capabilities of DES and presenting a novel method for improving the materials logistics plan of road construction projects. The proposed method provides practitioners with the basis for capturing the key operational details that were overlooked in the past. The proposed method can be adopted in road construction projects to reduce the overall material procurement cost.

Real-time discrete event simulation: a framework for an intelligent expert system approach utilising decision trees

This paper explores the use of discrete event simulation (DES) for decision making in real time based on the potential for data streamed from production line sensors. Technological innovations for data collection and an increasingly competitive global market have led to an increase in the application of discrete event simulation by manufacturing companies in recent years. Scenario analysis and optimisation methods are often applied to these simulation models to improve objectives such as cost, profit and throughput. The literature review has identified key research gaps as the lack of example cases where multi-objective optimisation methods have been applied to simulation models and the need for a framework to visualise the relationship between inputs and outputs of simulation models. A framework is presented to enable the optimisation DES simulation models and optimise multiple objectives simultaneously using design of experiments and meta-models to create a Pareto front of solutions. The results show that the resource allocation meta-model provides acceptable prediction accuracy whilst the lead time meta-model was not able to provide accurate prediction. Regression trees have been proposed to assist stakeholders with understanding the relationships between input and output variables. The framework uses regression and classification trees with overlaid values for multiple objectives and random forests to improve prediction accuracy for new points. A real-life test case involving a turbine assembly process is presented to illustrate the use and validity of the framework. The generated regression tree expressed a general trend by demonstrating relationships between input variables and two conflicting objectives. Random forests were implemented for creating higher accuracy predictions and they produced a mean square error of ~ 0.066 on the training data and ~ 0.081 on test data.

Investigating the impact of TBM downtimes on utilization factor based on sensitivity analysis

Tunnel boring machines (TBMs) are considered to be the most efficient tunneling method for long tunnels because of their higher productivity, adaptability to a variety of subsurface conditions, and providing a safer work environment. The number of TBMs utilized over the past decade has escalated, testimony to their continuing success and flexibility for application on projects of different diameter, length, and ground conditions. There are various guidelines available for TBM selection and for developing a suitable specification for a machine for a given project. Proper application of these guidelines is an essential part of machine selection to assure optimal performance of these machines in a given project. However, available guidelines are very general in nature and based on past experiences: they cannot take into account the many intricacies of a specific upcoming project including site set up and geology.This paper discusses use of Discrete Event Simulation (DES) to model tunneling activities, and demonstrates the ability of simulation to capture the interrelationships between the TBM, back-up system, and geologic interactions. The simulations provide insight into expectations for utilization of individual components as well as overall TBM system utilization. The simulation model can be constructed to enable estimation of the machine utilization under various site set up and back-up arrangements, and thus paves the way to quantitative assessment of the value of adding different capabilities on the machine and back-up system. Case studies are used to demonstrate the impact of some common tunneling activities in different ground conditions. In particular, DES was used to study the effect of eight different tunneling activities on the overall performance of the TBM. Significant differences in productivity can be seen by varying the specification of the TBM and system components. The results show the importance of selecting the right components for the back-up and their impact on overall machine utilization, leading to the possibility of allowing more methodic and fact-based selection of system components for optimal TBM performance.

Case study: evaluation of the automation of material handling with mobile robots

The automation of material handling is one of the solutions that many companies are relying on to reach their goals related to productivity increment, floor space optimization, higher standards for factory’s safety, and allocation of workers to value-added activities. Therefore, the objective of this study was to evaluate the current state of the material flow of finished goods for an automotive parts supplier plant and the technology available on the market to verify if it was worthwhile to invest in material handling automation. The analysis included the use of discrete event simulation to evaluate the different layout approaches combined with the mobile robots’ performance. It was proven that the tandem layout was the most beneficial approach to the analyzed plant’s reality with a minimal of three robots. Improvements to the material flow and automation of the labeling process were also proposed based on the study.

Masterplanning at the Port of Dover: The Use of Discrete-Event Simulation in Managing Road Traffic

The Port of Dover is Europe’s busiest ferry port, handling £119 billion or 17% of the UK’s annual trade in goods. The Port is constrained geographically to a small area and faces multiple challenges, both short- and long-term, with managing the flow of five million vehicles per year to/from mainland Europe. This article describes some of the work that the Port is doing to minimize the impact of port road traffic on the local community and environment using discrete-event simulation modeling. Modeling is particularly valuable in identifying where future bottlenecks are likely to form within the Port due to projected growth in freight traffic and comparing the effectiveness of different interventions to cope with growth. One of our key findings is that space which can be used flexibly is far more valuable than dedicated space. This is supported by the much greater reduction in traffic congestion that is expected to be achieved given a 10% increase in freight traffic by reallocating space at the front of the system to temporarily hold vehicles waiting to pass through border control and check-in compared to extending the amount of space for ferry embarkation at the rear of the system. The importance of flexible space has implications for port design that can be applied more broadly. Modeling is also useful in identifying critical thresholds for vehicle processing times that would cause the system to become overwhelmed. Increasing the check-in time by just three to five minutes, for example, would completely exceed the Port’s capacity and produce indefinite queueing. This finding has important implications for Brexit planning. From a wider context, the research presented here nicely illustrates how simulation can be used to instill more evidence-based thinking into port masterplanning and support “green port” and other corporate sustainability initiatives.

Machine-Learning-Based Approach for Parameterizing Material Flow Simulation Models

Discrete Event Simulation (DES) provides a far-reaching set of methods for planning and improving structures and processes of real factories. However, the use of DES is hampered by the high effort required for creating and parameterizing the corresponding models. Regarding the material flow of production systems with different products and small lot sizes, the expenditure for identifying current and historical processes and their parameters in the available data is time-consuming. Therefore, this paper presents an approach where machine learning algorithms identify these process specific parameters. Following that, the algorithms are integrated into the material flow simulation to parameterize the elements in the model.

Enhancing discrete-event simulation with big data analytics: A review

This article presents a literature review of the use of the OR technique of discrete-event simulation (DES) in conjunction with the big data analytics (BDA) approaches of data mining, machine learning, data farming, visual analytics, and process mining. The two areas are quite distinct. DES represents a mature OR tool using a graphical interface to produce an industry strength process modelling capability. The review reflects this and covers commercial off-the-shelf DES software used in an organisational setting. On the contrary the analytics techniques considered are in the domain of the data scientist and usually involve coding of algorithms to provide outputs derived from big data. Despite this divergence the review identifies a small but emerging literature of use-cases and from this a framework is derived for a DES development methodology that incorporates the use of these analytics techniques. The review finds scope for two new categories of simulation and analytics use: an enhanced capability for DES from the use of BDA at the main stages of the DES methodology as well as the use of DES in a data farming role to drive BDA techniques.

Assessing the supply chain performance: a causal analysis

Measuring the performance-related factors of a unit within a supply-chain is a challenging problem, mainly because of the complex interactions among the members governed by the supply chain strategy employed. Synergistic use of discrete-event simulation and structural equation modeling allows researchers and practitioners to analyze causal relationships between order-fulfillment characteristics of a supply-chain and retailers’ performance metrics. In this study, we model, simulate, and analyze a two-level supply-chain with seasonal linear demand, and using the information therein, develop a causal model to measure the links/relationships among the order-fulfillment factors and the retailer’s performance. According to the findings, of all the order-fulfillment characteristics of a supply-chain, the forecast inaccuracy was found to be the most important in mitigating the bullwhip effect. Concerning the total inventory cost and fill-rate as performance indicators of retailers, the desired service level had the highest priority, followed by the lead-time and forecast inaccuracy, respectively. To reduce the total inventory cost, the bullwhip effect seems to have the lowest priority for the retailers, as it does not appear to have a significant impact on the fill rate. Although seasonality (to some extent) influences the retailer’s performance, it does not seem to have a significant impact on the ranking of the factors affecting retailers’ supply-chain performance; except for the case where the backorder cost is overestimated.

27 Expanding Your Operational Toolkit: Using Computer Simulation to Improve Flow

Emergency department (ED) crowding has become a crisis for the U.S. health care system. Crowding often manifests as long waiting times for individual patients, extended overall length of stay (LOS), and high left-without-being seen (LWBS) rates. Hospitals and health care systems must invest in management strategies to address ED overcrowding and inpatient boarding. Computer simulation may be a cost-effective, efficient means to proactively identify and manage bottlenecks in patient care and excessive wait times or prolonged LOS. Simulation models are a novel tool to enhance prospective operational planning. We aimed to develop and validate a simulation model to estimate LOS in our ED. We then aimed to evaluate whether the simulation model could be used for operational planning by examining whether it could accurately predict the impact of planned operational changes. A high-fidelity, highly granular discrete event simulation (DES) model of patient flow through our ED was created in MedModel, a health care modeling operations software from the ProModel Corporation (Allentown, PA) that relies on queuing theory. This in silico model includes the entire ED, allowing visualization and analysis of multiple stages/queues during the patient flow through the ED. The model was developed using time stamps abstracted from the electronic health record (EHR): bed placement, RN assessment, MD assessment, and turnaround times (TOTs) for laboratory or imaging studies. Initial validation was performed by comparing system output LOS times to actual LOS values. The model was considered to be a valid predictor of LOS if simulated estimates were within a 5% margin of the actual LOS. After initial validation, the simulation was asked to model the impact of a dedicated inpatient holding unit. In the real world, a 15-bed ED pod was converted into a holding unit for admitted patients boarding in the ED awaiting inpatient bed assignment. LOS metrics were compared between simulation projections and actual data after implementation of this change. In this case, the model was considered valid if simulated estimates were within a 5% margin of the actual LOS and in the same direction of action. Initial validation of our simulation model confirmed that the model would predict LOS within 5% of real-world metrics. Simulated LOS was accurate, predicting LOS that was within 4.1% of actual LOS for all patients, and 0.8% of LOS for admitted patients. The simulation model also accurately predicted direction of impact on LOS for admitted patients following implementation of a 15-bed inpatient holding unit in the ED (Table 1). A DES simulation model is a valid tool to estimate LOS in the ED and can predict LOS following operational interventions (eg, implementation of an inpatient holding unit). Future work should test whether or not use of DES as an adjunct to standard decisionmaking improves operational metrics.Table 1Simulation model performance evaluating creation of inpatient holding unit.SimulationActual% DifferenceAdmit LOS, pre-implementation721 min726.4 min0.7%Admit LOS, post-implementation704 min715.8 min1.6% Open table in a new tab

Decision Support for Resource Optimization Using Discrete Event Simulation in Rehabilitation Hospitals

In order to make effective decisions regarding resource allocations in hospitals, managers need to have the ability to evaluate the volume of patients served by the hospitals as well as the based on available resources and examine the efficiency of various processes and procedures in various departments within the hospital. This paper proposes the use of discrete event simulation to provide managers with an evidence-based tool for examining patient flow in the hospital and to help support decisions for optimizing resource allocation in order to improve quality of care and resource utilization. Discrete event simulation can be used to evaluate various operational strategies and examine the execution of various tasks over time. Using simulation models provides a flexible and cost effective method for assessing operational changes prior to the actual implementation of these changes.